NIDDK Information Network dkNET Webinar

dkNET 3.0 Introductory Webinar

Tue, 12 Mar 2019 16:32:34 -0700

*Watch recorded webinar here: https://youtu.be/nr8fUz1OltA

*Webinar slides: https://www.slideshare.net/dkNET/dknet-introductory-webinar-03222019

The dkNET team is announcing exciting new changes to the NIDDK - dkNET Portal. The newly designed webportal now includes many new tools and reporting systems to enable researchers to easily navigate large amounts of data and information about research resources-reagents, tools, organisms, grants and other services. The new portal makes it easier to find and use information about the tools you use in your research. An exciting new feature is the Hypothesis Center, which analyzes large amounts of ‘omics data to provide new insights into the pathways involved in DK diseases.

Join us on Friday, March 22, 2019, 11am - 12pm (PDT) for a webinar where we will show you…

How to Create a Detailed Research Resource Report that includes….

A detailed overview of each resource.
Citation metrics from the biomedical literature.
Information about what resources have been used together.
Information on who else has used the resource
Information on documented problems with the resource
Community rating of the resource

How to Navigate NIH Mandates and Policies

How to Comply with FAIR Data Principles

Learn about the Hypothesis Center

Simplifies powerful data mining and hypothesis generation strategies for the bench researcher
Featuring a powerful new meta-data analysis platform
Survey across millions of DK mission-relevant biocurated ‘omics’ data points

Presenter: Jeffrey Grethe, PhD, dkNET Principal Investigator, University of California San Diego

Webinar link:

Date/Time: Friday, March 22, 2019, 11am - 12pm PDT

Naughty cell lines and RRIDs

Thu, 04 Apr 2019 17:10:20 -0700

*Watch recorded webinar here: https://youtu.be/NVI5xbkd8eI

*Webinar Slides: https://www.slideshare.net/dkNET/dknet-webinar-rrids-and-naughty-cell-lines-04122019

Join dkNET Webinar on Friday, April 12, 2019, 11am - 12pm (PDT)

Abstract

Dr. Anita Bandrowski will discuss newly published data suggesting that Research Resource IDentifiers, RRIDs improve the quality of cell lines being published.

Presenter: Anita Bandrowski, PhD, Research Resource Identification Initiate Project Lead, University of California San Diego

Link to publication: https://elifesciences.org/articles/41676

The Signaling Pathways Project: putting the R in FAIR data

Fri, 19 Apr 2019 11:14:44 -0700

*Watch recorded webinar here: https://youtu.be/gGBDMqBhfhQ

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-the-signaling-pathways-project-putting-the-r-in-fair-data-04262019

Join dkNET Webinar on Friday, April 26, 2019, 11am - 12pm (PDT)

Abstract

Public transcriptomic and ChIP-Seq datasets have the potential to illuminate facets of transcriptional regulation by mammalian cellular signaling pathways not yet explored in the research literature. Unfortunately, a variety of obstacles prevent routine re-use of these datasets by bench biologists for hypothesis generation and data validation. We have designed a web knowledgebase, the Signaling Pathways Project (SPP), which incorporates stable community classifications of three major categories of cellular signaling pathway node (receptors, enzymes and transcription factors) and the bioactive small molecules (BSMs) known to modulate their functions. We then subjected over 10,000 publically archived transcriptomic or ChIP-Seq experiments to a biocuration pipeline that mapped them to their relevant signaling pathway node, BSM or biosample (tissue or cell line of study). To provide for prediction of pathway node-target transcriptional regulatory relationships, we generated consensus ‘omics signatures, or consensomes, based on the significant differential expression or promoter occupancy of genomic targets across all underlying transcriptomic (expression array and RNA-Seq) or ChIP-Seq experiments. To expose the SPP knowledgebase to biology researchers, we designed a web browser interface that accommodates a variety of routine data mining strategies to identify node-gene target regulatory relationships previously uncharacterized in the research literature. SPP will power the Hypothesis Center of dkNET 3.0.

Presenter: Dr. Scott Ochsner, Biocuration Lead of the Signaling Pathways Project (SPP), Baylor College of Medicine

dkNET 3.0 Introductory Webinar

Fri, 03 May 2019 14:17:44 -0700

*Watch recorded webinar here: https://youtu.be/1MW9AmrVrfQ

Join us on Friday, May 10, 2019, 11am - 12pm (PDT) for a webinar where we will show you…

How to Create a Detailed Research Resource Report that includes….

A detailed overview of each resource.
Citation metrics from the biomedical literature.
Information about what resources have been used together.
Information on who else has used the resource
Information on documented problems with the resource
Community rating of the resource

How to Navigate NIH Mandates and Policies

How to Comply with FAIR Data Principles

Learn about the Hypothesis Center

Simplifies powerful data mining and hypothesis generation strategies for the bench researcher
Featuring a powerful new meta-data analysis platform
Survey across millions of DK mission-relevant biocurated ‘omics’ data points

Presenter: Jeffrey Grethe, PhD, dkNET Principal Investigator, University of California San Diego

Webinar link:

Date/Time: Friday, May 10, 2019, 11am - 12pm PDT

Rigor and Reproducibility Support at dkNET 3.0

Tue, 14 May 2019 16:35:52 -0700

*Watch recorded webinar here: https://youtu.be/aDA5ap_4Dag

Abstract

Problematic resources, such as contaminated or misidentified cell lines and antibodies with cross-reactivity, have been used in biomedical research studies and leads to reproducibility problems. Many factors, including the ability to easily retrieve alert information, results in the continued use of these resources wasting both time and money. Research Resource Identifiers (RRIDs) are unique identifiers for resources that assist finding, identifying, and tracking research resources in the published literature. Using RRIDs to aggregate information, dkNET has developed Authentication Reports to help scientists enhance the rigor and reproducibility of their research. In this webinar, we will show you how to comply with NIH's policies on authentication of key biological resources using dkNET's custom authentication reports.

Presenter: Jeffrey Grethe, PhD, dkNET Principal Investigator, University of California San Diego

Webinar link:

Date/Time: Friday, May 24, 2019, 11am - 12pm PDT

dkNET Webinar: Reproducibility - The Methods Behind the Data

Fri, 07 Jun 2019 18:10:39 -0700

*Watch recorded webinar here: https://youtu.be/26deD8tAFYA

Join dkNET Webinar on Friday, June 21, 2019 11am-12pm (PDT)

Abstract

Research papers and protocol organization in private labs and companies often lack detailed instructions for repeating experiments. protocols.io is an open access platform for scientists to create step-by-step, interactive and dynamic protocols that can be run on mobile or web. Researchers can share protocols with lab mates, collaborators, the scientific community or make them public, with ease and efficiency. Real time communication and interaction keep protocols up to date with versioning, forking, Q&A, and troubleshooting. Public protocols receive a DOI and allow open communication with authors and researchers to encourage efficient experimentation and reproducibility.

Presenter: Anita Brollochs, PhD, Head of Outreach, Protocols.io

Webinar link:

Date/Time: Friday, June 21, 2019 11am - 12pm PDT

dkNET Webinar: The Signaling Pathways Project, an integrated ‘omics knowledgebase for mammalian cellular signaling pathways

Wed, 06 Nov 2019 15:04:54 -0800

*Watch recorded webinar here: https://youtu.be/hC_aZ3opYcA

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-the-signaling-pathways-project-an-integrated-omics-knowledgebase-for-mammalian-cellular-signaling-pathways-11222019

Join dkNET Webinar on Friday, November 22, 2019, 11am - 12pm (PST)

Abstract

Mining of integrated public transcriptomic and ChIP-Seq (cistromic) datasets can illuminate functions of mammalian cellular signaling pathways not yet explored in the research literature. Here, we designed a web knowledgebase, the Signaling Pathways Project (SPP), which incorporates community classifications of signaling pathway nodes (receptors, enzymes, transcription factors and co-nodes) and their cognate bioactive small molecules. We then mapped over 10,000 public transcriptomic or cistromic experiments to their pathway node or biosample of study. To enable prediction of pathway node-gene target transcriptional regulatory relationships through SPP, we generated consensus ‘omics signatures, or consensomes, which ranked genes based on measures of their significant differential expression or promoter occupancy across transcriptomic or cistromic experiments mapped to a specific node family. Consensomes were validated using alignment with canonical literature knowledge, gene target-level integration of transcriptomic and cistromic data points, and in bench experiments confirming previously uncharacterized node-gene target regulatory relationships. To expose the SPP knowledgebase to researchers, a web browser interface was designed that accommodates numerous routine data mining strategies. SPP is freely accessible at https://www.signalingpathways.org. In this webinar, the presenters will demonstrate several SPP use cases, as well as take questions from the audience about specific aspects of SPP. SPP will power the Hypothesis Center of dkNET 3.0.

Presenter: Dr. Scott Ochsner, Biocuration Lead of the Signaling Pathways Project (SPP), Baylor College of Medicine; Dr. Neil McKenna, Project Leader of the Signaling Pathways Project, Baylor College of Medicine

The Mouse Metabolic Phenotyping Centers: Services and Data

Mon, 16 Dec 2019 15:28:36 -0800

*Watch recorded webinar here: https://youtu.be/UrJeuWwzHMY
*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-the-mouse-metabolic-phenotyping-centers-services-and-data-01242020

Join dkNET Webinar on Friday, January 24, 2020, 11am - 12pm PT

The Mouse Metabolic Phenotyping Centers (MMPC) is a National Institutes of Health-Sponsored resource that provides experimental testing services to scientists studying diabetes, obesity, diabetic complications, and other metabolic diseases in mice. Dr. Richard McIndoe will introduce resources and tools that are available at the MMPC.

The top 3 key questions that MMPC portal can answer:

1. What tests are available for metabolic phenotyping live mice?

2. What experimental datasets are relevant to a specific mammalian phenotype(s)?

3. What mammalian phenotypes are associated with specific gene manipulations in mouse models?

Abstract

A common strategy to dissect the etiology, genetics and underlying physiology of a disease is to create mouse models using gene targeting and manipulation techniques. These mouse models were developed by targeting one or more candidate genes or by using a whole genome mutagenesis strategy. The careful and reproducible characterization of these animal models is important for the advancement of biomedical research. The expense, expertise and time required to develop state-of-the-art phenotyping technologies is beyond the reach of many investigators. The Mouse Metabolic Phenotyping Centers (MMPC) were created to provide the scientific community with cost effective, high quality, standardized metabolic and phenotyping services. The focus of the MMPC is on experiments that characterize living animals as well as providing technologies that are important for understanding metabolism and physiology. The MMPC provides state-of-the-art technologies to investigators for a fee, with their services including characterization of mouse metabolism, blood composition (including hormones), energy balance, eating and exercise, organ function and morphology, physiology and histology. There are currently five MMPC Centers located at Vanderbilt University, University of California Davis, University of Cincinnati, University of Massachusetts and the University of Michigan. Investigators using the MMPC services agree to release the data generated by the MMPC to the general public via the national website database. This talk will review the structure of the MMPC, the services it provides and the data generated by the consortium for public use.

Presenter: Dr. Richard McIndoe, Professor, College of Graduate Studies and the College of Allied Health Sciences, Medical College of Georgia.

The NIH Mutant Mouse Resource and Research Centers (MMRRC) Consortium

Mon, 16 Dec 2019 15:33:39 -0800

*Watch recorded webinar here: https://youtu.be/kvNZTizhrWA

*Webinar Slides: https://www.slideshare.net/dkNET/dknet-webinar-the-nih-mutant-mouse-resource-and-research-centers-mmrrc-consortium

Join dkNET Webinar on Friday, February 14, 2020, 11am - 12pm PT

The Mutant Mouse Resource and Research Center (MMRRC) Program is the nation’s primary mutant mouse archive and distribution repository system. The MMRRC Program was established by the NIH 2 decades ago to ensure the preservation, dissemination, and development of valuable mutant mouse strains and data generated by research scientists. It also plays a key role in supporting rigor and reproducibility of experimental studies using mouse models. The MMRRC Program was constituted as a trans-national regionally-distributed network of four Centers each hosting an archive and distribution repository, located at the University of California Davis, University of Missouri-Columbia, The Jackson Laboratory, and the University of North Carolina-Chapel Hill, and an Informatics Coordination and Service Center (ICSC) located at UC Davis. Center members of the MMRRC Consortium serve the needs of the nation’s biomedical research community by ensuring access to and optimizing utilization of transgenic, knockout and other genetically engineered mutant mice and related biomaterials, services, and new technologies. To do so, the Centers import, verify, maintain, and distribute mice, gene-targeted embryonic stem (ES) cells, and germplasm of genetically unique, scientifically valuable mice that are essential for contemporary translational biomedical research. MMRRC Centers also provide services and procedures to assist investigators using genetically-altered mice for research in numerous areas including cancer, neurodegenerative, metabolic, developmental, genetic, and other diseases. Finally, Consortium members conduct resource-related research and develop and refine technologies that add scientific value to submitted mutant mouse strains and capitalize on the power of mouse genetics for biomedical research. By submitting their mice to the MMRRC Consortium, and upon acceptance, assignment, and deposition into an MMRRC Center, investigators fulfill their obligation under the NIH Data and Resource Sharing Policies. In return, the MMRRC Program strives to preserve, protect, quality control, and provide mouse models for study by research scientists and investigators across the nation and the globe. User surveys and feedback, discussions between MMRRC Consortium members and NIH Program representatives, input from Internal Advisors, and engagement with the MMRRC External Advisory Committee (EAC) of experts ensure that the MMRRC Program continues to serve the experimental mouse needs of the biomedical research community.

The top 3 key questions that MMRRC portal can answer:

How can I find mouse models of interest to my area of research?
What are the top 10 mouse models available from the MMRRC relevant to diabetes?
How can I deposit my mouse model into the MMRRC?

Presenter: Dr. Kent Lloyd, Professor, Department of Surgery, School of Medicine; Director, Mouse Biology Program; PI/PD, Mutant Mouse Resource and Research Center at University of California, Davis

The Type 2 Diabetes Knowledge Portal

Mon, 16 Dec 2019 15:35:42 -0800

*Watch recorded webinar here: https://youtu.be/Ka-ind9qHvg

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-the-type-2-diabetes-knowledge-portal-02282020

Join dkNET Webinar on Friday, February 28, 2020, 11am - 12pm PT

Abstract

The Type 2 Diabetes Knowledge Portal (T2DKP); type2diabetesgenetics.org), produced by the Accelerating Medicines Partnership in Type 2 Diabetes (AMP T2D), is an open-access resource that aims to facilitate the translation of genomic data into actionable knowledge for understanding and treatment of T2D and its complications. The supporting data and software platform is a modular system for data aggregation, analysis, and display, including: software for managing and tracking the transfer of data from contributors; automated analysis of Individual-level data (i.e. genotypes and phenotypes) or association summary statistics via statistical genetic or bioinformatic methods; storage of this information within a database accessible by a collection of Representation State Transfer (REST) APIs; and a web interface for visualizing these data. The T2DKP, which currently contains 84 datasets with genetic associations for 191 traits, makes genetic associations available for browsing by gene, variant, or genomic region, or browsing by phenotype in Manhattan plots. It presents distilled at-a-glance summaries for genes and regions while also offering the ability to drill down to the details of individual variant associations. The T2DKP also integrates epigenomic annotations and results of computational methods with GWAS results, to help researchers prioritize variants, genes, and tissues for further research. Interactive tools allow users to perform custom association analyses that securely access and compute on individual-level data without ever exposing the raw data. All datasets are fully documented, and summary statistic files may be made available for download from the T2DKP upon request of the study authors. The data and software platform have been applied to 4 additional open access resources for cardio-metabolic diseases; cardiovascular disease, cerebrovascular disease, and sleep disorders. We aim to release a companion resource for Type 1 Diabetes in 2020. All these resources provide 2 definitive features: access to authoritative results supplied by the generating research community; powered by a single underlying software system, thus allowing future integration into a common resource for common cardio-metabolic disease.

Questions you can address with the T2DKP

What are the genomic-wide associations for T2D and related traits from the definitive T2D genetic community datasets?
What are the most up to date and curated list of predicted T2D effector genes, along with the supporting lines for evidence?
What is the credible set of variants to study for functional follow up from any GWAS locus for T2D?
What are the results of computational approaches and relevant genomic annotations to assist in prioritization of a variant or gene from aGWAS loci?

Presenter(s):

Noël Burtt, Director, Operations and Development, Diabetes Research and Knowledge Portals, Program in Medical and Population Genetics, Broad Institute of Harvard and MIT

Jason Flannick, Assistant Professor of Pediatrics, Division of Genetics and Genomics, Boston Children’s Hospital and Harvard Medical School and Associate Member, Broad Institute of Harvard and MIT

Date/Time: Friday, February 28, 2020, 11am - 12pm PT

Sharing Data and Other Resources from the Human Islet Research Network

Mon, 16 Dec 2019 15:37:24 -0800

*Watch recorded webinar here: https://youtu.be/s2lvmTDEh5Q

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-sharing-data-and-other-resources-from-the-human-islet-research-network-03132020

Join dkNET Webinar on Friday, Mar. 13, 2020, 11am - 12pm PDT

The Human Islet Research Network (HIRN) connects many of the world’s leading scientists and laboratories together to address essential questions related to the loss of functional human beta cell mass in Type 1 Diabetes. Since its inception in 2014, hundreds of studies have been performed and published. Valuable resources have been generated for dissemination to communities and investigators of interest. In this webinar, you will be introduced to the services and resources available through HIRN, including bioreagents, datasets, documents, and technologies.

Presenter: John S. Kaddis, Ph.D.; MPI, Human Islet Research Enhancement Center (HIREC) for HIRN; Assistant Professor, Departments of Diabetes Immunology, and Diabetes and Cancer Discovery Science, City of Hope/Beckman Research Institute, Diabetes and Metabolism Research Institute, Duarte CA USA

Addgene, the Nonprofit Plasmid Repository

Mon, 16 Dec 2019 15:39:56 -0800

*Watch recorded webinar here: https://youtu.be/q_NLDhYe0HE

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-addgene-the-nonprofit-plasmid-repository-04242020

Date/Time: Friday, Apr. 24, 2020, 11am - 12pm PDT

Abstract

Addgene’s mission is to accelerate research and discovery by improving access to useful research materials and information. We facilitate the sharing of high-quality scientific materials, research reproducibility, and open science by archiving and distributing DNA-based research reagents and associated data to scientists worldwide. Our repository contains over 84,000 plasmids, including special collections on CRISPR and fluorescent proteins, and more than 450 ready-to-use AAV and lentiviral preparations. There is no cost for scientists to deposit plasmids, which saves time and money associated with shipping plasmids themselves. All plasmids in Addgene’s repository were deposited by your scientific colleagues from around the world. All plasmids are fully sequenced for validation and sequencing data is openly available. Furthermore, we offer free educational resources about molecular biology topics including the AAV Data Hub, our blog, eBooks, and written and video protocols.

The top 3 key questions that Addgene repository can answer:

1. How can I find relevant DNA-based reagents for studying my disease model of interest?

2. Which molecular biology tools and techniques are appropriate for my experiments?

3. What are the benefits of sharing my plasmids and how do I deposit plasmids into the repository?

Presenter: Dr. Angela Abitua, Outreach Scientist at Addgene, the nonprofit plasmid repository

Dial-in information:

Date/Time: Friday, April 24, 2020, 11am - 12pm PDT

A New Approach to the Study of Energy Balance and Obesity using CalR (CalRapp.org)

Fri, 24 Jan 2020 14:22:29 -0800

*Watch recorded webinar here: https://youtu.be/oEwlC3uAimQ

*Webinar Slides: https://www.slideshare.net/dkNET/dknet-webinar-a-new-approach-to-the-study-of-energy-balance-and-obesity-using-calr-05082020

Join dkNET Webinar on Friday, May. 8, 2020, 11 am - 12 pm PDT

Abstract

We report a web-based tool for analysis of experiments using indirect calorimetry to measure physiological energy balance. CalR simplifies the process to import raw data files, generate plots, and determine the most appropriate statistical tests for interpretation. Analysis using the generalized linear model (which includes ANOVA and ANCOVA) allows for flexibility in interpreting diverse experimental designs, including those of obesity and thermogenesis. Users also may produce standardized output files for an experiment that can be shared and subsequently re-evaluated using CalR. This framework will provide the transparency necessary to enhance consistency, rigor, and reproducibility. The CalR analysis software will greatly increase the speed and efficiency with which metabolic experiments can be organized, analyzed per accepted norms, and reproduced and has become a standard tool for the field. CalR is accessible at https://CalRapp.org/

The top 4 key questions that our tool can answer:

Can I reproducibly and transparently analyze indirect calorimetry experiments in under 10 minutes?
How hard is it to use Analysis of Covariance (ANCOVA) to determine whether my groups of animals are significantly different?
Is there an automated, reproducible way to exclude “noisy” outlier data from our indirect calorimetry experiments?
What are the key factors in determining metabolic rate of mice?

