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http://meme-suite.org/

Suite of motif-based sequence analysis tools to discover motifs using MEME, DREME (DNA only) or GLAM2 on groups of related DNA or protein sequences; search sequence databases with motifs using MAST, FIMO, MCAST or GLAM2SCAN; compare a motif to all motifs in a database of motifs; associate motifs with Gene Ontology terms via their putative target genes, and analyze motif enrichment using SpaMo or CentriMo. Source code, binaries and a web server are freely available for noncommercial use.

Proper citation: MEME Suite - Motif-based sequence analysis tools (RRID:SCR_001783) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/web/packages/babelgene/index.html

Software R package to convert between human and non-human gene orthologs/homologs. Integrates orthology assertion predictions sourced from multiple databases as compiled by the HGNC Comparison of Orthology Predictions (HCOP) (Wright et al. 2005 , Eyre et al. 2007 , Seal et al. 2011 ).

Proper citation: babelgene (RRID:SCR_027117) Copy   

•   Source: SciCrunch Registry

https://www.stat.auckland.ac.nz/~paul/plaudits/Iobion.htm

GeneTraffic is a web-based microarray data analysis and management software developed by Iobion Informatics that allows users to log onto a server, upload their microarray data and perform analysis and project management remotely. GeneTraffic was made by Iobion Informatics (now under Stratagene) and can be accessed thorough Internet Explorer 6.0 or greater on Windows XP.

Proper citation: GeneTraffic (RRID:SCR_008651) Copy   

•   Source: SciCrunch Registry

http://vortex.cs.wayne.edu/projects.htm#OE2GO

Onto-Express is a web-based tool in the Onto-Tools suite that performs automated function profiling for a list of differentially expressed genes. However, Onto-Express does not support functional profiling for the organisms that do not have annotations in public domain, or use of custom (i.e. user-defined) ontologies. This limitation is also true for most of the other existing tools for functional profiling, which means that researchers working with uncommon organisms and/or new annotations or ontologies may be forced to construct such profiles manually. Onto-Express To Go (OE2GO) is a new tool added to the Onto-Tools ensemble to address these issues. OE2GO is built on top of OE to leverage its existing functionality. In OE2GO, the users now have an option to use either the Onto-Tools database as a source of functional annotations or provide their own annotations in a separate file. Currently, OE2GO supports annotation file in the Gene Ontology format. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Onto-Express To Go (OE2GO) (RRID:SCR_008854) Copy   

•   Source: SciCrunch Registry

http://go.princeton.edu/cgi-bin/GOTermFinder

The Generic GO Term Finder finds the significant GO terms shared among a list of genes from an organism, displaying the results in a table and as a graph (showing the terms and their ancestry). The user may optionally provide background information or a custom gene association file or filter evidence codes. This tool is capable of batch processing multiple queries at once. GO::TermFinder comprises a set of object-oriented Perl modules GO::TermFinder can be used on any system on which Perl can be run, either as a command line application, in single or batch mode, or as a web-based CGI script. This implementation, developed at the Lewis-Sigler Institute at Princeton, depends on the GO-TermFinder software written by Gavin Sherlock and Shuai Weng at Stanford University and the GO:View module written by Shuai Weng. It is made publicly available through the GMOD project. The full source code and documentation for GO:TermFinder are freely available from http://search.cpan.org/dist/GO-TermFinder/. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Generic GO Term Finder (RRID:SCR_008870) Copy   

•   Source: SciCrunch Registry

http://apid.dep.usal.es

APID Interactomes (Agile Protein Interactomes DataServer) provides information on the protein interactomes of numerous organisms, based on the integration of known experimentally validated protein-protein physical interactions (PPIs). The interactome data includes a report on quality levels and coverage over the proteomes for each organism included. APID integrates PPIs from primary databases of molecular interactions (BIND, BioGRID, DIP, HPRD, IntAct, MINT) and also from experimentally resolved 3D structures (PDB) where more than two distinct proteins have been identified. This collection references protein interactors, through a UniProt identifier.

