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http://www.ibioseminars.org/

iBioSeminars offers: * Free, on-demand lectures: Many universities/colleges have limited access to high profile leaders in biological research. Our goal is to add 15-20 seminars per year, of similar quality to outstanding lectures that are currently in this library. Access, through web streaming or download, is completely free-of-charge. * Targeting a broad audience: iBioSeminars start with an extended introduction, making them accessible to non-specialists and students, and then progress to cover current research. Senior scientists and students can view and enjoy these lectures. * Education: iBioSeminars are being used by undergraduate and graduate teachers to augment their classroom material. We have now added an education component to this web site (including lecture notes, questions/answers and short video clips for teaching). * International communication: iBioSeminars have viewers in 115 countries and they are being internally promoted in several countries as an educational tool and scientific resource. * Goodwill: Lecturers generously donate their time to prepare these lectures. The project, largely funded by HHMI, is a grass roots efforts with time invested by several individuals at UCSF, HHMI and ASCB.

Proper citation: iBioSeminars (RRID:SCR_005848) Copy   

•   Source: SciCrunch Registry

https://neuroscienceblueprint.nih.gov/Resources-Tools/Blueprint-Resources-Tools-Library

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 22, 2023. National initiative to advance biomedical research through data sharing and online collaboration that provides data sharing infrastructure, software tools, strategies and advisory services. Groups may choose whether to share data internally or with external audiences. Hardware and data remain under control of individual user groups.

Proper citation: Biomedical Informatics Research Network (RRID:SCR_005163) Copy   

•   Source: SciCrunch Registry

http://people.biochem.umass.edu/fournierlab/3dmodmap/

Database of maps showing the sites of modified rRNA nucleotides. Access to the rRNA sequences, secondary structures both with modification sites indicated, 3D modification maps and the supporting tables of equivalent nucleotides for rRNA from model organisms including yeast, arabidopsis, e. coli and human is provided. This database complements the Yeast snoRNA Database at UMass-Amherst and relies on linking to some content from that database, as well as to others by colleagues in related fields. Therefore, please be very cognizant as to the source when citing information obtained herein. Locations of modified rRNA nucleotides within the 3D structure of the ribosome.

Proper citation: 3D Ribosomal Modification Maps Database (RRID:SCR_003097) Copy   

•   Source: SciCrunch Registry

http://www.mitomap.org/

Database of polymorphisms and mutations of the human mitochondrial DNA. It reports published and unpublished data on human mitochondrial DNA variation. All data is curated by hand. If you would like to submit published articles to be included in mitomap, please send them the citation and a pdf.

Proper citation: MITOMAP - A human mitochondrial genome database (RRID:SCR_002996) Copy   

•   Source: SciCrunch Registry

http://mistdb.com

Database which contains the signal transduction proteins for complete and draft bacterial and archaeal genomes. The MiST2 database identifies and catalogs the repertoire of signal transduction proteins in microbial genomes.

Proper citation: MiST - Microbial Signal Transduction database (RRID:SCR_003166) Copy   