Presenter: Alexander Banks, PhD, principal investigator and assistant professor at Harvard Medical School and the Beth Israel Deaconess Medical Center.

dkNET Hypothesis Center - Signaling Pathways Project Live Demo

Wed, 08 Apr 2020 09:58:02 -0700

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-dknet-hypothesis-center-signaling-pathways-project-live-demo

*Hypothesis generation using SPP coronavirus infection consensomes tutorial: https://youtu.be/jqyvHsORCZ0

Join the live demo of the dkNET Hypothesis Center Signaling Pathways Project (SPP)

May 15, 2020 11am - 12pm (PDT)

The dkNET Hypothesis Center Signaling Pathways Project (SPP) is a free, open source tool for bench scientists to generate research hypotheses using SPP consensomes and was used for the recent bioRxiv publication, A transcriptional regulatory atlas of coronavirus infection of human cells. The SPP project lead, Dr Neil McKenna, will describe a number of important facets of this study, including:

Identifying human genes most consistently transcriptionally responsive to coronavirus infection
Inferring human signaling pathway nodes implicated in the cellular response to coronavirus infection
Strategies for generating hypotheses for connections between your research area of interest and coronavirus infection

We hope this short webinar will provide an opportunity to use this tool to shape your research activities. No informatics experience required.

Date: Friday, May 15, 2020

Time: 11AM-12PM (PDT)

Presenter: Neil McKenna, PhD, Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX

Creating and Sustaining a FAIR Biomedical Data Ecosystem

Tue, 22 Sep 2020 23:58:23 -0700

*Watch recorded webinar here: https://youtu.be/cFCLVcT6naA

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-creating-and-sustaining-a-fair-biomedical-data-ecosystem-10092020

Join dkNET Webinar on Friday, Oct. 9, 2020, 11 am - 12 pm PDT

Abstract

In this presentation, Susan Gregurick, Ph.D., Associate Director of Data Science and Director, Office of Data Science Strategy at the National Institutes of Health, will share the NIH’s vision for a modernized, integrated FAIR biomedical data ecosystem and the strategic roadmap that NIH is following to achieve this vision. Dr. Gregurick will highlight projects being implemented by team members across the NIH’s 27 institutes and centers and will ways that industry, academia, and other communities can help NIH enable a FAIR data ecosystem. Finally, she will weave in how this strategy is being leveraged to address the COVID-19 pandemic.

Presenter: Susan Gregurick, Ph.D., Associate Director of Data Science and Director, Office of Data Science Strategy at the National Institutes of Health

Illuminating the Druggable Genome with Pharos

Tue, 29 Sep 2020 22:40:37 -0700

*Watch recorded webinar here: https://youtu.be/GeZG7iN78rM

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-illuminating-the-druggable-genome-with-pharos-10232020

Join dkNET Webinar on Friday, Oct. 23, 2020, 11 am - 12 pm PDT

Abstract

Pharos is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied “dark” targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. In this webinar, Dr. Tudor Oprea will introduce how to use Pharos to find targets of interest for drug discovery.

The top 3 key questions that Pharos can answer:

1. What are the novel drug targets that may play a role in a specific disease?

2. What are the diseases that are related directly or indirectly to a drug target?

3. Find researchers that are related directly or indirectly to a drug target.

Presenter: Tudor Oprea, MD, PhD, Professor of Medicine, Chief of Translational Informatics Division & Internal Medicine, University of New Mexico

Date/Time: Friday, Oct. 23, 2020, 11 am - 12 pm PDT

FAIR Data Require Better Metadata: The Case for CEDAR

Wed, 23 Sep 2020 00:11:49 -0700

*Webinar recording: https://youtu.be/JEAbAgRKomM

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-fair-data-require-better-metadata-the-case-for-cedar-11132020

Join dkNET Webinar on Friday, Nov. 13, 2020, 11 am - 12 pm PST

Abstract

With the explosion of interest in open science, the past few years have overflowed with discussions of making scientific data “FAIR”—findable, accessible, interoperable, and reusable. The problem is that most scientific datasets are by no means FAIR. When left to their own devices, scientists do a terrible job creating the metadata that describe the experimental datasets that make their way to online repositories. The lack of standardization makes it extremely difficult for other investigators to locate relevant datasets, to reanalyze them, and to integrate those datasets with other data. There is an urgent need to make it easy for investigators to author metadata that adhere to community standards and that describe datasets in reproducible terms. The Center for Expanded Data Annotation and Retrieval (CEDAR) is developing technology with the goal of doing just that. Although it will take more than technology to make data FAIR, solid infrastructure is an essential prerequisite. CEDAR demonstrates the value of making it easy for scientists to author metadata that are complete, comprehensive, and standardized.

The top 3 key questions that CEDAR can answer:

1. How can I describe my experiment in a way that will allow other investigators to find my data?

2. What are the essential metadata fields needed to describe an experiment that uses a method such as RNA-Seq?

3. How can I easily enter metadata that are acceptable to a repository such as NCBI’s BioSample database?

Presenter: Mark Musen, PhD, Professor of Biomedical Informatics and of Biomedical Data Science at Stanford University, and Director of the Stanford Center for Biomedical Informatics Research

Vivli: A Global Clinical Trials Data Sharing Platform

Wed, 23 Sep 2020 00:22:15 -0700

*Webinar recording: https://youtu.be/aYntzzu-y-g

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-vivli-a-global-clinical-trials-data-sharing-platform-12112020

Join dkNET Webinar on Friday, Dec. 11, 2020, 11 am - 12 pm PST

Abstract

Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform. Our focus is on sharing individual participant-level data from completed clinical trials to serve the entire the scientific community and a diverse group of stakeholders including industry, academic institutions, government and non-profits. The Vivli platform includes an independent data repository, in-depth search engine and a secure research environment. This session will explore when it is appropriate to share your data using a managed access platform such as Vivli and will show how the Vivli team can support you in this process. We will also explore what studies are available that may be of interest to the dkNET community on the platform.

The top 3 key questions that Vivli can answer:

Why should I share data from my completed clinical studies?
How can Vivli help me share my clinical study data?
How can I request data from other completed studies?

Presenter: Ida Sim, MD, PhD, Professor of Medicine, University of California San Francisco and Co-Founder, Vivli

FAIR Data & Software in the Research Life Cycle

Wed, 23 Sep 2020 00:39:41 -0700

*Webinar recording: https://youtu.be/SwMUc2H67X8

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-fair-data-software-in-the-research-life-cycle-01222021

Join dkNET Webinar on Friday, Jan. 22, 2021, 11 am - 12 pm PST

Abstract

Good data stewardship is the cornerstone of knowledge, discovery, and innovation in research. The FAIR Data Principles address data creators, stewards, software engineers, publishers, and others to promote maximum use of research data. The principles can be used as a framework for fostering and extending research data services.

This talk will provide an overview of the FAIR principles and the drivers behind their development by a broad community of international stakeholders. We will explore a range of topics related to putting FAIR data into practice, including how and where data can be described, stored, and made discoverable (e.g., data repositories, metadata); methods for identifying and citing data; interoperability of (meta)data; best-practice examples; and tips for enabling data reuse (e.g., data licensing). Practical examples of how FAIR is applied will be provided along the way.

Presenter: Christopher Erdmann, Engagement, support, and training expert on the NHLBI BioData Catalyst project at University of North Carolina Renaissance Computing Institute

nPOD nanotomy: Large-scale electron microscopy database for human type 1 diabetes

Wed, 23 Sep 2020 00:52:52 -0700

*Watch recorded webinar here: https://youtu.be/DXLvEBP7uII

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-npod-nanotomy-largescale-electron-microscopy-database-for-human-type-1-diabetes

Join dkNET Webinar on Friday, Feb. 12, 2021, 11 am - 12 pm PST

Abstract

Imaging of macromolecules and organelles in the context of cells and tissues is challenging because of the different scales and big data sharing. High resolution imaging of ultrastructure using electron microscopy (EM) typically has a small field of view. Panorama EM views, which we name nanotomy (nano-anatomy), now cross orders of magnitude scales (http://www.nanotomy.org/). The open-source sharing allows reuse of data for further analysis, e.g. of structures that were not the focus of the primary study. Nanotomy will likely become the future standard routine EM technique for tissue and cell imaging. In this talk I will highlight the technique and the recent database of nanotomy of human pancreas tissue obtained from the Network for Pancreatic Organ donors with Diabetes.

Research: cellbiology.n

lEM-dbase: nanotomy.org

UMIC core: umic.info

Presenter: Ben N. G. Giepmans, PhD, Associate Professor, Biomedical Sciences of Cells & Systems, UMC Groningen, The Netherlands

Population-based Approaches to Investigate Endocrine Communication

Tue, 12 Jan 2021 16:17:49 -0800

*Watch recorded webinar here: https://youtu.be/2w98-s7BIp8

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-populationbased-approaches-to-investigate-endocrine-communication-02262021

dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series

Join dkNET Webinar on Friday, Feb. 26, 2021, 11 am - 12 pm PST

Abstract

Mechanisms of inter-organ signaling have been established as hallmarks of nearly every pathophysiologic condition, where many exist as related and complex diseases. While significant work has been focused on understanding how individual cell types contribute and respond to specific perturbations related to common, complex disease, an equally-important but relatively less-explored question involves how relationships between organs are altered in the context of an integrated living organism. Current technical advances, such as proteomic analysis of plasma or conditioned media, have allowed for a more unbiased visualization and discovery of additional inter-tissue signaling molecules. However, one important feature which is lacking from these approaches is the ability to gain insight as to the function, mechanisms of action and target tissue(s) of relevant molecules. To begin to address these constraints, we initially developed a correlation-based bioinformatics framework which uses multi-tissue gene expression and/or proteomic data, as well as publicly available resources to statistically rank and functionally annotate endocrine proteins involved in tissue cross-talk. Using this approach, we identified many known and experimentally validated several novel inter-tissue circuits. This was this first study to directly link an endocrine-focused bioinformatics pipeline from population data directly to experimentally-validated mechanisms of inter-tissue communication. While these validations provide strong support for exploiting natural variation to discover new modes of communication, these serve as simple proof-of-principle studies and, thus, have promising potential for expansion. Some of these will be discussed during the presentation.

Presenter: Marcus Seldin, Ph.D. Assistant Professor, Biological Chemistry, University of California Irvine (2020 dkNET New Investigator Pilot Program in Bioinformatics Awardee)

Temporal plasma metabolome and gut microbiome in an early-childhood study of type 1 diabetes

Tue, 12 Jan 2021 16:18:53 -0800

*Watch recorded webinar here: https://youtu.be/86zKYb38Eb4

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-temporal-plasma-metabolome-and-gut-microbiome-in-sn-earlychildhood-study-of-type-1-diabetes-03122021

dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series

Join dkNET Webinar on Friday, March 12, 2021, 11 am - 12 pm PST

Abstract

The Environmental Determinants of Diabetes in the Young (TEDDY) study enrolled newborns by screening of HLA-DR-DQ haplogenotypes, and then collected plasma and stool samples for metabolomics and metagenomics analyses under a nested case-control design. Our recent research reported multiple metabolites at various time points heralding the onset of distinct initial islet autoantibodies. An unsupervised clustering analysis of temporal lipidome identified a subgroup of TEDDY children developing autoimmunity at an earlier age compared to the others, similar to the age of population-wide early incidence. In order to identify operational taxonomic units that signal the early-age seroconversion to autoimmunity adjusting for the known metabolic and genetic risk factors, we developed a three-part mixed effect model that integrates the temporal microbiota, risk of disease onset up to a fixed time point, metabolites and other time-invariant risk factors. Application to a subgroup of TEDDY children showed that at their 4-9 months of age, the temporal composition and presence of multiple genera and species were associated with the risk of seroconversion by 18 months of age, adjusting for the reported metabolic risk factors and HLA haplogenotype DR3&4. This method also confirmed that these metabolites and HLA DR3&4 were associated with the risk of early seroconversion to islet autoimmunity.

Presenter: Qian Li, Ph.D. Assistant Professor, Health Informatics Institute, University of South Florida (2020 dkNET New Investigator Pilot Program in Bioinformatics Awardee)

Dial-in In formation: https://uchealth.zoom.us/meeting/register/tZEkduutrTIuGddTDYKoOoj_1wEEm2A7RkD9

Multi-omics Data Integration for Phenotype Prediction of Type-1 Diabetes

Tue, 12 Jan 2021 16:19:56 -0800

*Watch recorded webinar here: https://youtu.be/qCP09Bf-xfk

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-multiomics-data-integration-for-phenotype-prediction-of-type1-diabetes-04092021

dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series

Join dkNET Webinar on Friday, Apr. 9, 2021, 11 am - 12 pm PST

Abstract

Omics techniques (e.g., i.e., transcriptomics, genomics, and epigenomics) report quantitative measures of more than tens of thousands of biological features and provide a more comprehensive molecular perspective of studied diabetes mechanisms compared to transitional approaches. Identifying representative molecular signatures from the tremendous number of biological features becomes a central problem in utilizing the data for clinical decision-making. Exploring the complex causal relations of the identified representative molecular signatures and diabetes phenotypes can be the most effective and efficient ways to improve the understanding of diabetes and assess the cause of diabetes for the new patients with already collected data influencing (e.g., TEDDY project). However, due to the unavoidable patient heterogeneity, statistical randomness, and experimental noise in the high-dimension, low-sample-size omics data of the diabetic patients, utilizing the available data for clinical decision-making remains an ongoing challenge for many researchers. To overcome the limitations, in this study we developed (1) a generative adversarial network (GAN)-based model to generate synthetic omics data for the samples with few omics profiles available; (2) a deep learning-based fusion network model for phenotype prediction of type-1 diabetes; (3) a long short-term memory (LSTM)-based model for predicting outcomes of islet autoantibody and persistent positivity. The models are tested on the multi-omics data in TEDDY project.

Presenter: Wei Zhang, Ph.D. Assistant Professor, Department of Computer Science & Genomics and Bioinformatics Cluster, University of Central Florida (2020 dkNET New Investigator Pilot Program in Bioinformatics Awardee)

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZwrcO6vrTksG9VzsopYGFEfcnsdFhOkHnWW

Solving the Undiagnosed Diseases through Machine Learning

Fri, 19 Feb 2021 18:30:04 -0800

*Watch recorded webinar here: https://youtu.be/ypN44EHRFRI

*Webinar slides:
https://www.slideshare.net/dkNET/dknet-webinar-solving-the-undiagnosed-diseases-through-machine-learning-05142021

Join dkNET Webinar on Friday, May. 14, 2021, 11 am - 12 pm PDT

Abstract

Every year hundreds of patients face uncertainty when healthcare providers are unable to discover the cause for their symptoms. The Undiagnosed Diseases Network (UDN) is a research study backed by the National Institutes of Health Common Fund that seeks to provide answers for patients and families affected by these mysterious conditions. For patients with potential rare genetic disorders, sequencing will be performed to identify the disease-causing variant. The process of defining pathogenicity currently requires labor-intensive manual searches of a variety of databases and web resources. This manual process is time-consuming, subject to inter-user variability and variations in the depth or quality of the databases. It also requires broad expertise across multiple biological and informatics domains. Here, we created a systematic, comprehensive search engine, MARRVEL (Model organism Aggregated Resources for Rare Variant ExpLoration, http://marrvel.org), that mines all the critical information for variant analysis and presents it in a succinct, user-friendly way. MARRVEL integrates human databases (OMIM, gnomAD, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER) and seven model organism databases from yeast to mammals. Furthermore, we are also developing a Knowledge-based and Explainable Artificial Intelligent system (MARRVEL-AI) to prioritize and identify novel disease-causing coding variants. The interpretability of a machine learning method inversely correlates with its accuracy for complex tasks. To circumvent this, we are combining different models of artificial intelligence with complementary strengths, such as expert system and random forest. With only a small training data set, our model achieved a high accuracy in identifying disease causing variants for UDN cases.

Top. 3 key questions that Undiagnosed Disease Network (UDN) can answer:

1. Which gene/its variants is likely to be the cause of a rare Mendelian Disorder?

2. What is the probable disease mechanism?

3. Do we have an animal model for the rare disease?

Presenter: Zhandong Liu, PhD, Associate Professor, Department of Pediatrics, Baylor College of Medicine

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZMrcOmhqDoiGNIkJ5VoIxvoUUmP10PULKst

Appyters: Turning Jupyter Notebooks into data-driven web apps

Thu, 25 Mar 2021 16:04:08 -0700

*Watch recorded webinar here: https://youtu.be/-Hi3y3EIpSs

*Webinar slides:
https://www.slideshare.net/dkNET/dknet-webinar-appyters-turning-jupyter-notebooks-into-datadriven-web-apps-05282021

Join dkNET Webinar on Friday, May 28, 2021, 11 am - 12 pm PDT

Abstract

Jupyter Notebooks have transformed the communication of data analysis pipelines by facilitating a modular structure that brings together code, markdown text, and interactive visualizations. Here, we extended Jupyter Notebooks to broaden their accessibility with Appyters. Appyters turn Jupyter Notebooks into fully functional standalone web-based bioinformatics applications. Appyters present to users an entry form enabling them to upload their data and set various parameters for a multitude of data analysis workflows. Once the form is filled, the Appyter executes the corresponding notebook in the cloud, producing the output without requiring the user to interact directly with the code. Appyters were used to create many bioinformatics web-based reusable workflows, including applications to build customized machine learning pipelines, analyze omics data, and produce publishable figures. These Appyters are served in the Appyters Catalog at https://appyters.maayanlab.cloud. In summary, Appyters enable the rapid development of interactive web-based bioinformatics applications.

The top 3 key questions that Appyters can answer:

1. I wrote my workflow as a Python Jupyter Notebook, is there an easy way that I can quickly convert this notebook into a web app so that others can use my workflow to process their data?

2. I have bulk RNA-seq data that I collected and would like to analyze. The genomics core provided me with the aligned reads file, but I am not sure about the next steps. Can I use an Appyter to analyze my data?

3. I am interested in doing some data analysis using the TCGA RNA-seq data, but I am having trouble accessing and formatting the data I need from the new GDC data portal. Is there an Appyter that I can use to access these RNA-seq data?

Presenter: Avi Ma'ayan, PhD, Mount Sinai Endowed Professor in Bioinformatics, Professor in Department of Pharmacological Sciences, and Director of Mount Sinai Center of Bioinformatics, Icahn School of Medicine at Mount Sinai

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZEvfuysrzwpG9RnaZmgADvOKsFe7vpB6S78

dkNET Hypothesis Center Live Demo

Thu, 26 Aug 2021 11:03:06 -0700

*Watch recorded webinar here: https://youtu.be/7jtMAiHrwSc

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-dknet-hypothesis-center-live-demo

Join dkNET Webinar on Friday, September 24, 2021, 11 am - 12 pm PDT

Presenter: Jeffrey Grethe, PhD, dkNET Principal Investigator, University of California San Diego

dkNET is creating a hub for big data and hypothesis generation, bringing together a collection of online tools that will allow researchers to explore different datasets and utilize analytics and visualization tools. The dkNET Hypothesis Center phenotype-genotype analytics module is currently performed utilizing data from the Signaling Pathways Project (SPP), and the Mouse Metabolic Phenotyping Centers (MMPC). Upcoming resources include the Human Islet Research Network Resource Browser, Appyters, Type 1 Diabetes Knowledge Portal,...and more. Through detailed tutorials and integrating different resources, the power of the dkNET Hypothesis Center can help answer the questions of immediate relevance to your research.

What you will learn:

Introduction of the dkNET Hypothesis Center
How to navigate and access tutorials that will teach you how to use FAIR data and bioinformatics tool(s)
How the dkNET Hypothesis Center can assist in answering your research questions and generating hypotheses

We hope this short webinar will provide an opportunity to use this tool to shape your research activities. No informatics experience required.

