Tools   Select Another Resource Report Type

http://www.vetmed.wisc.edu/ms-phd/

The Comparative Biomedical Sciences Graduate Degree program provides exceptional graduate research training in core areas of animal and human health including genomics, immunology, molecular and cellular biology, physiology, infectious disease, neuroscience, pharmacology and toxicology, and oncology. Seventy-five faculty members in a diverse number of UW departments including Bacteriology, Biochemistry, Medical Microbiology and Immunology, Medicine, Oncology, Pathology, Radiology in addition to the 4 departments of the School of Veterinary Medicine are trainers in the program. These internationally recognized professors, as well as the integrative nature of our program, provide outstanding and unique research opportunities for our students. Because the University of Wisconsin is consistently ranked as one of the best 10 graduate institutions in the nation, the strength of our program is not only due to the superb research and teaching of our faculty but also due to the University as a whole. Approximately 55 students, most of whom are Ph.D. candidates, are currently enrolled in the program. Research strategies and academic curricula are tailored to the specific needs of each individual student. Graduates from our program are highly successful in the biotechnology industry and at top-ranked research institutions in the U.S. and abroad. The Comparative Biomedical Sciences Graduate Program offers a diverse number of research opportunities in multiple fields of study. A brief description of some of the major areas of research being performed by faculty affiliated with the Comparative Biomedical Sciences Graduate Program is provided below. Use the pull down menu above or click on the heading to find faculty members doing research in these areas. Sponsors: CBMS is supported by the University of Wisconsin

Proper citation: Comparative Biomedical Sciences Graduate Program (RRID:SCR_008304) Copy   

•   Source: SciCrunch Registry

http://www.alz.washington.edu/

A clinical research, neuropathological research and collaborative research database that uses data collected from 29 NIA-funded Alzheimer's Disease Centers (ADCs). The database consists of several datasets, and searches may be done on the entire database or on individual datasets. Any researcher, whether affiliated with an ADC or not, may request a data file for analysis or aggregate data tables. Requested aggregate data tables are produced and returned as soon as the queue allows (usually within 1-3 days depending on the complexity).

Proper citation: National Alzheimer's Coordinating Center (RRID:SCR_007327) Copy   

•   Source: SciCrunch Registry

http://www.sanger.ac.uk/Projects/D_rerio/zmp/

Create knockout alleles in protein coding genes in the zebrafish genome, using a combination of whole exome enrichment and Illumina next generation sequencing, with the aim to cover them all. Each allele created is analyzed for morphological differences and published on the ZMP site. Transcript counting is performed on alleles with a morphological phenotype. Alleles generated are archived and can be requested from this site through the Zebrafish International Resource Center (ZIRC). You may register to receive updates on genes of interest, or browse a complete list, or search by Ensembl ID, gene name or human and mouse orthologue.

Proper citation: ZMP (RRID:SCR_006161) Copy   

•   Source: SciCrunch Registry

https://www.vet.k-state.edu/research/docs/BRITE-application.pdf

The BRITE Veterinary Student Program provides DVM students interested in research with a subsidized, in-depth mentored research experience. The opportunity can be used to gain research experience, to obtain an MS, or to jump-start a DVM/PhD program. The BRITE veterinary student program is designed to expose DVM students to hypothesis-driven research activities, methodologies involved in design and execution of laboratory experiments and ethical issues pertinent to biomedical research, at a formative stage of their veterinary education. BRITE veterinary students are given a unique opportunity to utilize the rigorous didactic basic science training obtained during the first two years of the professional curriculum in pursuit of a research problem relevant to human and animal health. Sponsors: The program is funded by Kansas State University.

Proper citation: Basic Research Immersion Training Experience Veterinary Student Program (RRID:SCR_008305) Copy   

•   Source: SciCrunch Registry

http://www.treefam.org

A database of phylogenetic trees of animal genes. It aims at developing a curated resource that gives reliable information about ortholog and paralog assignments, and evolutionary history of various gene families. TreeFam defines a gene family as a group of genes that evolved after the speciation of single-metazoan animals. It also tries to include outgroup genes like yeast (S. cerevisiae and S. pombe) and plant (A. thaliana) to reveal these distant members.TreeFam is also an ortholog database. Unlike other pairwise alignment based ones, TreeFam infers orthologs by means of gene trees. It fits a gene tree into the universal species tree and finds historical duplications, speciations and losses events. TreeFam uses this information to evaluate tree building, guide manual curation, and infer complex ortholog and paralog relations.The basic elements of TreeFam are gene families that can be divided into two parts: TreeFam-A and TreeFam-B families. TreeFam-B families are automatically created. They might contain errors given complex phylogenies. TreeFam-A families are manually curated from TreeFam-B ones. Family names and node names are assigned at the same time. The ultimate goal of TreeFam is to present a curated resource for all the families. phylogenetic tree, animal, vertebrate, invertebrate, gene, ortholog, paralog, evolutionary history, gene families, single-metazoan animals, outgroup genes like yeast (S. cerevisiae and S. pombe), plant (A. thaliana), historical duplications, speciations, losses, Human, Genome, comparative genomics

Proper citation: Tree families database (RRID:SCR_013401) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org/phenotypes.shtml

Enables comparative phenotype analysis, searches for human disease models, and hypothesis generation by providing access to spontaneous, induced, and genetically engineered mutations and their strain-specific phenotypes.