•   Source: SciCrunch Registry

http://zebrafinch.brainarchitecture.org/

Atlas of high resolution Nissl stained digital images of the brain of the zebra finch, the mainstay of songbird research. The cytoarchitectural high resolution photographs and atlas presented here aim at facilitating electrode placement, connectional studies, and cytoarchitectonic analysis. This initial atlas is not in stereotaxic coordinate space. It is intended to complement the stereotaxic atlases of Akutegawa and Konishi, and that of Nixdorf and Bischof. (Akutagawa E. and Konishi M., stereotaxic atalas of the brain of zebra finch, unpublished. and Nixdorf-Bergweiler B. E. and Bischof H. J., A Stereotaxic Atlas of the Brain Of the Zebra Finch, Taeniopygia Guttata, http://www.ncbi.nlm.nih.gov.) The zebra finch has proven to be the most widely used model organism for the study of the neurological and behavioral development of birdsong. A unique strength of this research area is its integrative nature, encompassing field studies and ethologically grounded behavioral biology, as well as neurophysiological and molecular levels of analysis. The availability of dimensionally accurate and detailed atlases and photographs of the brain of male and female animals, as well as of the brain during development, can be expected to play an important role in this research program. Traditionally, atlases for the zebra finch brain have only been available in printed format, with the limitation of low image resolution of the cell stained sections. The advantages of a digital atlas over a traditional paper-based atlas are three-fold. * The digital atlas can be viewed at multiple resolutions. At low magnification, it provides an overview of brain sections and regions, while at higher magnification, it shows exquisite details of the cytoarchitectural structure. * It allows digital re-slicing of the brain. The original photographs of brain were taken in certain selected planes of section. However, the brains are seldom sliced in exactly the same plane in real experiments. Re-slicing provides a useful atlas in user-chosen planes, which are otherwise unavailable in the paper-based version. * It can be made available on the internet. High resolution histological datasets can be independently evaluated in light of new experimental anatomical, physiological and molecular studies.

Proper citation: Zebrafinch Brain Architecture Project (RRID:SCR_004277) Copy   

•   Source: SciCrunch Registry

http://avis.princeton.edu/pixie/index.php

bioPIXIE is a general system for discovery of biological networks through integration of diverse genome-wide functional data. This novel system for biological data integration and visualization, allows you to discover interaction networks and pathways in which your gene(s) (e.g. BNI1, YFL039C) of interest participate. The system is based on a Bayesian algorithm for identification of biological networks based on integrated diverse genomic data. To start using bioPIXIE, enter your genes of interest into the search box. You can use ORF names or aliases. If you enter multiple genes, they can be separated by commas or returns. Press ''submit''. bioPIXIE uses a probabilistic Bayesian algorithm to identify genes that are most likely to be in the same pathway/functional neighborhood as your genes of interest. It then displays biological network for the resulting genes as a graph. The nodes in the graph are genes (clicking on each node will bring up SGD page for that gene) and edges are interactions (clicking on each edge will show evidence used to predict this interaction). Most likely, the first results to load on the results page will be a list of significant Gene Ontology terms. This list is calculated for the genes in the biological network created by the bioPIXIE algorithm. If a gene ontology term appears on this list with a low p-value, it is statistically significantly overrepresented in this biological network. As you move the mouse over genes in the network, interactions involving these genes are highlighted. If you click on any of the highlighted interactions graph, evidence pop-up window will appear. The Evidence pop-up lists all evidence for this interaction, with links to the papers that produced this evidence - clicking these links will bring up the relevant source citation(s) in PubMed. You may need to download the Adobe Scalable Vector Graphic (SVG) plugin to utilize the visualization tool (you will be prompted if you need it).

Proper citation: bioPIXIE (RRID:SCR_004182) Copy   

•   Source: SciCrunch Registry

http://www.lipidmaps.org/data/structure/

Collection of structures and annotations of biologically relevant lipids that contains unique lipid structures. Structures of lipids from : LIPID MAPS Consortium's core laboratories and partners; lipids identified by LIPID MAPS experiments; biologically relevant lipids manually curated from LIPID BANK, LIPIDAT, Lipid Library, Cyberlipids, ChEBI and other public sources; novel lipids submitted to peer-reviewed journals; and computationally generated structures for appropriate classes. All the lipid structures adhere to the structure drawing rules proposed by the LIPID MAPS consortium. A number of structure viewing options are offered: gif image (default), Chemdraw (requires Chemdraw ActiveX/Plugin), MarvinView (Java applet) and JMol (Java applet). All lipids have been classified using the LIPID MAPS Lipid Classification System. Each lipid structure has been assigned a LIPID MAPS ID (LM_ID) which reflects its position in the classification hierarchy. In addition to a classification-based retrieval of lipids, users can search using either text-based or structure-based search options.