Presenter: Jeffrey Grethe, PhD, dkNET Principal Investigator, University of California San Diego

Join the call! https://uchealth.zoom.us/meeting/register/tZ0ofuypqTIqHtwJyt_UiwyvqOl1QSXXgqB6

GeneNetwork: Experimental Precision Medicine and Smart FAIR+ Data for Metabolomics and Diabetes Research

Thu, 26 Aug 2021 11:07:50 -0700

*Watch recorded webinar here: https://youtu.be/JSHo222RVoQ

*Webinar slides:
https://www.slideshare.net/dkNET/dknet-webinar-genenetwork-genenetwork-experimental-precision-medicine-and-smart-fair-data-for-metabolomics-and-diabetes-research-10082021

Join dkNET Webinar on Friday, Oct 8, 2021, 11 am - 12 pm PDT

Abstract

The challenge of precision medicine is to model complex interactions among DNA variants, phenotypes, development, environments, and treatments. The community of researchers using animal models must address the challenge of both genetic and environmental complexity typical of human populations. We have developed large families of mice and rats that can be used as uniquely powerful model for experimental versions of precision medicine. For example, the BXD family of mice segregates for 6 million common DNA variants—a level that exceeds many human populations. Because each member is an isogenic strain, the entire family can be replicated in many environments and offered many treatments. Heritable traits can be mapped with high power and precision. The current BXD phenome is unsurpassed in coverage and include deep omics data and thousands of quantitative traits—including a great deal of data relevant to metabolism, obesity, aging, kidney function, and insulin levels. These new Experimental Precision Medicine resources can be expanded to as many as 20,000 isogenic but non-inbred F1 progeny and be used as a far more effective platform for testing causal modeling and for predictive validation—unique core resources for the fields of prevention and therapeutics.

The top 3 key questions that GeneNetwork can answer:

1. What is the relation between insulin levels as a function of diet, age, body weight and lifespan in mice? (BXD family; see Nature Metabolism paper by Roy et al., Sept-Oct 2021)

2. How can researchers using single strains of mice broaden the relevance of their findings to improve the translational relevance to human health and to diabetes prevention and treatment?

3. How in the world can a molecular or cell biologist master complex statistical genetic methods to test causal (aka, mechanistic) linkages between DNA variants and disease risk?

Presenter: Robert W. Williams, Ph.D. Chair, Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, UT-ORNL Governor's Chair in Computational Genomics

Dial-in Information:

Date/Time: Friday, October 8, 2021, 11 am - 12 pm PDT

https://uchealth.zoom.us/meeting/register/tZ0scOChrz8qGdOX_p4fh3XsaFC3QSBb53zH

YCharOS: Antibody Characterization Through Open Science

Wed, 22 Sep 2021 14:54:54 -0700

*Watch recorded webinar here: https://youtu.be/6KYfChyl4Hs

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-ycharos-10222021

Join dkNET Webinar on Friday, Oct 22, 2021, 11 am - 12 pm PDT

Abstract

Many (most!) genes/proteins linked to disease phenotypes remain severely understudied [1]. Distinct open-science initiatives are needed to promote the exploration of currently understudied proteins, from the proper identification of research reagents [2], to the development of a chemical probes for every human protein [1]. We focus on the proper characterization of antibodies to guide researchers in selecting the most specific/selective antibodies for their needed application(s) [3].

Antibodies are among the most commonly used reagents in cell biology. Generally, scientists purchase antibodies from commercial suppliers, and rely on the vendor’s quality control data to make their purchasing decisions. While there are many outstanding commercially-available antibodies, many other antibodies do not perform as advertised - and in the absence of an objective means to compare performance, it is impossible to tell one from the other. This is a widely known problem that plagues tens of thousands of scientists annually [4-6].

There is a scientific solution, enabled by CRISPR/Cas9 technology. By comparing signals from wild-type and isogenic knockout cells, one can readily test the specificity of antibodies. We applied this approach in a pilot study demonstrating that only three of the 16 commercially-available antibodies for C9ORF72, the protein product of a major amyotrophic lateral sclerosis disease locus, specifically recognized the protein. Distressingly, neither antibody had been used in a publication, and the antibody used most frequently in publications, which have been cited thousands of times, did not recognize the protein in any application [7].

We are now applying our antibody characterization pipeline to generate head-to-head comparisons of commercial antibodies for all human proteins. This work is performed in partnership with high-quality manufacturers that provide in-kind reagents (i.e. antibodies and knock-out lines). Finalized antibody characterization reports are progressively uploaded on a free open-science repository (https://zenodo.org/communities/ycharos/). We believe our initiative, Antibody Characterization through Open Science (YCharOS), will contribute to make science more reproducible and help illuminate the dark genome.

The top 3 key questions that YCharOS can answer:

Do antibodies used in my field perform as advertised?
How do I identify the best performing antibody for my protein of interest?
Do I need to launch an expensive and time-consuming antibody generation study or do effective commercial antibodies already exist for my protein of interest?
When will you be studying my protein?

1. Carter, A.J., et al., Target 2035: probing the human proteome. Drug Discov Today, 2019. 24(11): p. 2111-2115 DOI: 10.1016/j.drudis.2019.06.020.

2. Bandrowski, A.E. and M.E. Martone, RRIDs: A Simple Step toward Improving Reproducibility through Rigor and Transparency of Experimental Methods. Neuron, 2016. 90(3): p. 434-6 DOI: 10.1016/j.neuron.2016.04.030.

3. Laflamme, C., et al., Opinion: Independent third-party entities as a model for validation of commercial antibodies. N Biotechnol, 2021. 65: p. 1-8 DOI: 10.1016/j.nbt.2021.07.001.

4. Goodman, S.L., The antibody horror show: an introductory guide for the perplexed. N Biotechnol, 2018. 45: p. 9-13 DOI: 10.1016/j.nbt.2018.01.006.

5. Goodman, S.L., The path to VICTORy - a beginner's guide to success using commercial research antibodies. J Cell Sci, 2018. 131(10) DOI: 10.1242/jcs.216416.

6. Voskuil, J.L.A., et al., The Antibody Society's antibody validation webinar series. MAbs, 2020. 12(1): p. 1794421 DOI: 10.1080/19420862.2020.1794421.

7. Laflamme, C., et al., Implementation of an antibody characterization procedure and application to the major ALS/FTD disease gene C9ORF72. Elife, 2019. 8 DOI: 10.7554/eLife.48363.

Presenter: Carl Laflamme, PhD, Senior Postdoctoral Fellow at the Montreal Neurological Institute (The Neuro, McGill University) in the laboratory of Peter McPherson, distinguished James McGill professor.

Dial-in Information:

Date/Time: Friday, October 22, 2021, 11 am - 12 pm PDT

https://uchealth.zoom.us/meeting/register/tZIudOugpjwjHdaABvV9mtxvzkJau9rHoOrE

The Microphysiology Systems Database (MPS-Db): A platform for aggregating, analyzing, sharing and computationally modeling in vitro data

Thu, 23 Sep 2021 11:20:00 -0700

*Watch recorded webinar here: https://youtu.be/fsWUmBqjB4I

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-the-microphysiology-systems-database-mpsdb-a-platform-for-aggregating-analyzing-sharing-and-computationally-modeling-in-vitro-data-11122021

Join dkNET Webinar on Friday, Nov. 12, 2021, 11 am - 12 pm PST

Abstract

The Microphysiology Systems Database Center (MPS-DbC) developed and implemented the Microphysiology Systems Database (MPS-Db, https://mps.csb.pitt.edu/) for the management, analysis, sharing, integration of preclinical and clinical information, and computational modeling of data in one platform, enhancing the in vitro model value and user workflow. The MPS-Db supports data from a wide range of in vitro models including static and microfluidic 2D and 3D microplates, and microfluidic MPS for single and multiple organ models. Aggregation of metadata, experimental data, and references provides for robust and relevant interpretation of the results, and having a central repository facilitates data sharing among user-specified collaborators and groups. Ready access to experimental data and metadata from any in vitro platform, along with reference data in a mineable format, provides a convenient platform for statistical analysis of performance, and building computational models to predict PK, identify compound mechanisms of actions, and infer pathways of disease progression. The MPS-DbC assists users in capturing and managing MPS data, and the MPS-Db is the central repository for the Tissue Chip Testing Centers, as well as the NCATS Tissue Chips programs. We continue to build the research and commercial value of the MPS-Db by: 1) supporting MPS users to build content; 2) implementing on-line preclinical/clinical concordance analysis capabilities; 3) enhancing the suite of data mining and computational modeling tools; and 4) augmenting methods for ensuring data quality and the secure, controlled release of data to user-specified groups.

The top 3 key questions that Microphysiology Systems Database (MPS-Db) can answer:

1. What models are available, what are their characteristics, how reproducible are they, and how can they be used?

2. How does an organ model A compare with organ model B? For example, where model A and model B are constructed in different laboratories, on different days, or with difference cells, such as iPSCs vs. primary cells.

3. Which readouts from an organ model are predictive of a specific clinical outcome and how reliable is the prediction?

Presenter: Bert Gough, PhD, Association Professor of Computational and Systems Biology, Group Leader Informatics, University of Pittsburgh Drug Discovery Institute

Dial-in Information:

Date/Time: Friday, November 12, 2021, 11 am - 12 pm PDT

https://uchealth.zoom.us/meeting/register/tZEtcO2qrzIuHNEqe6qPXzswJK4--tUlWEGa

PanoramaWeb: A Resource to Manage, Share, and Collaborate on Quantitative Mass Spectrometry Experiments

Thu, 26 Aug 2021 11:00:17 -0700

*Watch recorded webinar here: https://youtu.be/CteGdDsup3Y

*Webinar slides:
https://www.slideshare.net/dkNET/dknet-webinar-panoramaweb-a-resource-to-manage-share-and-collaborate-on-quantitative-mass-spectrometry-experiments12102021

Join dkNET Webinar on Friday, December 10, 2021, 11 am - 12 pm PST

Abstract

PanoramaWeb was developed with the goal to address the growing need for a community resource to store, share, analyze, and reuse mass spectrometry assays created and refined with the Skyline Windows Client via a web-browser. Panorama allows laboratories to store and organize curated results contained in Skyline documents with fine-grained permissions, which facilitates distributed collaboration and secure sharing of published and unpublished data via a web-browser interface. It is fully integrated with the Skyline workflow and supports publishing a document directly to a Panorama server from the Skyline user interface. Panorama captures the complete Skyline document information content in a relational database schema. Curated results published to Panorama can be aggregated and exported as chromatogram libraries. These libraries can be used in Skyline to pick optimal targets in new experiments and to validate peak identification of target peptides. The Panorama Public repository makes use of the full data visualization capabilities of Panorama which facilitates disseminating results processed with Skyline upon publication. The website can provide reviewers and readers access to the data behind the published conclusions and improves the transparency of quantitative mass spectrometry assays. Additionally, PanoramaWeb is build on-top of LabKey Server, a biomedical research data management system. This makes PanoramaWeb an ideal resource for mass spectrometry and proteomics collaborative projects as there is infrastructure for sharing documents, wiki pages, message boards, and managing shared specimens etc... Laboratories and organizations can set up Panorama locally by downloading and installing the software on their own servers. They can also request freely hosted projects on https://panoramaweb.org, a Panorama server maintained by the Department of Genome Sciences at the University of Washington.

Types of questions that can be answered with PanoramaWeb?

Are there any existing proteomics experiments or quantitative assays that study my favorite protein?
Can I find the underlying RAW and processed mass spectrometry data generated in this paper I am reading?
How can I share experimental mass spectrometry details and method validation to colleagues who want to implement a specific assay?
Can I track instrument performance longitudinally?

Presenter: Michael J. MacCoss, Ph.D. Professor of Genome SciencesUniversity of Washington

Dial-in Information:

Date/Time: Friday, December 10, 2021, 11 am - 12 pm PST

https://uchealth.zoom.us/meeting/register/tZwrde2rqDkqGNVcuKLMy-UEhyyae9QSEfgb

Pancreatlas™: mapping the human pancreas in health and disease

Thu, 23 Sep 2021 11:25:37 -0700

*Watch recorded webinar here: https://youtu.be/UZAtXuzNGt0

*Webinar Slides: https://www.slideshare.net/dkNET/dknet-webinar-pancreatlas-mapping-the-human-pancreas-in-health-and-disease-01282022

Join dkNET Webinar on Friday, Jan. 28, 2022, 11 am - 12 pm PST

Abstract

Pancreatlas is an online resource that houses and links human pancreas imaging data with clinical data to facilitate advances in the understanding of diabetes, pancreatitis, and pancreatic cancer. Increasingly, human tissue phenotyping programs and projects are generating complex data from numerous imaging modalities, yet only a fraction are shared as static figures for publication. We built Pancreatlas to bring together imaging data under a standardized, intuitive, and interactive platform that is publicly accessible and connects data from disparate research efforts in order to accelerate discovery science. Pancreatlas currently contains over 1,800 full-resolution images organized across seven context-aware collections, including whole-slide images of histological stains and fluorescent immunohistochemical labeling, multiplex modalities CODEX and imaging mass cytometry, and confocal microscopy. Pancreatlas utilizes an open-source web application and application programming interface (API) framework (Flexible Framework for Integrating and Navigating Data; FFIND; https://github.com/Powers-Brissova-Research-Group/FFIND) and a back-end instance of Open Microscopy Environment Remote Objects Plus (OMERO Plus, Glencoe Software), which together integrate domain-specific data exploration with interactive image viewing (PathViewer, Glencoe Software). Looking ahead, we plan to expand connectivity and integration with other platforms and pancreas mapping efforts, including development of a graph database, improved annotations and ontologies, and enhanced search and browsing, as well as expanding connections between imaging and other omics resources.

The top 4 key questions that Pancreatlas can answer:

1. How does the architecture of the human pancreas change during the first decade of life?

2. What compositional alterations occur in islets from donors with type 1 and type 2 diabetes?

3. Which markers can be used to visualize non-endocrine cell types in human pancreas?

4. How much variation exists across histological features of clinically “normal” pancreata?

Presenters:

Marcela Brissova, PhD, Research Professor, Vanderbilt University Medical Center

Jean-Philippe Cartailler, PhD, Director of Creative Data Solutions, Vanderbilt University

Diane Saunders, PhD, Research Instructor, Vanderbilt University Medical Center

Dial-in Information:

Date/Time: Friday, January 28, 2021, 11 am - 12 pm PST

https://uchealth.zoom.us/meeting/register/tZAkde-rpzwoH9L6xVIg9wK5thAD1-N3lmZF

The Stimulating Peripheral Activity to Relieve Conditions (SPARC): Advancing Bioelectronic Medicine through Open Science

Thu, 27 Jan 2022 17:06:05 -0800

*Watch recorded webinar here: https://youtu.be/x7gtUGkSNDI

*Webinar Slides:
https://www.slideshare.net/dkNET/dknet-webinar-the-stimulating-peripheral-activity-to-relieve-conditions-sparc-advancing-bioelectronic-medicine-through-open-science-02112022

Join dkNET Webinar on Friday, Feb. 11, 2022, 11 am - 12 pm PST

Abstract

The Stimulating Peripheral Activity to Relieve Conditions (SPARC) program is supported by the NIH Common Fund to provide a scientific and technological foundation for future bioelectronic medicine devices and protocols.The goal of the SPARC program is to identify neural targets and accelerate the development of therapeutic devices that modulate electrical activity in the vagus and other nerves to help treat diseases and conditions, such as hypertension and gastrointestinal disorders, by precisely adjusting organ function. Some of the ways the SPARC program is working to advance this goal include: (1) Constructing anatomical and functional datasets from organ-specific neural circuitry, including those that mediate visceral pain. (2) Mapping the human vagus nerve, including circuit-level descriptions of human vagal anatomy and physiology. (3) Creating new tools and technologies, including open-source neuromodulation platforms, to enable precise manipulation and measurement of nerve-organ interactions and their associated functions. (4) Establishing effective research partnerships with clinicians, basic scientists, engineers, and private industry to pursue data-intensive, mechanistic clinical studies. (5) Implementing prize challenges for the research and development community to demonstrate proof-of-principle neuromodulation therapeutic benefits with limited off-target effects. (6) Developing the SPARC Portal to make high value autonomic nervous system data sets, maps, and computational studies freely available to the wider research community. The overall vision for the SPARC Portal is to accelerate autonomic neuroscience research and device development by providing access to digital resources that can be shared, cited, visualized, computed, and used for virtual experimentation.

The top 3 key questions that SPARC can answer:

1. Where can I find a detailed map ANS-end organ interactions?

2. Where can I find anatomical and physiological data for constructing computational models of the heart?

3. What are the targets of the vagus nerve?

Presenter: Dr. Maryann Martone, Professor Emerita, Department of Neurosciences, University of California San Diego

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZcvcOygpzgpGtMdbKElu-WzvPijxh8lvrF_

Uncovering novel mediators and mechanisms of leptin action

Mon, 22 Nov 2021 13:43:05 -0800

*Watch recorded webinar here: https://youtu.be/dhIG8q8q5ss

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-uncovering-novel-mediators-and-mechanisms-of-leptin-action-02252022

dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series

Join dkNET Webinar on Friday, Feb. 25, 2022, 11 am - 12 pm PST

Abstract

Rates of obesity and diabetes continue to rise, impacting the health and wellbeing of millions. Treatment options are limited, in part because of our incomplete understanding of the biology of hunger and energy expenditure. The hormone leptin is produced by adipose tissue and signals the repletion of adipose energy stores to leptin receptor (Lepr)-expressing neurons in the hypothalamus. Leptin- and Lepr-deficient humans and rodents display marked hyperphagia, reduced energy expenditure, and extreme obesity. The crucial cellular targets (i.e., Lepr neurons) and transcriptional mechanisms that mediate these responses remain largely unknown, however. To reveal the cellular architecture of Lepr cells, we performed single nucleus RNA-seq of the hypothalamus in lean and obese rodents and macaques and in enriched mouse Lepr neurons. We identified over a dozen distinct Lepr neuron populations distributed across multiple hypothalamic nuclei, including a novel conserved population of Lepr neurons that is marked by Glp1r expression and which displays strong transcriptional responses to diet-induced obesity. Deleting Lepr from these Lepr/Glp1r cells resulted in excessive food intake and weight gain, revealing the importance of these for the control of energy balance by leptin. In contrast, we found that ventromedial hypothalamic (VMH) Lepr neurons represent a distinct class of VMH neurons that promote energy expenditure. Finally, we showed that leptin signaling during obesity remains intact in a subset of hypothalamic Lepr populations, while other Lepr neurons that play key roles in energy balance exhibited blunted responses. Overall, these studies reveal the neuronal structure of leptin action and highlight cell populations and molecular pathways that represent potential targets for obesity therapy.

Presenter: Alan Rupp, Ph.D. Research Investigator in Metabolism, Endocrinology, and Diabetes, University of Michigan Medical School.

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZUkf-uuqDgtGtUvRe2LtF1C-9UXAvWiNURA

The Pediatric Obesity Metabolome and Microbiome Study (POMMS): Characterization of Pre- and Post-Intervention Microbiome and Metabolome in Adolescents with Obesity

Tue, 07 Sep 2021 16:37:33 -0700

*Watch recorded webinar here: https://youtu.be/eWdMUoEQpzs

Join dkNET Webinar on Friday, March 11, 2022, 11 am - 12 pm PST

Presenter: John F. Rawls, PhD (Professor) and Jessica R. McCann(Senior Research Associate), Departments of Molecular Genetics & Microbiology, Duke Microbiome Center, Duke University School of Medicine

Abstract

Pediatric obesity strongly predicts adult obesity as well as metabolic and cardiovascular disease. However recommended interventions result in a heterogeneous response and underlying predictive factors for treatment success remain unknown. We designed the POMMS study (https://sites.duke.edu/pomms/) to characterize the microbiome and metabolome in adolescents with obesity (OB) at baseline when compared to an age-matched healthy weight control group (HWC), and in response to weight loss intervention. We enrolled a racially and ethnically diverse group of 223 adolescents aged 10-18 years with Body Mass Index (BMI) >= 95th percentile, along with 71 HWC participants. We collected clinical data, fasting serum, and fecal samples at repeated intervals over a 6 month intervention. Here we present clinical data, targeted serum metabolite measurements, fecal 16S rRNA gene amplicon sequencing as well as fecal microbiome shotgun sequencing of samples from adolescents both at baseline and study completion. We found that clinical correlates such as insulin levels at baseline were associated with intervention outcome. While adolescents with OB had predictably higher clinical lab values when compared to HWC counterparts, we found evidence of a metabolite signature that appears to be unique to adolescents with OB and unlike adults with OB. Several targeted metabolites and specific microbial taxa at baseline were significantly associated with OB versus HWC status, or with changes in BMI or insulin resistance scores following the intervention. Preliminary analysis of microbiome shotgun sequencing suggested that distinct metabolic and enzymatic pathways encoded by the microbiome were associated with OB v HWC status at baseline. Fecal microbiome transplant studies using gnotobiotic mice have previously established that microbiomes from adult donors with OB are sufficient to promote weight gain, adiposity, and associated metabolic signatures. However, it remained unknown whether microbiome from adolescents with OB has similar causal roles. We colonized germ-free mice with fecal slurries from baseline and 6-month patient samples. Physiologic and metabolic outcomes were compared with those from mice colonized with age matched HWC fecal slurries. While metabolomic and longitudinal sampling results from these FMT studies are pending, we found that starting weight of the recipient mice had the strongest association with weight gain, followed by OB versus HWC status of the donor. In conclusion, we identify a unique metabolomic and microbiome signature of obesity in adolescents, features of which could be used to predict intervention outcome.

