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https://clue.io

Dataset of cellular signatures that catalogs transcriptional responses of human cells to chemical and genetic perturbation. CMap contains perturbagens, expression signatures, and small molecules from cell lines.

Proper citation: CMap (RRID:SCR_016204) Copy   

•   Source: SciCrunch Registry

https://integrativeomics.shinyapps.io/pseudofun_app/

Software as database and query tool for homologous pseudogene and coding gene families. Collection of human pseudogenes and gene associations. Supports search, graphical visualization and functional analysis of pseudogenes and coding genes based on PGG families.

Proper citation: PseudoFuN (RRID:SCR_017095) Copy   

•   Source: SciCrunch Registry

https://github.com/madeluis/GENIST

Software tool as an algorithm to infer gene regulatory networks from spatial and temporal datasets. Spatial dataset or any data that can provide information about coexpression is used by the first step of the algorithm to perform clustering and separate the genes in the network in smaller coexpressed groups. Temporal dataset is used by the second step of the algorithm to infer regulations among the genes, based on Bayesian networks.

Proper citation: GENIST (RRID:SCR_016942) Copy   

•   Source: SciCrunch Registry

https://bitbucket.org/charade/svengine

Software for analysis and simulation of gene sequences and structural variants. This software works with FASTA, FASTQ, BAM, VAR, META, and NEWICK file formats.

Proper citation: SVEngine (RRID:SCR_016235) Copy   

•   Source: SciCrunch Registry

https://github.com/shanglicheng/RandomPooling

Software tool to identify the most reliable differences between any two groups . Used to identify differentially expressed genes between two groups.

Proper citation: RandomPooling (RRID:SCR_016607) Copy   

•   Source: SciCrunch Registry

http://brainarchitecture.org/allen-atlas-brain-toolbox

Software Matlab toolbox for quantitative analysis of digitized brain wide gene expression data from Allen Atlas of adult mouse brain.

Proper citation: Brain Gene Expression Analysis toolbox (RRID:SCR_017438) Copy   

•   Source: SciCrunch Registry

https://www.rdocumentation.org/packages/DGCA/versions/1.0.2

Software R package to perform differential gene correlation analysis. Performs differential correlation analysis on input matrices, with multiple conditions specified by design matrix.

Proper citation: Differential Gene Correlation Analysis (RRID:SCR_020964) Copy   

•   Source: SciCrunch Registry

http://faryabi05.med.upenn.edu:8050/

Portal for scRNA-seq study. Includes dendrogram visualization and clustering of all cells in scRNA-seq study as well as interactive filtered views for cell type, gene and/or donor group.

Proper citation: Mapping the pancreas and its ecosystem at the cellular level in health and type 1 diabetes (RRID:SCR_020952) Copy   

•   Source: SciCrunch Registry

http://www.seattle.eric.research.va.gov/VETR/Home.asp

The Vietnam Era Twin (VET) Registry is a closed cohort composed of approximately 7,000 middle-aged male-male twin pairs both of whom served in the military during the time of the Vietnam conflict (1964-1975). The Registry is a United States Department of Veterans Affairs (VA) resource that was originally constructed from military records; the Registry has been in existence for almost 20 years. It is one of the largest national twin registries in the US and currently has members living in all 50 states. Initially formed to address questions about the long-term health effects of service in Vietnam, the Registry has evolved into a resource for genetic epidemiological studies of mental and physical health conditions. Several waves of mail and telephone surveys have collected a wealth of health-related information on Registry twins, referred to as members. In addition to twins, selected adult offspring of twins and the mothers of those offspring are also VET Registry members. More recent data collection efforts have focused on specific sets of twin pairs and have conducted detailed clinical or laboratory testing. Selected Vietnam Era Registry Research Studies: * Veteran Health Study * VETSA 2: A Longitudinal Study of Cognitive Aging * Alcoholism Course thought Midlife: A Twin Family Study and Offspring of Twins: G, E and GxE Risk for Alcoholism * GE: Offspring of Twins with Substance Use Disorder * Mechanisms Linking Depression to Cardiovascular Risk (Twins Heart Study 2) * Post-traumatic Stress Disorder and Cardiovascular Disease * Biological Markers for Post-traumatic Stress Disorder (T3) * Memory and the Hippocampus in Vietnam-era Twins with PTSD (Time 3)