Proper citation: LIPID MAPS Structure Database (RRID:SCR_003817) Copy   

•   Source: SciCrunch Registry

http://integromedb.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 26, 2016. Search engine that integrates over 100 curated and publicly contributed data sources and provides integrated views on the genomic, proteomic, transcriptomic, genetic and functional information currently available. Information featured in the database includes gene function, orthologies, gene expression, pathways and protein-protein interactions, mutations and SNPs, disease relationships, related drugs and compounds.

Proper citation: IntegromeDB (RRID:SCR_004620) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/thornton-srv/databases/profunc/index.html

The ProFunc server had been developed to help identify the likely biochemical function of a protein from its three-dimensional structure. It uses both sequence- and structure-based methods including fold matching, residue conservation, surface cleft analysis, and functional 3D templates, to identify both the protein''''s likely active site and possible homologues in the PDB. Often, where one method fails to provide any functional insight another may be more helpful. You can submit your own structure, analyze an existing PDB entry, or retrieve the results of a previously submitted run. The files are usually stored for about 6 months before being deleted. However, they are stored on a partition that is not backed up; so, in principle, they could disappear at any time.

Proper citation: ProFunc (RRID:SCR_004450) Copy   

•   Source: SciCrunch Registry

http://www.phenologs.org/

Database for identifying orthologous phenotypes (phenologs). Mapping between genotype and phenotype is often non-obvious, complicating prediction of genes underlying specific phenotypes. This problem can be addressed through comparative analyses of phenotypes. We define phenologs based upon overlapping sets of orthologous genes associated with each phenotype. Comparisons of >189,000 human, mouse, yeast, and worm gene-phenotype associations reveal many significant phenologs, including novel non-obvious human disease models. For example, phenologs suggest a yeast model for mammalian angiogenesis defects and an invertebrate model for vertebrate neural tube birth defects. Phenologs thus create a rich framework for comparing mutational phenotypes, identify adaptive reuse of gene systems, and suggest new disease genes. To search for phenologs, go to the basic search page and enter a list of genes in the box provided, using Entrez gene identifiers for mouse/human genes, locus ids for yeast (e.g., YHR200W), or sequence names for worm (e.g., B0205.3). It is expected that this list of genes will all be associated with a particular system, trait, mutational phenotype, or disease. The search will return all identified model organism/human mutational phenotypes that show any overlap with the input set of the genes, ranked according to their hypergeometric probability scores. Clicking on a particular phenolog will result in a list of genes associated with the phenotype, from which potential new candidate genes can identified. Currently known phenotypes in the database are available from the link labeled ''Find phenotypes'', where the associated gene can be submitted as queries, or alternately, can be searched directly from the link provided.

Proper citation: Phenologs (RRID:SCR_005529) Copy   

•   Source: SciCrunch Registry

http://wikipathways.org/

Open and collaborative platform dedicated to curation of biological pathways. Each pathway has dedicated wiki page, displaying current diagram, description, references, download options, version history, and component gene and protein lists. Database of biological pathways maintained by and for scientific community.

Proper citation: WikiPathways (RRID:SCR_002134) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/friaptamerstream/

The Aptamer Database is a comprehensive, annotated repository for information about aptamers and in vitro selection. This resource is provided to collect, organize and distribute all the known information regarding aptamer selection. Aptamers are DNA or RNA molecules that have been selected from random pools based on their ability to bind other molecules. Aptamers have been selected which bind nucleic acid, proteins, small organic compounds, and even entire organisms.

Proper citation: Aptamer Database - The Ellington Lab (RRID:SCR_001781) Copy   

•   Source: SciCrunch Registry

http://www.uniprot.org/

Collection of data of protein sequence and functional information. Resource for protein sequence and annotation data. Consortium for preservation of the UniProt databases: UniProt Knowledgebase (UniProtKB), UniProt Reference Clusters (UniRef), and UniProt Archive (UniParc), UniProt Proteomes. Collaboration between European Bioinformatics Institute (EMBL-EBI), SIB Swiss Institute of Bioinformatics and Protein Information Resource. Swiss-Prot is a curated subset of UniProtKB.