The top 3 key questions that POMMS can answer:

1. Biosample and data set of microbiome and metabolome from a diverse group of adolescents with and without obesity.

2. What clinical, microbial, and metabolic features are associated with weight loss over a 6 month intervention?

3. What clinical, microbial, and metabolic features separate healthy weight adolescents from adolescents with obesity?

Dial-in Information:

Date/Time: Friday, March 11, 2021, 11 am - 12 pm PST

https://uchealth.zoom.us/meeting/register/tZEvd-qqqzwjG9P7rVepWAxbrOUhL6SgD2FU

Machine Learning to Analyze Pancreas Imaging in Diabetes

Mon, 22 Nov 2021 14:10:34 -0800

dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series

*Watch recorded webinar here: https://youtu.be/CdxpTL9nsuQ

*Webinar slides:
https://www.slideshare.net/dkNET/dknet-webinar-machine-learning-to-analyze-pancreas-imaging-in-diabetes-04222022

Join dkNET Webinar on Friday, Apr. 22, 2022, 11 am - 12 pm PDT

Presenter: Jack Virostko, Ph.D. Assistant Professor of Diagnostic Medicine, Dell Medical School, University of Texas at Austin; Appointments in Oncology, Oden Institute for Computational Engineering and Sciences, LIVESTRONG Cancer Institutes

Abstract

The pancreas is smaller in individuals with diabetes and those at risk for developing the disease. Furthermore, quantitative measures of pancreas morphology and composition are altered in individuals with diabetes and display longitudinal changes accompanying disease progression. This talk will introduce MRI techniques for interrogating the pancreas. I will also demonstrate how machine learning may improve our understanding of pancreas changes in individuals with diabetes.

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZ0sc-qqrz0vE9VM1SrwKgQrPc0NMsHvguy1

T Cell Antigen Discovery: Experimental and Computational Approaches

Thu, 28 Apr 2022 17:28:13 -0700

*Watch recorded webinar here: https://youtu.be/6vnpI6RXmBs

*Webinar Slides: https://www.slideshare.net/dkNET/dknethirn-webinar-t-cell-antigen-discovery-experimental-and-computational-approaches-042822

dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series

Co-Hosted with Human Islet Research Network (HIRN)

Join dkNET Webinar on Friday, April 27, 2022, 9 am - 10 am PDT

Presenter: Alok V. Joglekar, Ph.D.Assistant Professor, Center for Systems Immunology and Department of Immunology, University of Pittsburgh School of Medicine

Abstract

T cells are key players in many autoimmune diseases including Type 1 Diabetes. T cell responses are highly antigen specific by virtue of their T cell receptors (TCRs), that recognize epitopes on target cells. The enormous diversity of TCRs in an immune response poses a challenge in studying them, particularly regarding their antigenic specificity. Several experimental approaches have been developed to identify T cell specificities, with a recent surge in cell-based assays. More recently, computational approaches to predict T cell specificity are being developed and show great promise. This webinar will provide an overview of the experimental and computational approaches to identify T cell antigens. Furthermore, we will highlight the research performed in the Joglekar lab towards applying these approaches for auto-antigen discovery in Type 1 Diabetes. Finally, we will project what the future of these approaches may be, particularly for studying autoimmune diseases.

Dial-in Information: https://cityofhope.zoom.us/meeting/register/tJcqfu6sqjsrHdD2bCBCL0zAaR82Qb_0YEsE

Integrative Artificial Intelligence Approach to Predict Type 1 Diabetes

Mon, 22 Nov 2021 14:29:25 -0800

*Watch recorded webinar here: https://youtu.be/lGnXXYMUdl8

*Webinar Slides: https://www.slideshare.net/dkNET/dknet-webinar-integrative-artificial-intelligence-approach-to-predict-t1d-05162022

dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series

Join dkNET Webinar on Friday, May 13, 2022, 11 am - 12 pm PDT

Presenter: Kenneth Young, Ph.D. Assistant Professor, Health Informatics Institute, University of South Florida

Abstract

Type 1 diabetes (T1D) is a complex and heterogenous autoimmune disease that is no longer considered a clear-cut clinically diagnosed disease. T1D is multifaceted and the efficacy of therapeutic interventions varies greatly. With the evidence of etiological differences in T1D and the availability of high-dimensional multi-omics data in combination with clinical and environmental data, this project aims to use an artificial intelligence (AI) exploratory approach that may aid in the identification of new markers to predict IA and T1D.

This project utilizes data from NIDDK funded by The Environmental Determinants of Diabetes in the Young (TEDDY) study. TEDDY has generated over 900TB of diverse data types including multi-omics data, deep phenotyping, and environmental factor measurements every three-six months for fifteen years. We utilize deep learning methods, such as convolutional neural networks (CNN) and recurrent neural networks (RNN) that apply bidirectional long short-term memory (LSTM), in combination with multi-layer perceptron (MLP), to evaluate the prediction of IA and T1D. To aid in T1D predication, this project uses innovative and transformative AI approaches that combine temporal and static data, which may ultimately provide insights into the complex heterogeneity, diversity, and pathogenesis of T1D. The knowledge gained from this project may not only help advance the T1D community, but may have a broad impact on a variety of autoimmune diseases such as celiac and thyroid diseases which frequently coexist and share genetic susceptibility to T1D.

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZMrdeGspzorGt0idOnF6hz0J-5LGhsFd2HK

Visualizing Organelle and Cell Longevity In Situ

Mon, 22 Nov 2021 14:02:46 -0800

*Watch recorded webinar here: https://youtu.be/-EgBNITq_2I

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-visualizing-organelle-and-cell-longevity-in-situ-052022

dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series

Join dkNET Webinar on Friday, May 20, 2022, 11 am - 12 pm PDT

Abstract

Cells in largely post-mitotic organs can be as old as their host organism. These long-lived cells (LLCs) face a lifelong demand for performance to maintain organ function and are constantly exposed to drivers of molecular and cellular damage. Accordingly, dysfunction of LLCs is associated with aging and age-associated disease processes. Understanding cellular longevity mechanisms requires the identity and distribution pattern of LLCs. We developed imaging tools to quantify the age of cells in situ, which led to the discovery of new LLC types throughout the mouse body. This includes different cell types in the pancreas, where most beta cells can be as old as neurons in the brain. In this presentation, I will show how to we apply different microscopy tools to bridge spatial and temporal scales in biology to quantify protein complex, organelle, and cell age in tissues. Applicable to virtually any cell, this imaging platform can reveal the temporal dynamics and longevity of structural components in vivo and their contribution to cell and tissue organization and function.

Presenter: Rafael Arrojo e Drigo, Ph.D. Assistant Professor, Department of Molecular Physiology and Biophysics, Vanderbilt University

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZAlfu6hqz0oGty2d9-2_LDJXYl-8B4SElnP

The Human BioMolecular Atlas Program (HuBMAP)

Wed, 21 Sep 2022 16:23:39 -0700

*Watch recorded webinar here: https://youtu.be/llZuT6dxJV0

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-the-human-biomolecular-atlas-program-hubmap-10142022

Join dkNET Webinar on Friday, October 14, 2022, 11 am - 12 pm PT

Abstract

HuBMAP aims to catalyze the development of an open, global framework for comprehensively mapping the human body at cellular resolution. HuBMAP goals include: (1) Accelerate the development of the next generation of tools and techniques for constructing high resolution spatial tissue maps. (2) Generate foundational 3D tissue atlases. (3) Establish an open data platform. (3) Coordinate and collaborate with other funding agencies, programs, and the biomedical research community. (4) Support projects that demonstrate the value of the resources developed by the program. The HuBMAP Portal can be found at https://portal.hubmapconsortium.org and the Visible Human MOOC describes the compilation and coverage of HuBMAP data, demonstrates new single-cell analysis and mapping techniques, and introduces major features of the HuBMAP portal.

The top 3 key questions that HuBMAP can answer:

1. What assay types are best to map the human body in 3D and across scales?

2. What Common Coordinate System (CCF) is best to construct the Human Reference Atlas?

3. How can others help construct and/or use the Human Reference Atlas?

Presenters:

Katy Börner, PhD, Victor H. Yngve Distinguished Professor of Engineering and Information Science, Department of Intelligent Systems Engineering and Information Science, Indiana University

Jeffrey Spraggins, PhD, Assistant Professor, Department of Cell and Developmental Biology, Vanderbilt University

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZEqd-urqjssHt0becUOTF4qCk5G9yqSTVRo

Date/Time: Friday, October 14, 2022, 11 am - 12 pm PT

Discovering and Evaluating Antibodies, Cell Lines, Software Tools, and Others

Thu, 13 Oct 2022 15:51:20 -0700

*Watch recorded webinar here:
https://youtu.be/S_d_pH6M318

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-discovering-and-evaluating-antibodies-cell-lines-software-tools-and-more-10282022pdf

Join dkNET Webinar on Friday, October 28, 2022, 11 am - 12 pm PT

Abstract

dkNET’s Resource Reports enable researchers to discover research resources that would be useful for their research. The resource report integrated data set and analytics platform combines Research Resource Identifiers (RRIDs), text mining and data aggregation to help you identify key biomedical resources, track these resources, and compare their performance. Resource Reports offer a detailed overview of each resource along with citation metrics from the biomedical literature and even information about what resources have been used together. You'll gain insights about who is using particular resources and how the community views those resources, including usage in published protocols.

The dkNET Co-PI, Dr Jeffrey Grethe, will give you live demos during this webinar, including:

How to find and select a research resource such as an antibody or cell line
How to find Research Resource Identifiers (RRIDs) and proper citation of your resources
How to register resource to obtain RRIDs if the resources do not exist in the system

We hope this short webinar will provide an opportunity to use this tool to shape your research activities.

Presenter:

Jeffrey Grethe, PhD, dkNET Co-Principal Investigator, University of California San Diego

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZ0tdOivrzwqGt1jNMWl1Z-gEgJ0kFdocqWp

Date/Time: Friday, October 28, 2022, 11 am - 12 pm PT

The Accelerating Medicines Partnership Common Metabolic Diseases Knowledge Portal: a resource for research into the genetic and genomic basis of diabetes and cardiometabolic disease

Wed, 21 Sep 2022 16:17:48 -0700

*Watch recorded webinar here: https://youtu.be/0sLKeCDw06U

Join dkNET Webinar on Friday, November 18, 2022, 11 am - 12 pm PT

Abstract

Common metabolic diseases are complex, as many genetic loci influencing disease risk have been identified to date. Discovering the genes and biological mechanisms through which these variants impact specific diseases requires the integration of multiple data types. We have created the Common Metabolic Diseases Knowledge Portal (CMDKP; hugeamp.org) to promote understanding and treatment of common metabolic diseases. The CMDKP aggregates and integrates multiple data types for 433 traits: full GWAS summary statistics from over 400 datasets, many of which are not available elsewhere; tissue-specific epigenomic annotations from over 5,500 datasets, via the Common Metabolic Diseases Genome Atlas (CMDGA; cmdga.org); manually curated credible sets; and multiple expert-generated effector gene lists. Bioinformatic methods are implemented at scale across these data: novel sample overlap-aware genetic association meta/analysis meta-analysis; predictions of variant effects (VEP and BASSETT), gene-level association analysis (MAGMA); credible set calculations; and annotation global enrichments (GREGOR and S-LDSC). The integrated results are accessible via an open-access web portal, which offers both forward and reverse genetics workflows, with results publicly available through direct download and programmatic APIs. In a forward genetics approach, researchers can query a trait or disease and see genome-wide variant- and gene-level genetic associations and tissue-specific epigenomic annotations that are globally enriched for those genetic associations. In a reverse genetics approach, the Region, Gene, and Variant pages distill and summarize results for a gene or variant of interest. Interactive tools allow researchers to perform custom, on-the-fly analyses, such as generating gene-level association scores based on protected individual-level data and assessing the weight of genetic evidence linking a gene to disease. The Variant Sifter visualizer allows users to explore, filter, and prioritize genetically associated variants, credible sets, and tissue-specific epigenomic annotations across a region, providing decision support for the prioritization of variants and genes for further research. We work directly with researchers to create community portals that showcase the results and methods deemed most valuable by each disease research community. In addition, we have created the Bring Your Own Results (BYOR) software service, which offers an auto-generated sortable data table, data filters, and visualizers, and allows individual researchers to easily and rapidly create web pages or portals to share results with reviewers, collaborators, or the public. Collaborations are welcome; please contact help@kp4cd.org.

The top 3 key questions that AMP-CMDKP can answer:

1. What genes are associated with chronic kidney disease?

2. Which tissues are most relevant for my disease or trait of interest?

3. What are the curated effector gene predictions for type 1 diabetes?

Presenter: MacKenzie Brandes, Project Manager, Broad Institute

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZEqd-6gpjosE9YJ9zVw5NdUJNtCKGYh_G4c

Date/Time: Friday, November 18, 2022, 11 am - 12 pm PT

The mission and progress of the(sugar)science: helping scientists who study Type 1 diabetes connect

Wed, 16 Nov 2022 16:08:58 -0800

*Watch recorded webinar here: https://youtu.be/g54yxvHjeh4

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-discovering-and-evaluating-antibodies-cell-lines-software-tools-and-more-10282022pdf

Join dkNET Webinar on Friday, Dec. 9, 2022, 11 am - 12 pm PST

Presenters:

Monica Westley, PhD, Founder, the(sugar)science

Tiffany Richardson

Neha Majety

Abstract

The(sugar)science was launched two years ago with the aim of helping scientists who study type 1 diabetes (T1D) and related interdisciplinary fields connect globally. We also wanted to create a digital space where trainees in the field can be supported, celebrated and connected to future positions. As part of our mission, our all volunteer team created the State of the Science series (2021. 2022), connecting global thought leaders around T1D research topics for discussion with a larger scientific audience. The second State of Science series was led by women scientists following the ADA publication which highlighted the paucity of women scientists in the leadership positions in the field.

To encourage the scientific community at large to dive into pre-existing data and pull out novel hypotheses that pertain to T1D, we created and together with dkNET, hosted D-Challenge 2021 and 2022. These competitions awarded $40K and $50K respectively to those who mined data and developed the most creative and testable hypothesis as judged by scientific experts in the field. These teams were also able to have an audience with the JDRFT1D Fund as part of a "pitch polish" which facilitated their interaction with venture capital.

To date, we have hosted over 200 interviews with T1D focused scientists in academia and industry and have an audience of 35K. Our reach on social media continues to grow and our metrics indicate a robust following. We share opportunities for positions in the field, engage and support trainees and together, our young scientific team published a paper, Similarities between bacterial GAD and human GAD65: Implications in gut mediated autoimmune type 1 diabetes, PLOS, February 2022.

We are currently engaged in the build of a T1D TCR Repository. We connected the AIRR data commons community with top TCR scientists in the field to begin this community based venture. It has the possibility to be incredibly instructive in defining the prodrome , which will further inform the field as it pertains to understanding the etiology of T1D.

Current team members that will join the discussion today will be Neha Mejety, Johns Hopkins University undergraduate and Tiffany Richardson, doctoral degree candidate at VUMC Diabetes.

The top 3 key questions that the(sugar)science can answer:

1. How can I find scientists to collaborate with in Type 1 diabetes research?

2. Where can I learn about Type 1 diabetes trending topics?

3. Where can I find forums to discuss novel ideas with scientists or key opinion leaders and find opportunities for Type 1 diabetes research.

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZ0sce2rqj0uGtEbhiij8Wpdf_uAHnbYfufp

Date/Time: Friday, December 9, 2022, 11 am - 12 pm PST

Attie Lab Diabetes Database (Rescheduled)

Mon, 20 Dec 2021 15:24:55 -0800

This webinar is rescheduled to next year. The date will be announced once it is confirmed.

Join dkNET Webinar on Friday, December 9, 2022, 11 am - 12 pm PT

Presenters:

Alan D. Attie, PhD, Jack Gorski Professor of Biochemistry, University of Wisconsin-Madison

Mark P. Keller, PhD, Distinguished Scientist, University of Wisconsin-Madison

Dial-in Information:

Date/Time: Friday, December 9, 2022, 11 am - 12 pm PT

https://uchealth.zoom.us/meeting/register/tZUvc-urqzkuG9Cl_0ElUyZr8Ar-8IX5_zqn

dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sharing Mandates"

Thu, 22 Dec 2022 15:34:54 -0800

Watch recording here:
https://youtu.be/uhAAG6tfEnQ

*Slides:
https://www.slideshare.net/dkNET/dknet-office-hours-are-you-ready-for-2023-new-nih-data-management-and-sharing-mandates

For all proposals submitted on/after January 25 2023, NIH will require the sharing of data from all NIH funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and resources that could help.

Date/Time: Friday, January 13, 2023, 11 am - 12 pm PT (2 pm - 3 pm ET)

FAIR Data Curation of Antibody/B-cell and T-cell Receptor Sequences in the AIRR Data Commons

Thu, 22 Dec 2022 11:18:04 -0800

*Watch recorded webinar here: https://youtu.be/yL2CkZjgVyM

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-fair-data-curation-of-antibodybcell-and-tcell-receptor-sequences-in-the-airr-data-commons-01272023finalpdf

Join dkNET Webinar on Friday, January 27, 2023, 11 am - 12 pm PT

Presenters

Dr. Felix Breden, Scientific Director, iReceptor

Dr. Brian Corrie, Technical Director, iReceptor

Dr. Kira Neller, Bioinformatics Director, iReceptor

Abstract

AIRR-seq data (antibody/B-cell and T-cell receptor sequences from Adaptive Immune Receptor Repertoires) can describe the adaptive immune response in exquisite detail, and comparison and analysis of these data across studies and institutions can greatly contribute to the development of diagnostics and therapeutics, including the discovery of monoclonal antibodies for treatment of autoimmune diseases.

The AIRR community has developed protocols and standards for curating, analyzing and sharing AIRR-seq data (www.airr-community.org), and supports the AIRR Data Commons, a set of geographically distributed repositories that follows the AIRR Community’s metadata standards and the FAIR principles. The ADC currently comprises > 5 Bn receptor sequences from over 86 studies and ~9000 repertoires. The data model of the ADC has recently been expanded to include gene expression and cell phenotype data from single immune receptor cells, as well as MHC/HLA genotyping.

The iReceptor Gateway (ireceptor.org) queries this AIRR Data Commons for specific “metadata”, e.g. “find all repertoires from T1D studies” or for specific CDR3 sequences (e.g., find all repertoires from healthy individuals expressing this CDR3 sequence). Data from these federated repositories can then be analyzed through the Gateway by several sophisticated analysis tools, or downloaded for further analysis offline. The iReceptor Team at Simon Fraser University has recently initiated a collaboration to greatly expand the amount of bulk and single-cell immune profiling data from T1D studies in the AIRR Data Commons. For more information on obtaining or sharing AIRR-seq data contact support@ireceptor.org.

The top 3 key questions that the Adaptive Immune Receptor Repertoire (AIRR) can answer:

1. A researcher observes that many individuals with Type 1 Diabetes express a specific B-cell or T-cell receptor compared to controls (i.e., a “public” clonotype). To what degree is this receptor observed to be public across other T1D studies or other autoimmune disease populations?

2. Can Machine Learning be used to identify individuals who will respond well to a new cancer immunotherapy based on differences in their antibody/B-cell or T-cell receptor repertoires as curated in the AIRR Data Commons?

3. Is there an association between particular HLA, immunoglobulin (IG), or T-cell receptor (TR) germline gene polymorphisms and propensity toward specific infectious or autoimmune diseases?

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZ0scuihrT8uGtZiOtklnOAtsoK4hFBsz5-u

Date/Time: Friday, January 27, 2023, 11 am - 12 pm PT

Choosing Sample Sizes for Multilevel and Longitudinal Studies Analyzed with Linear Mixed Models

Wed, 21 Sep 2022 16:12:32 -0700

*Watch recorded webinar here: https://youtu.be/tHiAs9wKU98
*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-choosing-sample-sizes-for-multilevel-and-longitudinal-studies-analyzed-with-linear-mixed-models-02102023

Join dkNET Webinar on Friday, February 10, 2023, 11 am - 12 pm PT

Presenter:

Kylie K. Harrall, MS, Research Instructor, Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus

Abstract

Planning a reproducible study requires selecting a sample size which will ensure appropriate statistical power. Free point-and-click software (Kreidler et al., Journal of Statistical Software, 2013, 10.18637/jss.v054.i10) makes it easy to select a sample size for clustered and longitudinal designs with linear mixed models. The software, a suite of training modules, and reference materials are freely available online (www.SampleSizeShop.org ). The software interface and training materials are aimed at biomedical scientists, included those funded by NIDDK. We give examples of study designs for which the software will compute power and sample size, including a study with clustering, a study with longitudinal repeated measures, and a study with multiple outcomes, where heterogeneity of response among subgroups is of interest.