Proper citation: Phenotypes and Mutant Alleles (RRID:SCR_017523) Copy   

•   Source: SciCrunch Registry

https://www.hmtvar.uniba.it

Manually curated database offering variability and pathogenicity information about mtDNA variants. Human mitochondrial variants data of healthy and diseased subjects.Data and text mining pipeline to annotate human mitochondrial variants with functional and clinical information.

Proper citation: HmtVar (RRID:SCR_017288) Copy   

•   Source: SciCrunch Registry

http://www.humphreyslab.com/SingleCell/

Software tool as analyzer for kidney single cell datasets. Allows users to query gene expression from mouse or human kidney and human kidney organoid single cell datasets. For details about datasets visit ReBuilding a Kidney website.

Proper citation: Kidney Interactive Transcriptomics (RRID:SCR_017209) Copy   

•   Source: SciCrunch Registry

https://brads.nichd.nih.gov/Home/

Access to data from the Division of Intramural Population Health Research (DIPHR) of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) from completed studies, including biospecimens and ancillary data.

Proper citation: Biospecimen Repository Access and Data Sharing (RRID:SCR_017383) Copy   

•   Source: SciCrunch Registry

http://10kimmunomes.org

Collection of reference datasets for human immunology, derived from control subjects in the NIAID ImmPort database . Available data include flow cytometry, CyTOF, multiplex ELISA, gene expression, HAI titers, clinical lab tests, HLA type, and others.

Proper citation: The 10000 Immunomes (RRID:SCR_016624) Copy   

•   Source: SciCrunch Registry

https://www.grnpedia.org/trrust/

TRUSST is reference database of human transcriptional regulatory interactions.TRRUST v2 is manually curated expanded reference database of human and mouse transcriptional regulatory interactions.

Proper citation: Transcriptional Regulatory Relationships Unrevealed by Sentence based Text mining database (RRID:SCR_022554) Copy   

•   Source: SciCrunch Registry

http://www.emcdda.europa.eu/eib

The EIB provides assessment tests for substance disorder related clinical instruments that are freely available. Details regarding copyright and/or possible use restrictions are specified for each instrument. Instruments are generally classed according to the intervention field they are designed to be used in (treatment, prevention, or harm reduction), though some instruments may be usable in more than one field.

Proper citation: Evaluation Instruments Bank (RRID:SCR_013246) Copy   

•   Source: SciCrunch Registry

http://www.loni.usc.edu/

Biomedical technology resource center specializing in novel approaches and tools for neuroimaging. It develops novel strategies to investigate brain structure and function in their full multidimensional complexity. There is a rapidly growing need for brain models comprehensive enough to represent brain structure and function as they change across time in large populations, in different disease states, across imaging modalities, across age and sex, and even across species. International networks of collaborators are provided with a diverse array of tools to create, analyze, visualize, and interact with models of the brain. A major focus of these collaborations is to develop four-dimensional brain models that track and analyze complex patterns of dynamically changing brain structure in development and disease, expanding investigations of brain structure-function relations to four dimensions.

Proper citation: Laboratory of Neuro Imaging (RRID:SCR_001922) Copy   

•   Source: SciCrunch Registry

http://www.cs.tau.ac.il/~shlomito/tissue-net/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. Network visualizations in which the expression and predicted flux data are projected over the global human network. These network visualizations are accessible through the supplemental website using the publicly available Cytoscape software (Cline, Smoot et al. 2007). Since many high degree nodes exist in the network, special layouts are required to produce network visualizations that are readily interpretable. To this end we produced network visualizations in which hub nodes are repeated multiple times and hence layouts with a small number of edge crossings can be generated. Contains entries for brain compartments and brain pathways.

Proper citation: Network-based Prediction of Human Tissue-specific Metabolism (RRID:SCR_007392) Copy   

•   Source: SciCrunch Registry

http://aws.amazon.com/1000genomes/

A dataset containing the full genomic sequence of 1,700 individuals, freely available for research use. The 1000 Genomes Project is an international research effort coordinated by a consortium of 75 companies and organizations to establish the most detailed catalogue of human genetic variation. The project has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals that researchers can now access on AWS for use in disease research free of charge. The dataset containing the full genomic sequence of 1,700 individuals is now available to all via Amazon S3. The data can be found at: http://s3.amazonaws.com/1000genomes The 1000 Genomes Project aims to include the genomes of more than 2,662 individuals from 26 populations around the world, and the NIH will continue to add the remaining genome samples to the data collection this year. Public Data Sets on AWS provide a centralized repository of public data hosted on Amazon Simple Storage Service (Amazon S3). The data can be seamlessly accessed from AWS services such Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic MapReduce (Amazon EMR), which provide organizations with the highly scalable compute resources needed to take advantage of these large data collections. AWS is storing the public data sets at no charge to the community. Researchers pay only for the additional AWS resources they need for further processing or analysis of the data. All 200 TB of the latest 1000 Genomes Project data is available in a publicly available Amazon S3 bucket. You can access the data via simple HTTP requests, or take advantage of the AWS SDKs in languages such as Ruby, Java, Python, .NET and PHP. Researchers can use the Amazon EC2 utility computing service to dive into this data without the usual capital investment required to work with data at this scale. AWS also provides a number of orchestration and automation services to help teams make their research available to others to remix and reuse. Making the data available via a bucket in Amazon S3 also means that customers can crunch the information using Hadoop via Amazon Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.