Proper citation: UniProt (RRID:SCR_002380) Copy   

•   Source: SciCrunch Registry

http://bioinf-apache.charite.de/supertarget_v2/

Database for analyzing drug-target interactions, it integrates drug-related information associated with medical indications, adverse drug effects, drug metabolism, pathways and Gene Ontology (GO) terms for target proteins. At present (May 2013), the updated database contains >6000 target proteins, which are annotated with >330 000 relations to 196 000 compounds (including approved drugs); the vast majority of interactions include binding affinities and pointers to the respective literature sources. The user interface provides tools for drug screening and target similarity inclusion. A query interface enables the user to pose complex queries, for example, to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target proteins within a certain affinity range.

Proper citation: SuperTarget (RRID:SCR_002696) Copy   

•   Source: SciCrunch Registry

http://www.physionet.org/

Collection of dissemination and exchange recorded biomedical signals and open-source software for analyzing them. Provides facilities for cooperative analysis of data and evaluation of proposed new algorithm. Providies free electronic access to PhysioBank data and PhysioToolkit software. Offers service and training via on-line tutorials to assist users at entry and more advanced levels. In cooperation with annual Computing in Cardiology conference, PhysioNet hosts series of challenges, in which researchers and students address unsolved problems of clinical or basic scientific interest using data and software provided by PhysioNet. All data included in PhysioBank, and all software included in PhysioToolkit, are carefully reviewed. Researchers are further invited to contribute data and software for review and possible inclusion in PhysioBank and PhysioToolkit. Please review guidelines before submitting material.

Proper citation: PhysioNet (RRID:SCR_007345) Copy   

•   Source: SciCrunch Registry

https://github.com/nbcrrolls/workflows/tree/master/Production/AmberGPUMDSimulation

A workflow for running molecular dynamics simulations. It can be used for all-atom molecular dynamic simulations, which involve five steps of minimization, one step of heating, three steps of equilibration, and one or more instances of production. The input is a set of directories that include the MD simulation input scripts, system topology and coordinate files. Output files are list of plots, simulation trajectories, intermediate files, restart files, and the like.

Proper citation: Molecular Dynamics Workflow (BioKepler) (RRID:SCR_014389) Copy   

•   Source: SciCrunch Registry

https://glimmpse.samplesizeshop.org/#/

Web based software tool that calculates power and sample size for study designs with normally distributed outcomes. Permits power calculations for clinical trials, randomized experiments, and observational studies with clustering, repeated measures, and both, and almost any testable hypothesis. GLIMMPSE Version 3 release back end has been refactored in Python, interface has been simplified, requiring user decisions about only one topic per screen, new menu improves specification of both between-participant and within-participant hypothese, recursive algorithm permits computing covariances for up to ten levels of clustering., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GLIMMPSE (RRID:SCR_016297) Copy   

•   Source: SciCrunch Registry

https://gitlab.com/gernerlab/cytomap/-/wikis/home

Software tool as spatial analysis software for whole tissue sections.Utilizes information on cell type and position to phenotype local neighborhoods and reveal how their spatial distribution leads to generation of global tissue architecture.Used to make advanced data analytic techniques accessible for single cell data with position information.

Proper citation: CytoMAP (RRID:SCR_021227) Copy   

•   Source: SciCrunch Registry

https://github.com/CEGRcode/stencil

Web engine for visualizing and sharing life science datasets.Designed to organize, visualize, and enable sharing of interactive genomic data visualizations. Provides ability to inspect and interpret sequencing data, without requiring programming expertise.

Proper citation: STENCIL (RRID:SCR_021878) Copy   

•   Source: SciCrunch Registry