The top 3 key questions that the Sample Size Shop can answer:

1. What free, online, point-and-click, wizard-style, NIH-funded, validated, published power and sample size software provides calculations for studies with clusters, longitudinal studies, and longitudinal studies with clusters?

2. Can GLIMMPSE (www.SampleSizeShop.org) compute power and sample size for randomized controlled clinical trials and observational studies funded by NIDDK?

3. Why use validated power and sample size software instead of writing simulations?

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZwvde2hpz4iGNRSwkcqPd6qUQxQuZBhGFfW

Date/Time: Friday, February 10, 2023, 11 am - 12 pm PT

Postpartum Glucose Screening Among Homeless Women with Gestational Diabetes

Wed, 11 Jan 2023 16:15:15 -0800

*Watch recorded webinar here: https://youtu.be/lmosG9m20JI

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-postpartum-glucose-screening-among-homeless-women-with-gestational-diabetes-02242023

dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series

Join dkNET Webinar on Friday, Feb. 24, 2023, 11 am - 12 pm PST

Abstract

Women with gestational diabetes mellitus (GDM) are at high risk of developing glucose intolerance after delivery. In the long term, women with GDM have a nearly 10-fold higher risk of developing type 2 diabetes mellitus (T2D) than women without GDM. The American Diabetes Association (ADA) and the American College of Obstetrics and Gynecology (ACOG) recommend that women with GDM undergo a 75-g oral glucose tolerance test (OGTT) between four and 12 weeks postpartum, and periodically thereafter. However, postpartum glucose screening (PGS) rate is historically low despite of various interventions to improve such rate. We hypothesized that PGS rate is lower among postpartum homeless women than their housed counterparts, and that interventions to improve PGS rate among postpartum homeless women with GDM should be tailored to their unique circumstances. The Japanese Society of Diabetes and Pregnancy (JSDP) modified the method to perform PGS with random plasma glucose (RPG) and glycated hemoglobin (HbA1c), which are simple and less invasive, to reduce the risk of COVID-19 infection by shortening the time spent in the hospital. RPG or HbA1c test do not require fasting. Therefore, homeless women who utilized care for other reasons could have the test as PGS. Given the barriers faced by homeless individuals, we hypothesize that RPG and HbA1c at healthcare utilizations during the postpartum period could be one of the strategies to identify high-risk individuals early because 1] healthcare utilizations are an opportunity for healthcare providers and social workers to educate homeless patients on GDM and their insurance eligibility and coverage for the screening, and 2] the physical barriers to health care access, which are often cited as a reason for the low PGS rate, are removed.

This proposed study will use administrative data from five states (AZ, CO, NC, NJ, and OR), which collectively include 9.3% of the US female homeless population. Each state will provide detailed, linked, multi-level, anonymized data for postpartum homeless women from four sources: 1] Medicaid claims; 2] Homeless Management Information System (HMIS); 3] birth records; and 4] the American Hospital Association (AHA) database to obtain hospital characteristics. With data from 2013 to 2020, an estimated sample size of 24,000 homeless women who delivered babies and 3,290 postpartum homeless women with GDM will be included.

First, we will estimate rates of GDM and PGS among homeless women. Second, we will estimate the cost-effectiveness of performing RPG and HbA1c tests when they utilized care among homeless women with GDM who missed the PGS 12 weeks postpartum. For individuals who meet the criteria for glucose intolerance defined by JSDP, OGTT will be performed to confirm the results in order to begin intervention. The effect of lifetime horizon will be estimated using the quality-adjusted life-years (QALYs).

This project has the potential to change clinical practice by providing evidence that performing RPG and HbA1c at the healthcare utilization during the postpartum period will be a cost-effective strategy to improve health status among homeless with GDM.

Presenter: Rie Sakai-Bizmark, PhD. Assistant Professor, The Lundquist Institute at Harbor-UCLA Medical Center, David Geffen School of Medicine at UCLA

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZ0rduGtrjMiGdAio41VIVSOxIw3f1isjj6X

dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sharing Mandates"

Fri, 24 Feb 2023 14:10:03 -0800

dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sharing Mandates"

Watch recording here: https://youtu.be/DreYDrxF6XM

*Slides: https://www.slideshare.net/dkNET/dknet-office-hours-are-you-ready-for-2023-new-nih-data-management-and-sharing-mandates-featuring-niddk-central-repository-03032023

For all proposals submitted on/after January 25 2023, NIH requires data sharing from all NIH-funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and available resources that could help.

In our upcoming session on March 3, 2023, we are pleased to invite Dr. Jeffrey Grethe, dkNET co-PI and expert on Data Management and Sharing, and Dr. Rebecca Rodriguez, Repository Program Director at NIDDK, Ms. Reaya Reuss, Chief of Staff to the Deputy Director at NIDDK, and the support team members from the NIDDK Central Repository. They will be available to answer any questions you may have.

Date/Time: Friday, March 3, 2023, 11 am - 12 pm PT (2 pm - 3 pm ET)

Estimating Relative Beta-Cell Function During Continuous Glucose Monitoring and Its Clinical Applications

Wed, 11 Jan 2023 16:24:49 -0800

*Watch recorded webinar here: https://youtu.be/MKgA02d7S2I

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-estimating-relative-betacell-function-during-continuous-glucose-monitoring-and-its-clinical-applications-03102023

dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series

Join dkNET Webinar on Friday, March 10, 2023, 11 am - 12 pm PST

Presenter: Joon Ha, PhD. Associate Professor, Department of Mathematics, Howard University, Washington DC.

Abstract

The most common form of diabetes, type 2 diabetes (T2D) is a failure of insulin-secreting pancreatic beta-cells to increase insulin to the level required to maintain normal blood glucose. Thus, identifying beta-cell function and insulin sensitivity in those who are at high risk is crucial to preventing and delaying the disease. Hyper-glycemic clamp and euglycemic hyper- insulinemic clamp are considered to be gold standard measures for these quantities. However, these two methods demand highly skilled labor and thus are cost-prohibitive. Glucose challenge tests have been used to estimate beta-cell function and insulin sensitivity. The product of beta-cell function and insulin sensitivity, termed the disposition index (DI), is of great value because it measures beta-cell function relative to insulin requirements. However, glucose challenge tests are expensive and time-consuming and therefore impractical to implement in large-scale clinical studies. To address this challenge, we developed a model disposition index (mDI estimated without insulin) that does not require insulin measurements during an oral glucose tolerance test (OGTT) (Ha et al., Diabetes 2021 (70) suppl. 1). mDI outperforms the conventional oral disposition index (oDI) at predicting progression to diabetes.

To further increase access and refine the assessments of beta-cell function, we are adapting our model to calculate a model disposition index using continuous glucose monitoring (CGM). CGM has been in the spotlight of diabetes management and has revolutionized the field of medicine as they are approved for glucose monitoring and clinical decision-making in patients with diabetes. CGM devices are relatively inexpensive compared to oral glucose challenge tests, accessible, and simple to use, especially in remote or free-living environments. The CGM device continuously measures interstitial glucose every 5 minutes and provides glucose profiles for 7-14 days. Thus, there are numerous data points compared to glucose challenge tests, but the abundant data points have not previously been used for estimating metabolic parameters. We compared mDI to two widely used CGM-derived metabolic parameters for assessing metabolic status and risk, mean glucose and glycemic excursion. Both mean glucose and glycemic excursion correlated strongly with mDI. The new approach promises to be cost- effective and easy to perform and therefore implementable in large-scale clinical studies. As for specific clinical applications, estimated model parameters during OGTTs identified ethnic differences in common pathways to T2D between Pima Indians and Koreans.

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZ0tdOuhqj4oGdc2g8Eq_dssrZYUPzHtZo15

Single Molecule Spatial Transcriptomics: interpreting the spatial expression of RNAs from FISH images

Wed, 11 Jan 2023 16:38:38 -0800

*Watch recorded webinar here: https://youtu.be/6E-r4B41c_g

dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series

Join dkNET Webinar on Friday, April 21, 2023, 11 am - 12 pm PDT

Presenter: Jing Liu, Ph.D. Assistant Professor, Department of Physics, Indiana University-Purdue University Indianapolis, Member of Center for Computational Biology and Bioinformatics, and Center for Diabetes and Metabolic Diseases; Associate member of Simon Comprehensive Cancer Center, Indiana University School of Medicine.

Abstract

Recently emerged spatial transcriptomics approaches combine the RNA sequencing (RNA-Seq) with spatial localization to reveal the spatial heterogeneity of transcriptome in pancreatic islet. However, the interrogation of the transcriptomic expression in a single cell is missing, particularly the spatial distribution of each RNA molecule. Here we proposed a quantitative approach to quantify the spatial distribution of RNA molecules in a single cell, and gave a case study to investigate the miRNA expression in single beta cells obtained from human pancreatic tissues. A multi-dimensional quantitative model was established to describe the spatial distribution of individual RNAs as a library of “features”, which includes RNA expression, locations, clustering/dispersion, and reciprocal positions. In particular, the degree of RNA clustering/dispersion was described by the mathematical model of clusters, i.e. Ripley's H function. Extracted features are then analyzed by statistical distribution modelling and supervised machine learning. Machine learning enables the classification of 3 groups of beta cells (control, T1D, and AAb+) using spatial transcriptomic features with high accuracy (65%±3%). Furthermore, it offers quantitative evaluation of those distinctive features contributing to the classification and phenotyping. All evidence suggests the spatial heterogeneity of transcriptome of beta cells in T1D, and this transcriptomic disparity has been leveraged to classify beta cells into different pathological conditions. This work will not only disclose fundamental mechanisms that are associated with beta cell survival in T1D; more practically, it could lead to important transcriptomic features of beta cells that could have clinical relevance in stratifying the T1D phenotypes.

Dial-in Information:
https://uchealth.zoom.us/meeting/register/tZ0vcu2gqj8rHdcZM2hJhDuB4xxR2yZ_STrd

Defining the Spatiotemporal and Molecular Trajectory of Beige Adipogenesis

Wed, 11 Jan 2023 16:46:31 -0800

*Watch recorded webinar here: https://youtu.be/NhKvdr14nMg

dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series

Join dkNET Webinar on Friday, May 12, 2023, 11 am - 12 pm PDT

Presenter: Nabil Rabhi, PhD. Instructor, Department of Biochemistry, Boston University School of Medicine

Abstract

The unique capacity of inguinal white adipose tissue (iWAT) to brown has emerged as a promising therapeutic approach for treating obesity and its adverse complications. Both white and beige adipose arise from a subpopulation of perivascular adipocyte progenitor cells. However, the early signaling events controlling ACP differentiation to beige adipocytes are still unknown.

To uncover the stromal cells heterogeneity during beige adipogenesis, we performed a single cell RNA-sequencing of iWAT under control conditions, treatment with beta3-adrenergic receptor (ADRB3) agonist, or exposure to cold. ScRNA-seq revealed the landscape of APCs undergoing beige adipogenesis. We identified a distinct subpopulation of APCs expressing SM22 (Smooth Muscle Protein 22-Alpha), that is predicated in silico to give rise to multiple cell types composing adipose depot. Using SM22 lineage tracing mouse model, we found that SM22+ APCs accumulate in response to cold and ?3-adrenergic stimulations but only cold-induced their differentiation to beige adipocytes. Further investigations revealed that beige adipogenesis is a multi-step signaling process involving paracrine communication between mature adipocytes and vascular progenitors. This process involves (1) ADRB3 activation of adipocytes, followed by (2) lipids release by mature adipocytes (3) that induce a metabolic switch and ADRB1 expression in SM22+ APCs. Activation of ADRB1 by catecholamine released under cold exposure promotes primed APCs differentiation to beige adipocytes.Altogether, our data uncovered early steps necessary to promote beige adipogenesis.

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZApc-msrj4jEtICetdKXiXPFuJrxwcPLPLf

Leveraging Computational Strategies to Identify Type 1 Diabetes Risk and Clinical Trial Responder Status

Wed, 11 Jan 2023 16:54:34 -0800

*Watch recorded webinar here: https://youtu.be/mouDqLrrhmI
*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-leveraging-computational-strategies-to-identify-type-1-diabetes-risk-and-clinical-trial-responder-status-05192023

dkNET New Investigator Pilot Program in Bioinformatics Awardee Webinar Series

Join dkNET Webinar on Friday, May 19, 2023, 11 am - 12 pm PDT

Presenter: Wenting Wu, PhD. Research Assistant Professor, Center for Diabetes and Metabolic Diseases, Department of Medical and Molecular Genetics, Associate Director of Data and Analytics Core for Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine

Abstract

Type 1 diabetes (T1D) is an immune-mediated disease that results in insulin insufficiency and affects 0.3% of the population, including both children and adults. To support clinical trial efforts, there is an urgent need to develop reliable biomarkers capable of predicting T1D risk and guiding therapeutic interventions. Recently, whole blood bulk RNA sequencing has been used to guide T1D clinical trial design and assess response to disease modifying interventions. While the use of bulk RNA sequencing is cost-effective, these datasets provide limited information about cell specific gene expression changes. Here, we aimed to apply computational strategies to deconvolute cell type composition using cell specific gene expression references. Single-cell RNA sequencing (scRNA-seq) was conducted to profile peripheral blood mononuclear cells obtained from youth within recent T1D onset and age- and sex-matched controls and identified 31 distinct cell clusters. Using this pre-defined reference dataset, we ran computational algorithms CIBERSORTx and other deconvolution methods simultaneously to deconvolute cell proportions using public clinical trial data. We focused our initial analysis on data from the TN-20 Rituximab trial, which tested the anti-CD20 monoclonal antibody rituximab vs placebo in recent onset T1D. This talk will introduce recent advances of scRNA-seq techniques and computational deconvolution methods and demonstrate that how we apply different deconvolution approaches for secondary analysis of existing clinical trial data, in the purpose of linking cell specific immune signatures associated with drug responder status.

Dial-in Information: https://youtu.be/mouDqLrrhmI

Discover the Latest from dkNET - Biomed Resource Watch

Thu, 18 May 2023 14:27:32 -0700

*Watch recorded webinar here: https://youtu.be/ZKi2NQ37zns
*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-biomed-resource-watch-06022023-258216425

The dkNET (NIDDK Information Network) team is announcing an exciting new service - Biomed Resource Watch (BRW, https://scicrunch.org/ResourceWatch), a knowledge base for aggregating and disseminating known problems and performance information about research resources such as antibodies, cell lines, and tools. We aggregate trustworthy information from authorized sources such as Cellosaurus, Antibody Registry, Human Protein Atlas, ENCODE, and many more. In addition, BRW includes antibody specificity text mining information extracted from the literature via natural language processing. BRW provides researchers and curators an easy-to-use interface to report their claims about a specific resource. Researchers can check information about a resource before planning their experiments via BRW-enhanced Resource Reports. This new service aims to help improve efficiency in selecting appropriate resources, enhancing scientific rigor and reproducibility, and promoting a FAIR (Findable, Accessible, Interoperable, Reusable) research resource ecosystem in the biomedical research community.

Join us on Friday, June 2, 2023, 11 am - 12 pm (PDT) for a webinar to introduce the following resources & topics:

An overview of dkNET
How Resource Reports benefit you
Biomed Resource Watch

Navigating Biomed Resource Watch
How to Submit a Claim

Presenter: Jeffrey Grethe, PhD, dkNET Principal Investigator, University of California San Diego

Date/Time: Friday, June 2, 2023, 11 am - 12 pm PDT

Link: https://uchealth.zoom.us/meeting/register/tZcrd-GurT8tHdyr61qZCoAQk6fLu6kKe2nZ

dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sharing Mandates"

Wed, 26 Jul 2023 15:11:53 -0700

Watch recording here: https://youtu.be/xzbjk6d4ZfQ

*Slides: https://www.slideshare.net/dkNET/dknet-office-hours-are-you-ready-for-2023-new-nih-data-management-and-sharing-mandates-on-september-22-2023

Date/Time: Friday, September 22, 2023, 11 am - 12 pm PT (2 pm - 3 pm ET)

The National Sleep Research Resource (NSRR) - Opportunities for Large-Scale Sleep and Circadian Data to Promote Understanding of Metabolic Diseases

Thu, 07 Sep 2023 11:53:39 -0700

*Watch recorded webinar here: https://youtu.be/FC3Nerfeh1E

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-the-national-sleep-research-resource-nsrr-opportunities-for-largescale-sleep-and-circadian-data-to-promote-understanding-of-metabolic-diseases-10272023

Join dkNET Webinar on Friday, October 27, 2023, 11 am - 12 pm PT

Presenter: Susan Redline, MD, MPH, Peter C. Farrell Professor of Sleep Medicine, Professor of Epidemiology, Harvard T.H. Chan School of Public Health

Abstract

Experimental, clinical and epidemiological studies have identified multiple inter-relationships of sleep with glucose regulation and metabolic disease. In one meta-analysis, after overweight and family history of diabetes, the next 7 top risk factors for incident diabetes were measures of sleep health. These included poor sleep quality, insomnia, short or extremely long sleep duration, and sleep apnea; each sleep problem was associated with incident diabetes with relative risks ranging from 1.38 to 1.74. A mechanism linking sleep apnea with diabetes is through the effects of intermittent hypoxemia on insulin sensitivity. However, studies using neurophysiological markers of sleep in healthy adults showed that selective reduction of slow wave sleep reduced glucose tolerance by 23%, thus additionally suggesting the importance neurophysiological mechanisms during sleep in glucose regulation. In support of this, longitudinal epidemiological studies demonstrated that higher proportions of slow wave sleep (N3) were protective for the development of type 2 diabetes. Recent animal and human studies also point to the effects of sleep micro-architecture—specifically the coupling of slow waves and spindles- on short-term and long-term glucose regulation, possibly through the effects on signaling between the hippocampus and hypothalamus, and changes in autonomic nervous system output. Experimental data also demonstrate a prominent role of the circadian system in regulating glucose and lipid levels. In support of those studies, epidemiological associations have identified significant associations between actigraphy-based measures of sleep irregularity (a marker of circadian disruption) with incident metabolic dysfunction and hypertension. This rich data implicating sleep disturbances as drivers of metabolic disease, coupled with data indicating a high prevalence of sleep and circadian disorders in the population, suggest novel opportunities to target sleep and circadian pathways for preventing or treating metabolic dysfunction, as well as key knowledge gaps.

The National Sleep Research Resource (NSRR; sleepdata.org) provides a large and growing repository of well-annotated polysomnograms (PSGs), actigraphy studies, and questionnaires, some associated with clinical and biochemical data relevant to understanding the links between sleep and circadian disorders with metabolic disease. Notably, the NSRR includes over 50,000 PSGs, which concurrently include multiple physiological signals with high temporal resolution, allowing generation of thousands of variables summarizing dynamic physiological changes and “cross-talk” between physiological systems that could be explored for understanding novel questions on sleep and metabolism. Actigraphy data in several well-established cohorts, including MESA and HCHS/SOL, with multiple days of daily rest-activity measurements, allow sleep-wake and circadian rhythm patterns to be characterized and related to health outcomes. Additional circadian, animal and human data are in the process of being ingested into NSRR.

This talk will: a) provide an overview of the links between sleep and metabolic disease; and b) provide an overview of the goals, structure, and content of the NSRR; and c) suggest opportunities for the metabolic researcher to study the inter-relationships between sleep and metabolic disease.

Top questions that can be asked of NSRR data:

1. What are the macro- and micro-architecture features of sleep that can predict metabolic dysfunction?

2. How can dynamic changes in sleep, breathing, oxygenation, vascular stiffness, and heart rate be modeled to provide insights into autonomic dysfunction and other pathways linking sleep disorders to metabolic dysfunction?

3. By linking with data within dbGaP or TOPMed, what are the metabolomic pathways that may explain associations between sleep and circadian disorders with metabolic dysfunction? Are there sex, race/ethnicity and other differences in these associations?