Proper citation: 1000 Genomes Project and AWS (RRID:SCR_008801) Copy   

•   Source: SciCrunch Registry

https://confluence.crbs.ucsd.edu/display/NIF/StemCellInfo

Data tables providing an overview of information about stem cells that have been derived from mice and humans. The tables summarize published research that characterizes cells that are capable of developing into cells of multiple germ layers (i.e., multipotent or pluripotent) or that can generate the differentiated cell types of another tissue (i.e., plasticity) such as a bone marrow cell becoming a neuronal cell. The tables do not include information about cells considered progenitor or precursor cells or those that can proliferate without the demonstrated ability to generate cell types of other tissues. The tables list the tissue from which the cells were derived, the types of cells that developed, the conditions under which differentiation occurred, the methods by which the cells were characterized, and the primary references for the information.

Proper citation: National Institutes of Health Stem Cell Tables (RRID:SCR_008359) Copy   

•   Source: SciCrunch Registry

http://harvard.eagle-i.net/i/0000012a-2518-fb6c-5617-794280000000

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 27, 2023. Core provides services: RT PCR service, Gene expression profiling service, Proteomics analysis service, Bioinformatics and Systems Biology analyses, Next Generation Sequencing Service, Affymetrix Human and Mouse Gene 2.0 ST Arrays and 2.1 ST Arrayplates. Core proteomics facility for the Dana-Farber/Harvard Cancer Center. Workflows and algorithms for analysis of next-generation sequencing data including RNA-Seq, ChIP-Seq, Epigenetics-Seq and DNA seq, Comprehensive workflow for analysis of Microbiome sequencing data, Integrated systems biology analysis of transcriptome, miRNA, epigenome, metabolomics and proteomics data. Pipelines: MALDI Tissue imaging and targeted quantitative proteomics.

Proper citation: Beth Israel Deaconess Medical Center Genomics Proteomics Bioinformatics and Systems Biology Center (RRID:SCR_009668) Copy   

•   Source: SciCrunch Registry

https://www.crukscotlandinstitute.ac.uk/advanced-technologies/molecular-technology.html

Core provides Next Generation Sequencing (NGS) services and Single Cell services predominantly focussing on single cell RNAseq. Processes samples for variety of cancer associated projects, in both mouse and human derived materials. Offers full end-to-end service, from initial study design and planning, through sample QC, full library preparation, sequencing and data return. Offers range of standard molecular tests covering, plasmid purifications, Sanger sequencing and mycoplasma screening.

Proper citation: Cancer Research UK Scotland Institute Molecular Technology Service Core Facility (RRID:SCR_027368) Copy   

•   Source: SciCrunch Registry

https://marmotgraph.org

Knowledge graph system developed for managing and organizing rich metadata objects, initially for the Human Brain Project (HBP) and now extended to be a more generic, domain-agnostic solution. It is associated with CSCS (Swiss National Supercomputing Centre) and aims to provide a comprehensive toolset and API for working with knowledge graphs.

Proper citation: MarmotGraph (RRID:SCR_027452) Copy   

•   Source: SciCrunch Registry

http://matrixdb.univ-lyon1.fr/

Freely available database focused on interactions established by extracellular proteins and polysaccharides, taking into account the multimeric nature of the extracellular proteins (e.g. collagens, laminins and thrombospondins are multimers). MatrixDB is an active member of the International Molecular Exchange (IMEx) consortium and has adopted the PSI-MI standards for annotating and exchanging interaction data. It includes interaction data extracted from the literature by manual curation, and offers access to relevant data involving extracellular proteins provided by the IMEx partner databases through the PSICQUIC webservice, as well as data from the Human Protein Reference Database. The database reports mammalian protein-protein and protein-carbohydrate interactions involving extracellular molecules. Interactions with lipids and cations are also reported. MatrixDB is focused on mammalian interactions, but aims to integrate interaction datasets of model organisms when available. MatrixDB provides direct links to databases recapitulating mutations in genes encoding extracellular proteins, to UniGene and to the Human Protein Atlas that shows expression and localization of proteins in a large variety of normal human tissues and cells. MatrixDB allows researchers to perform customized queries and to build tissue- and disease-specific interaction networks that can be visualized and analyzed with Cytoscape or Medusa. Statistics (2013): 2283 extracellular matrix interactions including 2095 protein-protein and 169 protein-glycosaminoglycan interactions.

Proper citation: MatrixDB (RRID:SCR_001727) Copy   

•   Source: SciCrunch Registry