Proper citation: Vietnam Era Twin Registry (RRID:SCR_008807) Copy   

•   Source: SciCrunch Registry

https://www.sanger.ac.uk/collaboration/sequencing-idd-regions-nod-mouse-genome/

Genetic variations associated with type 1 diabetes identified by sequencing regions of the non-obese diabetic (NOD) mouse genome and comparing them with the same areas of a diabetes-resistant C57BL/6J reference mouse allowing identification of single nucleotide polymorphisms (SNPs) or other genomic variations putatively associated with diabetes in mice. Finished clones from the targeted insulin-dependent diabetes (Idd) candidate regions are displayed in the NOD clone sequence section of the website, where they can be downloaded either as individual clone sequences or larger contigs that make up the accession golden path (AGP). All sequences are publicly available via the International Nucleotide Sequence Database Collaboration. Two NOD mouse BAC libraries were constructed and the BAC ends sequenced. Clones from the DIL NOD BAC library constructed by RIKEN Genomic Sciences Centre (Japan) in conjunction with the Diabetes and Inflammation Laboratory (DIL) (University of Cambridge) from the NOD/MrkTac mouse strain are designated DIL. Clones from the CHORI-29 NOD BAC library constructed by Pieter de Jong (Children's Hospital, Oakland, California, USA) from the NOD/ShiLtJ mouse strain are designated CHORI-29. All NOD mouse BAC end-sequences have been submitted to the International Nucleotide Sequence Database Consortium (INSDC), deposited in the NCBI trace archive. They have generated a clone map from these two libraries by mapping the BAC end-sequences to the latest assembly of the C57BL/6J mouse reference genome sequence. These BAC end-sequence alignments can then be visualized in the Ensembl mouse genome browser where the alignments of both NOD BAC libraries can be accessed through the Distributed Annotation System (DAS). The Mouse Genomes Project has used the Illumina platform to sequence the entire NOD/ShiLtJ genome and this should help to position unaligned BAC end-sequences to novel non-reference regions of the NOD genome. Further information about the BAC end-sequences, such as their alignment, variation data and Ensembl gene coverage, can be obtained from the NOD mouse ftp site.

Proper citation: Sequencing of Idd regions in the NOD mouse genome (RRID:SCR_001483) Copy   

•   Source: SciCrunch Registry

http://metscape.ncibi.org

A software program that allows users to visualize and interpret human metabolim and expression profiling data by providing users with a bioinformatics framework. Its features include bulding and analyzing networks of genes and compounds, identifying enriched pathways from expression profiling data, and visualizing changes in metabolite data.

Proper citation: Metscape (RRID:SCR_014687) Copy   

•   Source: SciCrunch Registry

http://soap.genomics.org.cn/

Software package that provides full solution to next generation sequencing data analysis consisting of an alignment tool (SOAPaligner/soap2), a re-sequencing consensus sequence builder (SOAPsnp), an indel finder ( SOAPindel ), a structural variation scanner ( SOAPsv ), a de novo short reads assembler ( SOAPdenovo ), and a GPU-accelerated alignment tool for aligning short reads with a reference sequence. (SOAP3/GPU)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: SOAP (RRID:SCR_000689) Copy   

•   Source: SciCrunch Registry

http://www.cs.cmu.edu/~jernst/stem/

The Short Time-series Expression Miner (STEM) is a Java program for clustering, comparing, and visualizing short time series gene expression data from microarray experiments (~8 time points or fewer). STEM allows researchers to identify significant temporal expression profiles and the genes associated with these profiles and to compare the behavior of these genes across multiple conditions. STEM is fully integrated with the Gene Ontology (GO) database supporting GO category gene enrichment analyses for sets of genes having the same temporal expression pattern. STEM also supports the ability to easily determine and visualize the behavior of genes belonging to a given GO category or user defined gene set, identifying which temporal expression profiles were enriched for these genes. (Note: While STEM is designed primarily to analyze data from short time course experiments it can be used to analyze data from any small set of experiments which can naturally be ordered sequentially including dose response experiments.) Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Short Time-series Expression Miner (STEM) (RRID:SCR_005016) Copy   