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZ0udOyopzIuHNKWQsmcU5aw68oSEfXdGh_u

Date/Time: Friday, October 23, 2023, 11 am - 12 pm PT

A Single Cell Atlas of Human and Mouse White Adipose Tissue

Thu, 07 Sep 2023 12:02:35 -0700

*Watch recorded webinar here: https://youtu.be/6m-ZQ2NhU6A

*Webinar slides: https://www.slideshare.net/dkNET/dknet-webinar-a-single-cell-atlas-11172023-of-human-and-mouse-white-adipose-tissue

Join dkNET Webinar on Friday, November 17, 2023, 11 am - 12 pm PT

Presenter: Margo Emont, PhD. Instructor, Beth Israel Deaconess Medical Center/Harvard Medical School

Abstract

White adipose tissue, once regarded as morphologically and functionally bland, is now recognized to be dynamic, plastic and heterogenous, and is involved in a wide array of biological processes including energy homeostasis, glucose and lipid handling, blood pressure control and host defense. High-fat feeding and other metabolic stressors cause marked changes in adipose morphology, physiology and cellular composition, and alterations in adiposity are associated with insulin resistance, dyslipidemia and type 2 diabetes. Here we provide detailed cellular atlases of human and mouse subcutaneous and visceral white fat at single-cell resolution across a range of body weight. We identify subpopulations of adipocytes, adipose stem and progenitor cells, vascular and immune cells and demonstrate commonalities and differences across species and dietary conditions. We link specific cell types to increased risk of metabolic disease and provide an initial blueprint for a comprehensive set of interactions between individual cell types in the adipose niche in leanness and obesity. These data comprise an extensive resource for the exploration of genes, traits and cell types in the function of white adipose tissue across species, depots and nutritional conditions.

The top 3 key questions that this resource can answer:

1. How specific is my gene of interest to a particular cell type in adipose tissue?

2. Is the gene/pathway that I am studying in mouse adipose tissue also present in human adipose tissue (and is it regulated similarly in low vs high body weight)?

3. What are the changes in gene expression in a specific cell type at low vs high body weight?

Resource link: https://singlecell.broadinstitute.org/single_cell/study/SCP1376/a-single-cell-atlas-of-human-and-mouse-white-adipose-tissue#study-summary

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZIvduGvpjstGdQhMFKNAbQVPgSCa-q7SY2n

Date/Time: Friday, November 17, 2023, 11 am - 12 pm PT

An Encyclopedia of the Adipose Tissue Secretome to Identify Mediators of Health and Disease

Thu, 07 Sep 2023 12:09:57 -0700

*Watch recorded webinar here: https://youtu.be/mVpWPxzmJXM

*Webinar slides:
https://www.slideshare.net/slideshows/dknet-webinar-an-encyclopedia-of-adipose-tissue-secretome-to-identify-mediators-of-health-disease-02092024/266330575

Join dkNET Webinar on Friday, February 9, 2024, 11 am - 12 pm PT

Presenter: Paul Cohen, MD, PhD, Albert Resnick, M.D. Associate Professor, Rockefeller University

Abstract

White and brown adipocytes not only play a central role in energy storage and combustion but are also dynamic secretory cells that secrete signaling molecules linking levels of energy stores to vital physiological systems. Disruption of the signaling properties of adipocytes, as occurs in obesity, contributes to insulin resistance, type 2 diabetes, and other metabolic disorders. Fat cells have been estimated to secrete over 1,000 polypeptides and microproteins and an even larger number of small molecule metabolites. The great majority of the adipocyte secretome has not been defined or characterized. A major obstacle has been the lack of suitable technologies to quantitatively identify circulating proteins and metabolites, determine their cellular origin, and elucidate their function. Building on key innovations in chemical biology and mass spectrometry, our team is generating an encyclopedia of the white and brown adipocyte secretome in mouse models and humans. Our work has the potential to identify new secreted mediators with roles in obesity, type 2 diabetes, and metabolic diseases, provide a crucial resource for researchers and clinicians, and lead to new biomarkers and therapies.

The top 3 key questions that this resource can answer:
1. What techniques can be used to characterize the secretome of a cell type in vitro and in vivo?
2. What is the full complement of proteins and metabolites secreted by different kinds of adipocytes?
3. How should one prioritize uncharacterized secreted mediators for functional study?

Resource link: https://secrepedia.org/

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZ0lc-2qpjMsHtQCPxxO9ryWqUdl3mR2aKDZ

Date/Time: Friday, February 9, 2024, 11 am - 12 pm PT

The Collaborative Microbial Metabolite Center - Democratizing Mechanistic and Functional Interpretation of Microbial Metabolites

Wed, 06 Dec 2023 15:43:28 -0800

*Watch recorded webinar here: https://youtu.be/0h5tHrHnkME

*Webinar slides: https://www.slideshare.net/slideshows/dknet-webinar-the-collaborative-microbial-metabolite-center-democratizing-mechanistic-and-functional-interpretation-of-microbial-metabolites-02232024/266506264

Join dkNET Webinar on Friday, February 23, 2024, 11 am - 12 pm PT

Presenter: Pieter Dorrestein, PhD, Professor, Skaggs, School of Pharmacy and Pharmaceutical Sciences, Department of Pharmacology and Pediatrics, University of California San Diego

Abstract

In the analysis of organs, volatilome, or biofluids, the microbiome influences 15-70% of detectable mass spectrometry molecules. Typically, only 10% of human untargeted metabolomics data can be assigned a molecular structure, with merely 1-2% traceable to microbial origins. Human microbiomes contribute metabolites through the microbial metabolism of host-derived substances, digestion of food and beverage molecules, and de novo assembly using proteins encoded by genetic elements. Despite the significance of microbiome-derived metabolites to human health, there is no centralized knowledge base for community access. To address this, the "Collaborative Microbial Metabolite Center" (CMMC) leverages expertise in mass spectrometry, microbiome innovation, and the GNPS ecosystem to built a knowledgebase. It aims to create a user-accessible microbiome resource, enrich bioactivity knowledge, and facilitate data deposition. The CMMC includes the construction of a knowledge base, MicrobeMASST tool, and health phenotype enrichment workflows, the construction and use will be discussed in this presentation. The use of this ecosystem will be exemplified by the discovery of 20,000 bile acids, many of which were shown to be of microbial origin and linked to diet and IBD.

The top 3 key questions that this resource can answer:

How can we leverage the 1000’s of public metabolomics studies to discover microbial metabolites and their organ distributions as well as their phenotypic, including health, associations?
If one has an unknown molecule, how can one assess what microbes make a molecule without known structure?
How can one contribute to the expansion of the knowledgebase on microbial metabolites?

Resource link: https://cmmc.gnps2.org/

Dial-in Information:
https://uchealth.zoom.us/meeting/register/tZMlce-hrDIoHdPr3NDYUybAGiTzkgfoG1GS

Date/Time: Friday, February 23, 2024, 11 am - 12 pm PT

The Multi-omic Response to Exercise Training Across Rat Tissues: Data Dissemination Through the MoTrPAC Data Hub

Wed, 06 Dec 2023 15:46:42 -0800

*Watch recorded webinar here: https://youtu.be/U7BXtIQbT4c

*Webinar slides: https://www.slideshare.net/slideshows/dknet-webinar-the-multiomic-response-to-exercise-training-across-rat-tissues-data-dissemination-through-the-motrpac-data-hub-03082024/266776034

Join dkNET Webinar on Friday, March 8, 2024, 11 am - 12 pm PT

Presenter: Malene Lindholm, PhD, Instructor, Department of Medicine, Stanford University

Abstract

The Molecular Transducers of Physical Activity Consortium (MoTrPAC) aims to map the molecular responses to exercise and training to elucidate how exercise improves health and prevents disease. The first MoTrPAC data provides an extensive temporal map of the dynamic multi-omic response to endurance training across multiple rat tissues. All results can be viewed, interrogated, and downloaded in a user-friendly, publicly accessible data portal (https://motrpac-data.org). The MoTrPAC data compendium includes transcriptomics, proteomics, metabolomics, phosphoproteomics, acetylproteomics, ubiquitylproteomics, DNA methylation, chromatin accessibility, and multiplexed immunoassay data. This compilation constitutes of 211 datasets across 19 tissues, 25 molecular assays, and 4 training time points in adult male and female rats. Over 35,000 analytes were found to be differentially regulated in response to endurance training, with many displaying sexual dimorphism. We observed a male-specific recruitment of immune cells to adipose tissues and an anticorrelated transcriptional response in the adrenal gland related to the stress response. Temporal multi-omic and multi-tissue integration demonstrated similar temporal responses in the heart and skeletal muscle, reflecting a concerted adaptation of mitochondrial biogenesis and metabolism. Integrative multi-omic network analysis revealed connections between the heat shock-mediated stress response and mitochondrial biogenesis. Training increased phospholipids and decreased triacylglycerols in the liver, and there were extensive changes to mitochondrial protein acetylation. Many changes were relevant for human health conditions, such as non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular wellness, and tissue damage and repair. Altogether, this MoTrPAC resource provides an unprecedented view of the effects of exercise across an organism, revealing mechanistic details of how exercise impacts mammalian health. The MoTrPAC data hub is the primary online resource to disseminate this large-scale multi-omics data.

The top 3 questions that the MoTrPAC resource can answer:

1. What is the multi-omic response to endurance exercise across different tissues?

2. What are the top signaling pathways affected in response to exercise and do they differ between males and females?

3. How can the MoTrPAC data hub be utilized to interrogate all the MoTrPAC findings?

Dial-in Information:
https://uchealth.zoom.us/meeting/register/tZUqdO2qpzMoH93Mb0_-7USN4EW-g0zJxvKV

Date/Time: Friday, March 8, 2024, 11 am - 12 pm PT

Tabula Sapiens

Thu, 07 Dec 2023 17:11:31 -0800

*Watch recorded webinar here: https://youtu.be/57m6NK7VVXo

*Webinar slides:
https://www.slideshare.net/slideshow/dknet-webinar-tabula-sapiens-03222024/267201639

Join dkNET Webinar on Friday, March 22, 2024, 11 am - 12 pm PT

Presenter: Angela Oliveira Pisco, PhD

Abstract

Although the genome is often called the blueprint of an organism, it is perhaps more accurate to describe it as a parts list composed of the various genes that may or may not be used in the different cell types of a multicellular organism. While nearly every cell in the body has essentially the same genome, each cell type makes different use of that genome and expresses a subset of all possible genes. This has motivated efforts to characterize the molecular composition of various cell types within humans and multiple model organisms, both by transcriptional and proteomic approaches. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression. One caveat to current approaches to make cell atlases is that individual organs are often collected at different locations, collected from different donors, and processed using different protocols. Controlled comparisons of cell types between different tissues and organs are especially difficult when donors differ in genetic background, age, environmental exposure, and epigenetic effects. To address this, we developed an approach to analyzing large numbers of organs from the same individual. We collected multiple tissues from individual human donors and performed coordinated single-cell transcriptome analyses on live cells. The donors come from a range of ethnicities, are balanced by gender, have a mean age of 51 years, and have a variety of medical backgrounds. Tissue experts used a defined cell ontology terminology to annotate cell types consistently across the different tissues, leading to a total of 475 distinct cell types with reference transcriptome profiles. The Tabula Sapiens also provided an opportunity to densely and directly sample the human microbiome throughout the gastrointestinal tract. The Tabula Sapiens has revealed discoveries relating to shared behavior and subtle, organ-specific differences across cell types. We found T cell clones shared between organs and characterized organ-dependent hypermutation rates among B cells. Endothelial cells and macrophages are shared across tissues, often showing subtle but clear differences in gene expression. We found an unexpectedly large and diverse amount of cell type–specific RNA splice variant usage and discovered and validated many previously undefined splices. The intestinal microbiome was revealed to have nonuniform species distributions down to the 3-inch (7.62-cm) length scale. These are but a few examples of how the Tabula Sapiens represents a broadly useful reference to deeply understand and explore human biology at cellular resolution.

The top 4 questions that the Tabula Sapiens can answer:

How similar is the gene expression of the organs that have been profiled across different donors?
What is the cell type composition of the organs in the atlas?
Are the endothelial cells found in multiple organs of a single donor identical?
What are the marker genes for the tissue cell types?

Resource link: https://tabula-sapiens-portal.ds.czbiohub.org

Dial-in Information:
https://uchealth.zoom.us/meeting/register/tZ0ucOuspzkvH9GN3vaUg-hHHqRiklQim294

Date/Time: Friday, March 22, 2024, 11 am - 12 pm PT

Unlocking the Power of FAIR Data Sharing with ImmPort

Wed, 20 Dec 2023 14:05:58 -0800

*Watch recorded webinar here: https://youtu.be/Yhr-wDDQQNs

*Webinar slides: https://www.slideshare.net/slideshow/dknet-webinar-unlocking-the-power-of-fair-data-sharing-with-immport-04122024/267710124

Join dkNET Webinar on Friday, April 12, 2024, 11 am - 12 pm PT

Presenter: Sanchita Bhattacharya, ImmPort Science Program Lead, Bakar Computational Health Sciences Institute UCSF

Abstract

The Immunology Database and Analysis Portal (ImmPort, https://www.immport.org/home) is a domain-specific data repository for immunology-related data which is funded by the National Institutes of Health, National Institute of Allergy and Infectious Diseases, and Division of Allergy, Immunology, and Transplantation. ImmPort has been making scientific data Findable, Accessible, Interoperable, and Reusable (FAIR) for over 20 years. ImmPort data sets encompass over 7 million experimental results across 160 diseases and conditions, including data related to diabetes, kidney and liver transplantation, celiac disease, and many more conditions. In this webinar, participants will learn about data management and sharing through ImmPort, as well as finding and leveraging data sets of interest for research.

The top 3 key questions that the ImmPort can answer:

1. How can researchers share data through ImmPort to comply with the NIH Data Management and Sharing policy?

2. How does ImmPort support FAIR data and why is this powerful for research?

3. What scientific data does ImmPort house that would be of interest to NIDDK researchers?

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZ0qceGhrj8sHNMJNeG4RqyxaUwrIpdlt5rt

Date/Time: Friday, April 12, 2024, 11 am - 12 pm PT

Texera: A Scalable Cloud Computing Platform for Sharing Data and Workflow-Based Analyses

Thu, 07 Dec 2023 17:13:18 -0800

*Watch recorded webinar here: https://youtu.be/B81iMFS5fPc

*Webinar slides: https://www.slideshare.net/slideshow/dknet-webinar-texera-a-scalable-cloud-computing-platform-for-sharing-data-and-workflowbased-analyses-04262024/267710991

Join dkNET Webinar on Friday, April 26, 2024, 11 am - 12 pm PT

Presenter: Chen Li, PhD. Professor, Department of Computer Science, University of California Irvine

Abstract

Many data analytics projects have collaborators with complementary backgrounds, including biologists, bioinformaticians, computer scientists, and AI/ML experts. Many of them have limited experience to code, set up a computing infrastructure, and use MLmodels. Existing tools and services, such as email attachments, GitHub, and Google Drive are inefficient for sharing data and analyses. In this talk, we present an open source system called Texera that provides a cloud computing platform for collaborators to share data and analyses as workflows. After seven years of development, the system has a rich set of powerful features, such as shared editing, shared execution, version control, commenting, debugging, user-defined functions in multiple languages (e.g., Python, R, Java), and support of state-of-the-art AI/ML techniques. Its backend parallel engine enables scalable computation on large data sets using computing clusters. We will show a demo of the system, and present our vision supported by a recent NIH award, dkNET(NIDDK Information Network, https://dknet.org), to serve the diabetes, endocrinology, and metabolic diseases research communities through the FAIR sharing of data and knowledge.

Resource link: https://github.com/Texera/texera

Dial-in Information:
https://uchealth.zoom.us/meeting/register/tZMrcuuvrTgsHdaSU_sHRUiygD5_l5kOhbfq

Date/Time: Friday, April 26, 2024, 11 am - 12 pm PT

dkNET Office Hours: NIH Data Management and Sharing Mandates

Thu, 11 Apr 2024 17:13:28 -0700

*Watch recording here: https://youtu.be/ohbz5uwPez0

*Slides: https://www.slideshare.net/slideshow/dknet-office-hours-nih-data-management-and-sharing-mandate-05-03-2024/267770619

Presenter: Jeffrey Grethe, PhD, Principal Investigator of NIDDK Information Network (dkNET), Center for Research in Biological Systems, University of California San Diego

For all proposals submitted on/after January 25 2023, NIH requires the sharing of data from all NIH funded studies. Do you have appropriate data management practices and sharing plans in place to meet these requirements? Have questions or need some help? Join the dkNET office hours to learn about NIH’s policy (NOT-OD-21-013) and resources that could help.

Date/Time: Friday, May 3, 2024, 11 am - 12 pm PT (2 pm - 3 pm ET)

The 4DN Data Portal - Data, Resources and Tools to Help Elucidate Nuclear Organization in Space and Time

Wed, 20 Dec 2023 14:08:48 -0800

*Watch recorded webinar here: https://youtu.be/HCZOSd20wwY

*Webinar slides: https://www.slideshare.net/slideshow/dknet-webinar-the-4dn-data-portal-data-resources-and-tools-to-help-elucidate-nuclear-organization-in-space-and-time-05-10-2024/268548868

Join dkNET Webinar on Friday, May 10, 2024, 11 am - 12 pm PT

Presenter: Andrew Schroeder, PhD. Project Manager & Senior Data Curator, 4D Nucleome Data Coordination and Integration Center (4DN-DCIC), Park Lab, Department of Biomedical Informatics, Harvard Medical School

Abstract

The Common Fund 4D Nucleome program, currently in its 9th year, is a consortium of researchers that aims to understand the principles behind the three-dimensional organization of the nucleus and how this organization can change over time to affect a variety of cellular processes. The 4DN Data Portal (data.4dnucleome.org) is an expanding resource hosting data generated by the 4DN Network and other reference nucleomics data sets. The portal provides tools for search, exploration, visualization, and download. An overview of the data portal, highlighting available data, how it can be found, visualized and used for analyses will be presented.

The top 3 key questions that the 4DN data portal can answer:

1. Are there significant sites of long-range chromatin contacts near my gene or region of interest?

2. What omics datasets are available for my tissue of interest?

3. Are there imaging datasets available that are relevant to my tissue of interest?

Dial-in Information:
https://uchealth.zoom.us/meeting/register/tZIodu-qpzIqEtReuoLvGNCfMoxa4PfsxsXb

Date/Time: Friday, May 10, 2024, 11 am - 12 pm PT

Single Cell Multi-Omics Analysis of Beta Cell Heterogeneity and Differentiation

Wed, 03 Jan 2024 14:11:30 -0800

Join dkNET Webinar on Friday, May 17, 2024, 11 am - 12 pm PT

Presenter: Yan Li, PhD, Associate Professor, Department of Genetics and Genomics, Case Western Reserve University

Abstract

In human tissues, not only different cell types are present in a tissue, but the same cell type from the same person may also exist in different states depending on various factors such as environment, age, or disease state. The concept of cellular heterogeneity has been actively pursued in some diseases (e.g., cancer stem cell). However, the presence and disease relevance of cellular heterogeneity in non-proliferating cell types, such as pancreatic beta cells are not well studied. We are using single cell RNA-seq, single cell ATAC-seq, Hi-C, and CRISPR technologies to investigate this interesting question. Another interest in the lab is to explore pluripotent stem cell (PSC) as a model for diabetes research and therapy. We performed the first single cell RNA-seq study to map the entire differentiation process from human ESC to the final stage, which include both endocrine and non-endocrine cell populations. This study shed light on the strategies to improve beta-cell differentiation for therapeutics. It also makes another conceptual contribution to the diabetes field that many DNA variants may cause disease risk during the development.

The top 3 key questions that the Beta Cell Hub can answer:

1. What are the cell type specific gene expression patterns within human islets?

2. What are the dynamic gene expression patterns during bets cell differentiation from embryonic stem cell?

3. scRNA, scATAC and HiC omics datasets generated from human islets relevant to Type 2 Diabetes.

Resource link: Beta Cell Hub https://hiview.case.edu/public/BetaCellHub/differentiation.php

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZUsc-uvrDkoHt2I3YdD8RAfyws35dqZC7rQ

Date/Time: Friday, May 17, 2024, 11 am - 12 pm PT

Introduction to AI-READI, Studying Salutogenesis in T2DM

Mon, 09 Sep 2024 15:31:10 -0700

Join dkNET Webinar on Friday, October 11, 2024, 11 am - 12 pm PT

Presenters:

Cecilia Lee, MD, MS. Professor, Klorfine Family Endowed Chair of Ophthalmology, University of Washington

Bhavesh Patel, PhD. Research Professor, California Medical Innovations Institute

Sally L. Baxter, MD, MSc. Associate Professor of Ophthalmology and Biomedical Informatics, University of California San Diego

Abstract

The AI-READI (Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights, https://aireadi.org) project, funded by the NIH Common Fund’s Bridge2AI Program, aims to develop a multimodal dataset specifically designed to be AI-ready for the study of salutogenesis in Type 2 Diabetes Mellitus (T2DM). Despite advancements in diabetes care, limited knowledge exists on how individuals with T2DM may revert to health. AI-READI team is building this dataset from a diverse cohort of 4,000 participants, ensuring it is structured for immediate use in machine learning algorithm training and analysis. The project emphasizes ethical and equitable data collection, adherence to FAIR (Findable, Accessible, Interoperable, Reusable) data principles, and establishing best practices for data sharing and management. By focusing on AI-readiness, the dataset will enable rapid application of machine learning to uncover novel insights into effective treatment strategies.