•   Source: SciCrunch Registry

http://www.btool.org/ADGO2

A web-based tool that provides composite interpretations for microarray data comparing two sample groups as well as lists of genes from diverse sources of biological information. It provides multiple gene set analysis methods for microarray inputs as well as enrichment analyses for lists of genes. It screens redundant composite annotations when generating and prioritizing them. It also incorporates union and subtracted sets as well as intersection sets. Users can upload their gene sets (e.g. predicted miRNA targets) to generate and analyze new composite sets.

Proper citation: ADGO (RRID:SCR_006343) Copy   

•   Source: SciCrunch Registry

http://tbdb.org

Database providing integrated access to genome sequence, expression data and literature curation for Tuberculosis (TB) that houses genome assemblies for numerous strains of Mycobacterium tuberculosis (MTB) as well assemblies for over 20 strains related to MTB and useful for comparative analysis. TBDB stores pre- and post-publication gene-expression data from M. tuberculosis and its close relatives, including over 3000 MTB microarrays, 95 RT-PCR datasets, 2700 microarrays for human and mouse TB related experiments, and 260 arrays for Streptomyces coelicolor. (July 2010) To enable wide use of these data, TBDB provides a suite of tools for searching, browsing, analyzing, and downloading the data.

Proper citation: Tuberculosis Database (RRID:SCR_006619) Copy   

•   Source: SciCrunch Registry

https://scicrunch.org/scicrunch/data/source/nlx_154697-7/search?q=*

Virtual database currently indexing interaction between genes and diseases from Online Mendelian Inheritance in Man (OMIM) and Comparative Toxicogenomics Database (CTD).

Proper citation: Integrated Gene-Disease Interaction (RRID:SCR_006173) Copy   

•   Source: SciCrunch Registry

https://www.jax.org/jax-mice-and-services/in-vivo-pharmacology/mouse-tumor-biology-database

Database supports use of mouse model system for human cancer by providing comprehensive resource for data and information on various tumor models.

Proper citation: Mouse Tumor Biology Database (RRID:SCR_006517) Copy   

•   Source: SciCrunch Registry

http://www.droidb.org

A gene and protein interactions database designed specifically for the model organism Drosophila including protein-protein, transcription factor-gene, microRNA-gene, and genetic interactions. For advanced searches and dynamic graphing capabilities the IM Browser and a DroID Cytoscape plugin are available.

Proper citation: DroID - Drosophila Interactions Database (RRID:SCR_006634) Copy   

•   Source: SciCrunch Registry

http://www.pdgene.org/

The PDGene database aims to provide a comprehensive, unbiased and regularly updated collection of genetic association studies performed on Parkinson's disease (PD) phenotypes. Eligible publications are identified following systematic searches of scientific literature databases, as well as the table of contents of journals in genetics, neurology, and psychiatry. The database can be searched either by a variety of dropdown menus or by specific keywords. For each gene, summary overviews are provided displaying key characteristics for each publication, including links to genotype distributions of the polymorphisms studied, random-effects allelic meta-analyses, and funnel plots for an assessment of publication bias. The PDGene database, developed by Massachusetts General Hospital/Harvard Medical School, The Michael J. Fox Foundation and the Alzheimer Research Forum, is supported by a grant from The Michael J. Fox Foundation in partnership with the Alzheimer Research Forum.

Proper citation: PDGene - A database for Parkinsons disease genetic association studies (RRID:SCR_006666) Copy   

•   Source: SciCrunch Registry

https://www.arb-silva.de

High quality ribosomal RNA databases providing comprehensive, quality checked and regularly updated datasets of aligned small (16S/18S, SSU) and large subunit (23S/28S, LSU) ribosomal RNA (rRNA) sequences for all three domains of life (Bacteria, Archaea and Eukarya). Supplementary services include a rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. The extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches. Alignment tool, SINA, is available for download as well as available for use online.

Proper citation: SILVA (RRID:SCR_006423) Copy   

•   Source: SciCrunch Registry