This presentation will introduce the AI-READI project, present the dataset, demonstrate how to request the datasets and explore potential research questions that can be addressed using machine learning, such as identifying predictors of health improvement in T2DM, understanding disease progression, and investigating the impact of various risk factors.

The top 3 key questions that Bridge2AI AI-READI datasets can answer:

How can we better understand Type 2 Diabetes (T2DM) heterogeneity?
What are the connections between multi-organ function in T2DM, and how are the kidney, heart, eye, brain interlinked?
How do interactions between environmental factors (e.g. air pollution) drive outcomes in T2DM?

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZEsdOmqqDgiEtes2c8OVfCoFPZlZ2vxi6Hx

Date/Time: Friday, October 11, 2024, 11 am - 12 pm PT

The ADI IsletCore and HumanIslets.com: Improving Accessibility, Integration and Usability of Human Research Islet Data

Mon, 09 Sep 2024 15:43:01 -0700

Join dkNET Webinar on Friday, October 25, 2024, 11 am - 12 pm PT

Presenter: Patrick MacDonald, PhD. Professor, Canada Research Chair in Islet Biology, University of Alberta, Edmonton, Canada

Abstract

The Alberta Diabetes Institute (ADI) IsletCore is one of the world's largest centers dedicated to isolating, distributing, and biobanking insulin-producing pancreatic islets from organ donors, exclusively for research. Serving research groups and institutions globally, ADI IsletCore now distributes banked and fresh pancreas, intestine, adipose, lymph nodes and spleen. Leveraging a network of collaborators to collecting islet phenotyping data, the HumanIslets.com Consortium was established to enhance the quality, accessibility, usability, and integration of comprehensive molecular and physiological phenotyping datasets from human islets, which are critical yet limited resources in diabetes research. HumanIslets.com, an open resource, currently includes data on 547 human islet donors, offering researchers access to linked datasets describing molecular omics profiles, islet functions, and donor phenotypes, and enabling statistical, visual, and functional analyses at donor, islet, or single-cell levels while considering potential confounders. This platform, in conjunction with an active biobanking program, provides a growing and adaptable set of resources and tools to support the global diabetes and islet research community.

The top 4 key questions that HumanIslets can answer:

1. Which pancreatic islet transcripts and proteins correlate with organ donor phenotypes such as age, sex, BMI or diabetes status?

2. What are some novel islet cell-type specific gene markers?

3. Does my expression of my gene/protein of interest correlate with any islet functional measures, such as insulin secretion or electrical activity?

4. How can I correct for technical co-variates in islet data analysis?

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZEqcumurTsvGNVlP05QqYaEhA37CSrOU7rL

Date/Time: Friday, October 25, 2024, 11 am - 12 pm PT

Mapping the human landscape of metabolite-mediated communication signals between single cells

Mon, 09 Sep 2024 15:57:04 -0700

Join dkNET Webinar on Friday, November 8, 2024, 11 am - 12 pm PT

Presenter: Kaifu Chen, PhD. Associate Professor, Department of Pediatrics, Harvard Medical School

Abstract

Cell-cell communication (CCC) is crucial for cellular function and tissue homeostasis. Due to fundamental differences in the underlying biological mechanisms, existing methods for protein-oriented CCC detection often miss metabolite-mediated CCC (mCCC). To fill this gap, we first developed MEBOCOST, an algorithm designed on top of scRNA-seq and metabolic flux balance analysis to detect mCCC among single cells. Comprehensive benchmarking analyses based on simulation, spatial, CRISPR screen, and clinical patient data demonstrated the robustness of MEBOCOST in detecting biologically significant mCCC events. We next applied MEBOCOST to landscape analysis and identified 210,215 significant mCCC events from 2 million single cells across 228 cell types of 13 tissues, 56 disease states, and 70 biological conditions. Notably, analysis in white adipose tissues unraveled macrophages as the predominant source of mCCC reprogramming in obese patients. Moreover, analysis in mice brown adipocyte tissue successfully recapitulated known and further uncovered new mCCC events, including a glutamine-mediated endothelial-to-adipocyte communication experimentally verified to regulate adipocyte differentiation. The MEBOCOST algorithm and our web portal, MCCP (http://cbp-kfc.org/mccp/), which allows researchers to explore the mCCC atlas easily, will be a valuable resource for metabolism research in diverse biological contexts and disease samples.

The top 3 key questions that MEBOCOST can answer

1. Is there a curated knowledge base for individual metabolites reported as might mediate signaling events between cells in a tissue?

2. Is there a web portal for biological researchers to easily check individual metabolite-mediated communication signals between cells in any published single cell RNA-sequencing (scRNA-seq) datasets?

3. Is there a computational tool to detect metabolite-mediated communication signals between cells in a new single cell RNA sequencing (scRNA-seq) dataset?

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZArde6orz8iGdKoCg5ysS4C_wm6ONaEouCt

Date/Time: Friday, November 8, 2024, 11 am - 12 pm PT

Expanding Human Islet Research via the Integrated Islet Distribution Program (IIDP) Research Data Repository

Mon, 09 Sep 2024 16:08:52 -0700

Join dkNET Webinar on Friday, November 22, 2024, 11 am - 12 pm PT

Presenters:
Joyce Niland, PhD, Professor & Endowed Chair, Department of Diabetes & Cancer Discovery Science, IIDP Principal Investigator, City of Hope, Duarte CA

James Cravens, MPH, IIDP Program Manager, City of Hope, Duarte, CA

Abstract

The Integrated Islet Distribution Program (IIDP - formerly the Islet Cell Resource Center Consortium from 2002-2009), is sponsored by the National Institute for Diabetes and Digestive and Kidney Diseases (NIDDK) to provide crucial availability of high-quality human islets, associated tissues, and rich complementary data for diabetes research. As one of the largest islet distribution programs worldwide, the IIDP has supplied more than 291 million islet equivalents to 594 unique research studies, supporting over 950 peer reviewed publications. The IIDP has improved protocol standardization and methodology, enhanced donor data through reporting genetic ancestry and T1D/T2D risk scores, expanded islet quality and phenotypic information, and provides ancillary tissues matched to the islets. In 2019, IIDP released the Research Data Repository (RDR) to provide direct access to integrated data across all islet isolations for any approved researcher, including donor information, United Network for Organ Sharing (UNOS) data, islet broadcast details, genetic risk scores and phenotyping results. Via the RDR, investigators can establish search criteria, select desired data points, save searches created, and download a resulting file for data exploration into the relationships between multiple donor factors and islet biology. Through its partnership with dkNET and the National Center for Biotechnology Information (NCBI), the IIDP was an early adopter of Research Resource Identifiers (RRIDs) for all IIDP records, to promote data transparency, rigor and analytic reproducibility, a facet that will become even more critical as new advances in artificial intelligence (AI) and machine learning (ML) make expanded, more complex analyses feasible.

The top 4 questions that the IIDP RDR can answer:
1. What characteristics and exposures of the pancreas donor influence a successful islet yield?
2. How does genetic risk for Type 2 diabetes vary with ancestry?
3. What factors impact the composition of the major islet cell types (alpha, beta gamma)?
4. You tell us! The IIDP is offering a new funding opportunity through February 2025 for data projects using our Research Data Repository (RDR): the Data Resource Trainee Scholar Award (DRTSA). Please visit https://iidp.coh.org/Resources-Offered/Research-Data-Repository to view the type of available IIDP data and begin to craft your own hypothesis. Additional information about the upcoming DRTSA will be available at https://iidp.coh.org/DRTSA_2025 in the near future, including eligibility criteria.

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZcqf-uhqDgoG9EETnvV2RFtRhCIO5fbwZJZ

Date/Time: Friday, November 22, 2024, 11 am - 12 pm PT

Inference and analysis of the external communication signals using exFINDER

Fri, 18 Oct 2024 19:15:31 -0700

Join dkNET Webinar on Friday, December 6, 2024, 11 am - 12 pm PT

Presenter: Changhan He, PhD. Visiting Assistant Professor, Department of Mathematics, University of California, Irvine

Abstract

Cells make decisions through their communication with other cells and receiving signals from their environment. Different computational tools have been developed to infer cell-cell communication through ligands and receptors using single-cell transcriptomics. However, existing methods only address signals sent by the cells measured within the dataset, leaving out signals received from external sources. This presentation introduces exFINDER, a computational approach designed to identify external signals received by cells in single-cell transcriptomics datasets and explores the analysis of related ligand-target signaling networks.

The top 3 key questions that exFINDER can answer:

1. What external signals influence cellular behavior within a single-cell transcriptomics dataset?

2. What are the most critical external signals and target genes of the inferred ligand-target signaling networks?

3. Which biological processes are associated with the ligand-target networks identified by exFINDER?

Resource link: https://github.com/ChanghanGitHub/exFINDER

Dial-in Information: https://uchealth.zoom.us/meeting/register/tZYtdeGhqTwuH9aO6fU73dtYyCbo6ALRcNQk

Date/Time: Friday, December 6, 2024, 11 am - 12 pm PT

Exploring Genomic Frontiers: Tools and Opportunities from Alzheimer’s Research

Thu, 09 Jan 2025 16:06:32 -0800

Join dkNET Webinar on Friday, February 14, 2025, 11 am - 12 pm PT

Presenter: Li-San Wang, PhD, Peter C. Nowell, M.D. Professor and Vice Chair for Research, Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine

Abstract

In this talk, we explore the genomic frontiers of Alzheimer's research via the revolutionary Alzheimer's Disease Sequencing Project (ADSP; https://adsp.niagads.org). ADSP integrates together various components that collectively unravel the intricate genetic landscape of Alzheimer's disease with the ultimate goal of advancing precision medicine for the millions affected globally by this devastating disease. With an eventual goal of sequencing and analyzing up to 150,000 complete genomes and associated clinical and functional data, ADSP has amassed an unprecedented wealth of genomic data from diverse populations, providing a comprehensive and holistic understanding of the genetic underpinnings of Alzheimer's disease.

This presentation highlights key components of the ADSP: (1) NIA Genetics of Alzheimer’s Disease Data Storage Site (NIAGADS; https://www.niagads.org): As a national data repository, NIAGADS supports Alzheimer’s Disease and Related Dementias (ADRD) research with high-quality data collections, genome-wide association study (GWAS) summary statistics, and richly annotated genomic knowledge bases. (2) Diversity Initiative: The ADSP places a paramount emphasis on diversity, ensuring the inclusion of a wide range of populations in its genomic dataset. (3) Phenotype Harmonization: Harmonizing phenotypic data across diverse cohorts is a critical aspect of the ADSP, facilitating meaningful comparisons and analyses. (4) Functional Genomics: Moving beyond genetic variations, the ADSP incorporates functional genomics to discern the biological mechanisms at play. (5) AI/ML Opportunities: the focus of this session, we will start by illuminating the wide range of opportunities the ADSP offers to AI/ML researchers.

By showcasing the tools and frameworks developed through the ADSP, this talk aims to inspire collaborations that extend beyond Alzheimer’s research, opening pathways for integrating genomic resources and expertise to advance research in diabetes, kidney disease, and other complex disorders.

The Top 5 Key Questions that Alzheimer's Disease Sequencing Project (ADSP) Resources Can Answer:

1. Where can researchers find key genes or pathways identified through the ADSP and how does the ADSP incorporate functional genomics with genetics to identify potential therapeutic targets?

2. What are the most promising AI/ML approaches currently being applied to ADSP data, and how might they be adapted to study diabetes or kidney diseases?

3. How does ADSP data support the development of predictive models for disease risk or progression?

4. Can tools developed for ADSP’s phenotype harmonization be applied to harmonize data for complex traits in diabetes or kidney disease?

5. How does the ADSP ensure diverse population representation in its datasets, and how can these efforts inform studies on genetic risks in underrepresented populations for diabetes or kidney disease?

Dial-in Information: https://uchealth.zoom.us/meeting/register/x-98M_efRPSx0yfUcCOZGA

Date/Time: Friday, February 14, 2025, 11 am - 12 pm PT

Metabolomics Workbench - A gateway to multiomics integration and disease biology

Thu, 09 Jan 2025 16:13:56 -0800

Join dkNET Webinar on Friday, February 28, 2025, 11 am - 12 pm PT

Presenter: Shankar Subramaniam, PhD, Distinguished Professor of Bioengineering, Bioinformatics and Systems Biology, Computer Science & Engineering, Cellular & Molecular Medicine, and Nano Engineering, University of California San Diego

Abstract

In less than a quarter century metabolomics has emerged as the sine qua non of omics-driven physiology and pathology. In this period our ability to detect and measure metabolites has transitioned from tens to hundreds of thousands in a single experiment. Such a technological revolution rivals that of other omics and is beginning to provide detailed insights into normal and diseased physiological functions of cells and tissues. Further new standards, data analytics and tools are making metabolomics indispensable in modern life science research. In this talk, I will introduce the Metabolomics Workbench and all the resources that are available to the research community in finding, accessing, and using the metabolomics data and its integration with other omics data. I will demonstrate how metabolomics has become an essential element in systems biology approaches to cell and tissue function.

Acknowledgements: Research presented in this talk is supported by grants from the National Institutes of Health, NIDDK, NHLBI, and OD.

The Top 4 Questions that Metabolomics Workbench Can Answer:

1. How can you access metabolomics data pertinent to human diseases, specifically of interest to diseases relevant to diabetes, digestive, and kidney diseases?

2. What are the most important metabolomic abnormalities that characterize fatty liver diseases?

3. Can I develop hypotheses on integrative omics mechanisms associated with liver cancer?

4. What tools are available for analyses of metabolomics data?

Dial-in Information: https://uchealth.zoom.us/meeting/register/SgG2fDHXRiOGpEZvGFXoGg

Date/Time: Friday, February 28, 2025, 11 am - 12 pm PT

Building knowledge graphs towards transparent biomedical AI

Thu, 09 Jan 2025 16:19:45 -0800

Join dkNET Webinar on Friday, March 14, 2025, 11 am - 12 pm PT

Presenter: Jie Liu, PhD, Associate Professor, Computational Medicine and Bioinformatics, Computer Science and Engineering, University of Michigan

Abstract

Knowledge graphs have recently emerged as a powerful data structure to organize biomedical knowledge with explicit representation of nodes and edges. The knowledge representation is in a machine-learning ready format and supports explainable AI models. In this talk, I will describe several knowledge graphs built in my lab or in a larger team, including Genomic Knowledge Base (GenomicKB, https://gkb.dcmb.med.umich.edu/), Genomic Literature Knowledge Base (GLKB, https://glkb.dcmb.med.umich.edu/), and PanKgraph, the knowledge graph within the PanKbase project (https://pankgraph.org/). I will focus on the scope of these knowledge graphs as well as how they support transparent and explainable AI, including using them in machine learning tasks, reducing hallucination of large language models (LLM), and helping experimental scientists explore their data for scientific discoveries.

The top 3 key questions that PanKgraph can answer:
1. Which genes are highly expressed in specific tissues (e.g., pancreatic islets) and have known functional roles in T1D?

Explanation: The scRNAseq expression data allows researchers to filter genes that show high tissue specificity in T1D-relevant organs.

2. How do SNPs influence quantitative trait loci (QTLs) of specific genes (e.g., CFTR) in specific tissues (e.g., pancreatic islets)?

Explanation: This question helps in understanding how genetic variations regulate gene expression in disease-relevant tissues, leveraging QTL and fine-mapped eQTL data.

3. What are the most studied genes related to critical T1D-related pathways (e.g., TCR signal processing)?

Explanation: By integrating pathway annotations and literature references, PanKGraph can highlight top genes studied in T1D-relevant signaling pathways, helping researchers prioritize targets for further investigation.

Dial-in Information: https://uchealth.zoom.us/meeting/register/WO9DXVa3R12eVaaKsM9Gkg

Date/Time: Friday, March 14, 2025, 11 am - 12 pm PT

Using knowledge graphs within biomedical knowledge portals: The Biomedical Translator and the Common Fund Knowledge Center

Thu, 09 Jan 2025 16:25:14 -0800

Join dkNET Webinar on Friday, April 11, 2025, 11 am - 12 pm PT

Presenter: Jason Flannick, PhD, Assistant Professor of Pediatrics, Harvard Medical School

Abstract

Knowledge graphs (KGs) have emerged as a powerful framework for integrating and exploring biomedical data. In this webinar I will discuss two resources that aim to integrate biomedical knowledge -- the NCATS Biomedical Translator (https://ui.transltr.io) and the Common Fund Data Ecosystem Knowledge Center (CFDE KC; cfdeknowledge.org) -- as case studies in the challenges and opportunities associated with using KGs to guide basic and translational discovery. I will begin by outlining the vision behind Translator, including the establishment of KG standards and semantic precision. I will provide practical examples of Translator use cases, drawing explicit comparisons with alternative approaches like large language models (LLMs) to highlight the strengths and weaknesses of each approach. I will then introduce the CFDE KC, which addresses similar challenges through a different complementary strategy. I will review the use of KGs within the CFDE KC and contrast them with other access mechanisms of data within the CFDE. I will conclude by synthesizing broader lessons learned from these two experiences, offering insights into the nuanced role KGs should play in future biomedical resource development. Ultimately, I suggest strategies for combining the strengths of KGs, LLMs, and statistical data integration to maximize translational impact and user adoption in biomedical research.

Top questions that these resources can answer:

NCATS Biomedical Translator (https://ui.transltr.io)
1. What drugs may treat conditions related to type 2 diabetes?
2. What chemicals may decrease the activity of SLC30A8?

Common Fund Data Ecosystem Knowledge Center (CFDE KC) (cfdeknowledge.org)
1. What exercise-related molecular mechanisms may underlie type 2 diabetes?
2. What variants are observed from sequencing pediatric patients in the Kids First project, and what are the phenotypes of patients in which they are observed?

Dial-in Information: https://uchealth.zoom.us/meeting/register/R1NDDLmAR62qkPqOVhd7vg

Date/Time: Friday, April 11, 2025, 11 am - 12 pm PT

Endothelial Cell Fates in Diabetic Kidney Disease—Lessons from the Kidney Precision Medicine Project

Wed, 15 Jan 2025 14:43:15 -0800

Join dkNET Webinar on Friday, April 25, 2025, 11 am - 12 pm PT

Presenter: Ricardo Melo Ferreira, PhD, Assistant Research Professor of Medicine, Indiana University School of Medicine

Abstract

The Kidney Precision Medicine Project (https://www.kpmp.org/) obtains altruistically donated biopsies of kidney patients with the purpose of understanding and finding new ways to treat chronic kidney disease (CKD) and acute kidney injury (AKI). A plethora of orthogonal molecular interrogation techniques are used with to uncover mechanisms underlying kidney disease. In this work we sought to understand the spatially-anchored regulation and transition of endothelial and mesangial cells from health to injury in DKD. From 74 human kidney samples, an integrated multi-omics approach was leveraged to identify cellular niches, cell-cell communication, cell injury trajectories, and regulatory transcription factor (TF) networks in glomerular capillary endothelial (EC-GC) and mesangial cells. We identified a cellular niche in diabetic glomeruli enriched in a proliferative endothelial cell subtype (prEC) and altered vascular smooth muscle cells (VSMCs). Cellular communication within this niche maintained pro-angiogenic signaling with loss of anti-angiogenic factors. We identified a TF network of MEF2C, MEF2A, and TRPS1 which regulated SEMA6A and PLXNA2, a receptor-ligand pair opposing angiogenesis. In silico knockout of the TF network accelerated the transition from healthy EC-GCs toward a degenerative (injury) endothelial phenotype, with concomitant disruption of EC-GC and prEC expression patterns. Glomeruli enriched in the prEC niche had histologic evidence of neovascularization. MEF2C activity was increased in diabetic glomeruli with nodular mesangial sclerosis. The gene regulatory network (GRN) of MEF2C was dysregulated in EC-GCs of patients with DKD, but sodium glucose transporter-2 inhibitor (SGLT2i) treatment reversed the MEF2C GRN effects of DKD. The MEF2C, MEF2A, and TRPS1 TF network carefully balances the fate of the EC-GC in DKD. When the TF network is “on” or over-expressed in DKD, EC-GCs may progress to a prEC state, while TF suppression leads to cell death. SGLT2i therapy may restore the balance of MEF2C activity.

The top 3 key questions that the KPMP resource can answer:
1. How the expression of a gene of interest changes across cell types and conditions?
2. How genes, proteins and metabolites are spatially distributed in different samples?
3. Which datasets I can download and use to complement my research?

Dial-in Information: https://uchealth.zoom.us/meeting/register/DxDgWA4LT_a45dHmRfSIhw

Date/Time: Friday, April 25, 2025, 11 am - 12 pm PT

An Introduction to the CAIRIBU Research Community

Tue, 14 Jan 2025 16:51:54 -0800

Join dkNET Webinar on Friday, May 9, 2025, 11 am - 12 pm PT

Presenter: Kristina Penniston, PhD. Distinguished Senior Scientist, Department of Urology, University of Wisconsin School of Medicine & Public Health

Abstract

CAIRIBU (Collaborating for the Advancement of Interdisciplinary Research in Benign Urology, https://cairibu.urology.wisc.edu/), funded by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), is a growing community of investigators and trainees committed to advancing benign urologic research through collaboration and shared resources. Originating from NIDDK-funded urology research centers, CAIRIBU has expanded to include participants from across the broader genitourinary research community. The CAIRIBU Interactions Core, established to provide scientific leadership within the nonmalignant urologic research space, fosters collaboration and outreach between CAIRIBU-affiliated investigators and those in the wider nonmalignant urologic research community, promotes interdisciplinary engagement, and supports a wide range of initiatives for researchers at all career stages. The overall mission of the CAIRIBU is to accelerate progress in understanding and treating benign urologic conditions. This webinar will provide an overview of CAIRIBU’s development, mission, current initiatives, resources, and the tools used to evaluate its impact.

The top 3 key questions that CAIRIBU resources can help answer:
1. Where can I find research resources such as antibodies, datasets, and software tools that are used by the benign urology research community?
2. Where can I find training materials and educational events, including webinars tailored for genitourinary researchers?
3. Where can I learn about funding opportunities and connect with potential collaborators in the field of non-malignant urology?

Resource link: CAIRIBU (Collaborating for the Advancement of Interdisciplinary Research in Benign Urology) https://cairibu.urology.wisc.edu/

Dial-in Information: https://uchealth.zoom.us/meeting/register/oV3suRN-S8yKSkLPiNp0yA

Date/Time: Friday, May 9, 2025, 11 am - 12 pm PT

Integrating RAG Chatbots into Bioinformatics Platforms

Wed, 15 Jan 2025 15:39:07 -0800

Join dkNET Webinar on Friday, May 16, 2025, 11 am - 12 pm PT

Abstract

The advent of large language models (LLMs) has revolutionized numerous fields with their impressive performance on a variety of natural language processing tasks. However, their application in biomedical research remains controversial due to their well-documented tendency to generate non-factual content. Given the foundational importance of accuracy, rigor, and reproducibility in the life sciences, such limitations pose significant barriers to adoption. Retrieval-Augmented Generation (RAG) frameworks offer a promising solution by grounding LLM responses with curated external knowledge sources, such as databases or document repositories, thereby constraining outputs to verifiable and citable information. Over the past two years, the Ma’ayan Lab has integrated RAG-based chatbots into several of its web-based bioinformatics platforms to enhance user interaction and provide accurate, context-specific assistance. These platforms include Playbook Workflow Builder, D2H2, GeneSetCart, Harmonizome, and the CFDE Workbench. In this presentation, I will provide an overview of each of these software platforms, highlighting how RAG-enhanced conversational interfaces were implemented to improve usability, facilitate knowledge discovery, and support data-driven discovery with examples related to diabetes.

Links to RAG chatbot features in software platforms developed by the Ma'ayan Lab:

Playbook Workflow Builder: https://playbook-workflow-builder.cloud/chat

Diabetes Data and Hypothesis Hub (D2H2): https://d2h2.maayanlab.cloud/hypotheses

GeneSetCart: https://genesetcart.cfde.cloud/gmt-cross

Harmonizome: https://maayanlab.cloud/Harmonizome/chatbot

CFDE Workbench: https://data.cfde.cloud/chat

The top 3 key questions that Diabetes Data and Hypothesis Hub(D2H2) resource can answer:

1. What transcription factors or small molecules are associated with my gene set in the context of diabetes?

2. Which diabetes-related studies show significant differential expression of my gene or gene set?

3. Which LINCS L1000 small molecules may reverser or mimic my up and down gene sets?

Dial-in Information: https://uchealth.zoom.us/meeting/register/91I6Og3PQwCIJe_iXBLjPQ

Date/Time: Friday, May 16, 2025, 11 am - 12 pm PT

Data-driven Biology with CFDE CONNECT Community of Resources

Tue, 16 Sep 2025 12:38:31 -0700

Join dkNET Webinar on Friday, November 21, 2025, 11 am - 12 pm PT

Presenter: Jake Y. Chen, Ph.D., FACMI, FAIMBE, FAMIA. Founding Director, Systems Pharmacology AI Research Center (SPARC); Triton Endowed Professor of Biomedical Informatics and Data Science; Adjunct Professor of Genetics, Computer Science, and Biomedical Engineering; Director, NIH Common Fund Data Ecosystems (CFDE) Integration and Coordination Center; The University of Alabama at Birmingham (UAB) Heersink School of Medicine

Abstract
The rapidly expanding landscape of systems biology presents numerous challenges in data integration, interpretation, and application. This webinar will explore how the NIH Common Fund Data Ecosystems (CFDE) and its CONNECT initiative serve as powerful tools for integrating diverse biological datasets and resources. Highlighting a variety of case studies performed at the AI.MED lab and the new Systems Pharmacology AI Research Center, the presentation will illustrate how CFDE resources can add to existing systems biology analysis and drive collaborative data-driven research across data sets and disciplines. By showcasing the potential of CFDE developed at various centers and labs across the CFDE community, this talk will provide attendees with a clear understanding of how they can leverage CFDE to enhance their own research with CFDE resources, fostering a data-driven ecosystem that accelerates discovery and innovation in biomedical science.

The top 3 key questions that the CFDE CONNECT can answer:

1. What are the top 10 genes relevant to a specific signaling pathway associated with a disease?

Utilize the CFDE Workbench to query gene sets and identify genes enriched in pathways related to diseases such as diabetes or cancer.
Employ the GeneSetCart tool to visualize and analyze gene sets across multiple Common Fund programs.

2. Are there omics datasets relevant to a specific disease?

Access the CFDE Data Resource Portal to discover datasets from various Common Fund programs, including genomics, transcriptomics, and metabolomics, pertinent to diseases like Alzheimer’s or kidney disease.

3. What tests are available for metabolic phenotyping in live mice?

Explore the CFDE Knowledge Center for curated analyses and tools related to metabolic phenotyping, including protocols and assays developed by programs like MoTrPAC.

Dial-in Information: https://uchealth.zoom.us/webinar/register/WN_qU5lCUWIRB6tVheiB9FZAQ

Date/Time: Friday, November 21, 2025, 11 am - 12 pm PT

Precision Diabetes: Understanding and Managing Glucose Dysregulation Using Deep Data and Remote Monitoring

Tue, 16 Sep 2025 12:42:50 -0700

Join dkNET Webinar on Friday, January 23, 2026, 11 am - 12 pm PT

Presenter: Michael Snyder, PhD, Stanford W. Ascherman Professor of Genetics; Director, Stanford Center for Genomics and Personalized Medicine, School of Medicine, Stanford University

Abstract

We use deep data and longitudinal measurements to monitor health which enable both early detection of disease and personal aging patterns (ageotypes). Remote monitoring using devices (e.g smartwatches) and microsampling enables frequent measurements and high resolution health tracking. Using (Continuous Glucose Monitoring (CGM) we demonstrate that individuals have personalized glucose responses to different carbohydrates. We further show that individuals have different subtypes of glucose dysregulation (e.g. muscle insulin resistance, beta cell defect, incretin effects) and that the response and mitigation of the postprandial response depends upon the dysregulation subtype. Association of lifestyle including food, activity and timing are correlated with glucose levels. This information enables personalized management of glucose dysregulation.

The top 3 key questions that these resources can answer:

1. Which types of measurements are most powereful and why?
2. What are the correlations between lifestyle and glucose levels?
3. Where does AI impact diabetes research?

Dial-in Information: https://uchealth.zoom.us/webinar/register/WN_TWuWrDOGRu2LSa2S-gdTgQ

Date/Time: Friday, January 23, 2026, 11 am - 12 pm PT

The Mammalian Adipose Tissue Knowledge Portal (MATKP): An Open Community Portal for Multi-Species Adipose Biology

Wed, 29 Oct 2025 17:49:36 -0700

Join dkNET Webinar on Friday, February 27, 2026, 11 am - 12 pm PT

Presenter: Julie Jurgens, PhD, The Broad Institute of MIT and Harvard

Abstract

The Mammalian Adipose Tissue Knowledge Portal (MATKP; https://matkp.org/) is an open-access, community-driven resource designed to accelerate discovery in mammalian adipose biology. MATKP integrates multi-species datasets across a range of adipose tissue depots and cell types, with an initial focus on human and mouse transcriptomic datasets. The portal currently provides bulk, pseudobulk, single-cell, and single-nucleus RNA sequencing data alongside interactive visualization tools, including a Single Cell Browser and Differential Gene Expression Browser. MATKP harmonizes datasets using standardized ontologies to enable cross-study interoperability and integrates genetic association results, functional annotations, and pathway enrichment analyses to connect molecular data with metabolic disease phenotypes. The platform also features a public Data Registry to support community data submission. This webinar will introduce the latest updates to the MATKP, including expanded mouse datasets, interactive features to explore the adipogenesis process, and inferred gene programs across adipose tissue cell types.

The top 3 key questions that the MATKP can answer:

1. How do specific genes influence adipose tissue biology and related metabolic phenotypes?

2. Which adipose tissue signatures, pathways, and gene programs are associated with metabolic disease phenotypes?

3. How do cell type-specific expression patterns contribute to disease mechanisms?

Dial-in Information: https://uchealth.zoom.us/webinar/register/WN_vSra3PQzQHCPMTeFJnul0w

Date/Time: Friday, February 27, 2026, 11 am - 12 pm PT

Towards a Virtual Cell - the Need for Actionable, Robust Perturbation Models

Thu, 02 Oct 2025 16:54:26 -0700

Join dkNET Webinar on Thursday, March 12, 2026, 11 am - 12 pm PT

Presenter: Prof. Fabian Theis, PhD, Institute of Computational Biology, Helmholtz Center Munich, Germany; TUM Schools of Computation, Information and Technology & Life Sciences, Technical University of Munich, Germany

Abstract

The vision of a virtual cell, i.e. a computational model that predicts cellular responses to genetic or pharmacological interventions, has been around for decades. Recent large-scale foundation models in single-cell genomics promise to accelerate this vision, yet they often remain descriptive and fall short of robust, intervention-ready prediction.

In this talk, I will highlight our contributions toward closing this gap: from atlas-scale representation learning and multimodal integration to foundation models such as NicheFormer, which learn transferable embeddings across tissues and contexts. Building on this, I will present CellFlow, our generative perturbation framework that models how drugs, gene edits, or differentiation protocols shift cellular states and enables in silico experimental design.

I will argue that achieving virtual cells requires not only scale, but carefully designed perturbation data and robust generative models that move from observing biology to shaping it.

Dial-in Information:
https://uchealth.zoom.us/webinar/register/WN_ah0G3w3YQwqoV15WQYKe7g

Date/Time: Thursday, March 12, 2026, 11 am - 12 pm PT

Proto-OKN (Prototype Open Knowledge Network) (Cancelled)

Tue, 16 Sep 2025 12:36:20 -0700

*Please note that this webinar is cancelled

Join dkNET Webinar on Friday, April 10, 2026, 11 am - 12 pm PT

Presenter: Aidong Zhang, PhD, Thomas M. Linville Professor, Computer Science, Biomedical Engineering, and Data Science, University of Virginia

Abstract

TBA

Dial-in Information: https://uchealth.zoom.us/webinar/register/WN_r4V96pukS_qWIahpG3Y2Fw

Date/Time: Friday, April 10, 2026, 11 am - 12 pm PT

A tour of HumanBase, an interactive AI platform for human biology

Tue, 17 Feb 2026 16:53:03 -0800

Join dkNET Webinar on Friday, April 24, 2026, 11 am - 12 pm PT

Presenter: Rachel Sealfon, PhD, Research Scientist, Flatiron Institute

Abstract

The wealth of existing heterogeneous biological datasets presents computational challenges for analysis as well as opportunities for learning valuable functional insights. Cutting-edge algorithms can facilitate analyses but are often not accessible to biomedical researchers or anyone lacking expensive computational resources. Here, we present HumanBase (humanbase.io), a data integration and analysis platform that applies AI/machine learning algorithms to learn tissue, cell type, and context-specific biological associations from massive genomic datasets. HumanBase integrates ~62,000 experiments, inferring the chromatin, expression, and post-transcriptional impact of genetic variants, predicting tissue-specific functional networks, and clustering genes into functional modules. HumanBase can guide experimental design and discovery by democratizing advanced, computation-intensive methods through intuitive web interfaces and distributed computing resources. This powerful, broadly accessible analytic platform allows users without coding knowledge to run high-performance end-to-end predictive pipelines and generate publication-quality visualizations, while the API provides programmatic access.

The top 3 key questions that HumanBase can answer:
1.What additional genes are functionally related to my gene(s) of interest in a specific tissue context?
2. What are the main function and process themes in my large list of genes?
3. What is the impact of a non-coding variant on regulatory activity, epigenetics, post-transcriptional regulation, and cell-type specific gene expression?

Dial-in Information: https://uchealth.zoom.us/webinar/register/WN_P-WOMsuARj-JIpznHJBjHQ

Date/Time: Friday, April 24, 2026, 11 am - 12 pm PT

BioMCP: An AI-Powered MCP Resource for Biomedical Research

Thu, 09 Apr 2026 18:58:58 -0700

Join dkNET Webinar on Friday, May 15, 2026, 11 am - 12 pm PT

Presenter: Yijia Xiao, PhD Student, Department of Computer Science, University of California Los Angeles, CA

Abstract

BioMCP is a web-accessible, Model Context Protocol(MCP)-enabled biomedical research assistant that helps users search scientific literature, genes, and proteins through natural language. It connects large language models with structured biomedical tools and public data sources, allowing researchers to ask domain questions in plain language while transparently seeing which tools were called and what results were returned. BioMCP currently supports literature, gene, and protein search through a browser-based interface and a deployable containerized service. We also plan to integrate additional capabilities such as deeper protein structure analysis, richer clinical interpretation, and broader biomedical knowledge sources.

The top 3 key questions that the Bio-MCP can answer:

1. What are the most relevant papers for a given gene, protein, or biomedical topic?
2. What genes or proteins are associated with a biomedical question, and what related evidence can I retrieve?
3. What literature is available to help me investigate a specific gene or protein of interest?

Dial-in Information: https://uchealth.zoom.us/webinar/register/WN_PqBXcwY7SXOELFw_8OpPWA

Date/Time: Friday, May 15, 2026, 11 am - 12 pm PT

NIDDK Information Network dkNET Webinar

dkNET 3.0 Introductory Webinar

Naughty cell lines and RRIDs

The Signaling Pathways Project: putting the R in FAIR data

dkNET 3.0 Introductory Webinar

Rigor and Reproducibility Support at dkNET 3.0

dkNET Webinar: Reproducibility - The Methods Behind the Data

dkNET Webinar: The Signaling Pathways Project, an integrated ‘omics knowledgebase for mammalian cellular signaling pathways

The Mouse Metabolic Phenotyping Centers: Services and Data

The NIH Mutant Mouse Resource and Research Centers (MMRRC) Consortium

The Type 2 Diabetes Knowledge Portal

Sharing Data and Other Resources from the Human Islet Research Network

Addgene, the Nonprofit Plasmid Repository

A New Approach to the Study of Energy Balance and Obesity using CalR (CalRapp.org)

dkNET Hypothesis Center - Signaling Pathways Project Live Demo

Creating and Sustaining a FAIR Biomedical Data Ecosystem

Illuminating the Druggable Genome with Pharos

FAIR Data Require Better Metadata: The Case for CEDAR

Vivli: A Global Clinical Trials Data Sharing Platform

FAIR Data & Software in the Research Life Cycle

nPOD nanotomy: Large-scale electron microscopy database for human type 1 diabetes

Population-based Approaches to Investigate Endocrine Communication

Temporal plasma metabolome and gut microbiome in an early-childhood study of type 1 diabetes

Multi-omics Data Integration for Phenotype Prediction of Type-1 Diabetes

Solving the Undiagnosed Diseases through Machine Learning

Appyters: Turning Jupyter Notebooks into data-driven web apps

dkNET Hypothesis Center Live Demo

GeneNetwork: Experimental Precision Medicine and Smart FAIR+ Data for Metabolomics and Diabetes Research

YCharOS: Antibody Characterization Through Open Science

The Microphysiology Systems Database (MPS-Db): A platform for aggregating, analyzing, sharing and computationally modeling in vitro data

PanoramaWeb: A Resource to Manage, Share, and Collaborate on Quantitative Mass Spectrometry Experiments

Pancreatlas™: mapping the human pancreas in health and disease

The Stimulating Peripheral Activity to Relieve Conditions (SPARC): Advancing Bioelectronic Medicine through Open Science

Uncovering novel mediators and mechanisms of leptin action

The Pediatric Obesity Metabolome and Microbiome Study (POMMS): Characterization of Pre- and Post-Intervention Microbiome and Metabolome in Adolescents with Obesity

Machine Learning to Analyze Pancreas Imaging in Diabetes

T Cell Antigen Discovery: Experimental and Computational Approaches

Integrative Artificial Intelligence Approach to Predict Type 1 Diabetes

Visualizing Organelle and Cell Longevity In Situ

The Human BioMolecular Atlas Program (HuBMAP)

Discovering and Evaluating Antibodies, Cell Lines, Software Tools, and Others

How to find and select a research resource such as an antibody or cell line

How to find Research Resource Identifiers (RRIDs) and proper citation of your resources

The Accelerating Medicines Partnership Common Metabolic Diseases Knowledge Portal: a resource for research into the genetic and genomic basis of diabetes and cardiometabolic disease

The mission and progress of the(sugar)science: helping scientists who study Type 1 diabetes connect

Attie Lab Diabetes Database (Rescheduled)

dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sharing Mandates"

FAIR Data Curation of Antibody/B-cell and T-cell Receptor Sequences in the AIRR Data Commons

Choosing Sample Sizes for Multilevel and Longitudinal Studies Analyzed with Linear Mixed Models

Postpartum Glucose Screening Among Homeless Women with Gestational Diabetes

dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sharing Mandates"

Estimating Relative Beta-Cell Function During Continuous Glucose Monitoring and Its Clinical Applications

Single Molecule Spatial Transcriptomics: interpreting the spatial expression of RNAs from FISH images

Defining the Spatiotemporal and Molecular Trajectory of Beige Adipogenesis

Leveraging Computational Strategies to Identify Type 1 Diabetes Risk and Clinical Trial Responder Status

Discover the Latest from dkNET - Biomed Resource Watch

dkNET Office Hours - "Are You Ready for 2023: New NIH Data Management and Sharing Mandates"

The National Sleep Research Resource (NSRR) - Opportunities for Large-Scale Sleep and Circadian Data to Promote Understanding of Metabolic Diseases

A Single Cell Atlas of Human and Mouse White Adipose Tissue

An Encyclopedia of the Adipose Tissue Secretome to Identify Mediators of Health and Disease

The Collaborative Microbial Metabolite Center - Democratizing Mechanistic and Functional Interpretation of Microbial Metabolites

The Multi-omic Response to Exercise Training Across Rat Tissues: Data Dissemination Through the MoTrPAC Data Hub

Tabula Sapiens

Unlocking the Power of FAIR Data Sharing with ImmPort

Texera: A Scalable Cloud Computing Platform for Sharing Data and Workflow-Based Analyses

dkNET Office Hours: NIH Data Management and Sharing Mandates

The 4DN Data Portal - Data, Resources and Tools to Help Elucidate Nuclear Organization in Space and Time

Single Cell Multi-Omics Analysis of Beta Cell Heterogeneity and Differentiation

Introduction to AI-READI, Studying Salutogenesis in T2DM

The ADI IsletCore and HumanIslets.com: Improving Accessibility, Integration and Usability of Human Research Islet Data

Mapping the human landscape of metabolite-mediated communication signals between single cells

Expanding Human Islet Research via the Integrated Islet Distribution Program (IIDP) Research Data Repository

Inference and analysis of the external communication signals using exFINDER

Exploring Genomic Frontiers: Tools and Opportunities from Alzheimer’s Research

Metabolomics Workbench - A gateway to multiomics integration and disease biology

Building knowledge graphs towards transparent biomedical AI

Using knowledge graphs within biomedical knowledge portals: The Biomedical Translator and the Common Fund Knowledge Center

Endothelial Cell Fates in Diabetic Kidney Disease—Lessons from the Kidney Precision Medicine Project

An Introduction to the CAIRIBU Research Community

Integrating RAG Chatbots into Bioinformatics Platforms