Proper citation: Agile Protein Interactomes DataServer (RRID:SCR_008871) Copy   

•   Source: SciCrunch Registry

http://plantgrn.noble.org/LegumeIP/

LegumeIP is an integrative database and bioinformatics platform for comparative genomics and transcriptomics to facilitate the study of gene function and genome evolution in legumes, and ultimately to generate molecular based breeding tools to improve quality of crop legumes. LegumeIP currently hosts large-scale genomics and transcriptomics data, including: * Genomic sequences of three model legumes, i.e. Medicago truncatula, Glycine max (soybean) and Lotus japonicus, including two reference plant species, Arabidopsis thaliana and Poplar trichocarpa, with the annotation based on UniProt TrEMBL, InterProScan, Gene Ontology and KEGG databases. LegumeIP covers a total 222,217 protein-coding gene sequences. * Large-scale gene expression data compiled from 104 array hybridizations from L. japonicas, 156 array hybridizations from M. truncatula gene atlas database, and 14 RNA-Seq-based gene expression profiles from G. max on different tissues including four common tissues: Nodule, Flower, Root and Leaf. * Systematic synteny analysis among M. truncatula, G. max, L. japonicus and A. thaliana. * Reconstruction of gene family and gene family-wide phylogenetic analysis across the five hosted species. LegumeIP features comprehensive search and visualization tools to enable the flexible query on gene annotation, gene family, synteny, relative abundance of gene expression.

Proper citation: LegumeIP (RRID:SCR_008906) Copy   

•   Source: SciCrunch Registry

http://www.hapsample.org/

Web application for simulating SNP genotypes for case-control and affected-child trio studies by resampling from Phase I/II HapMap SNP data. The user provides a list of SNPs to be genotyped, along with a disease model file that describes causal SNPs and their effect sizes. The simulation tool is appropriate for candidate regions or whole-genome scans. (entry from Genetic Analysis Software)

Proper citation: HAP-SAMPLE (RRID:SCR_009234) Copy   

•   Source: SciCrunch Registry

http://pages.stat.wisc.edu/~yandell/qtl/software/qtlbim/

Software library for QTL Bayesian Interval Mapping that provides a Bayesian model selection approach to map multiple interacting QTL. It works on experimentally inbred lines and performs a genome-wide search to locate multiple potential QTL. The package can handle continuous, binary and ordinal traits. (entry from Genetic Analysis Software)

Proper citation: R/QTLBIM (RRID:SCR_009375) Copy   

•   Source: SciCrunch Registry

http://meme.nbcr.net/meme/cgi-bin/gomo.cgi

Gene Ontology for Motifs (GOMO) is an alignment- and threshold-free comparative genomics approach for assigning functional roles to DNA regulatory motifs from DNA sequence. The algorithm detects associations between a user-specified DNA regulatory motif (expressed as a position weight matrix; PWM) and Gene Ontology terms. The original method for predicting the roles of transcription factors (TFs starts with a PWM motif describing the DNA-binding affinity of the TF. GOMO uses the PWM to score the promoter region of each gene in the genome for its likelihood to be bound by the TF. The resulting ''''affinity'''' scores are then used to test each term in the Gene Ontology for association with high-scoring genes. The algorithm was subsequently extended to leverage conserved signals using multiple, related species in a comparative approach, which greatly improves the resulting annotations. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GOMO - Gene Ontology for Motifs (RRID:SCR_008864) Copy   

•   Source: SciCrunch Registry

http://rgd.mcw.edu/rgdCuration/?module=portal&func=show&name=renal

An integrated resource for information on genes, QTLs and strains associated with a variety of kidney and renal system conditions such as Renal Hypertension, Polycystic Kidney Disease and Renal Insufficiency, as well as Kidney Neoplasms.

Proper citation: Renal Disease Portal (RRID:SCR_009030) Copy   

•   Source: SciCrunch Registry

http://ageing-map.org/

Database of age-related changes covering different biological levels, including molecular, physiological, psychological and pathological age-related data, to create an interactive portal that serves as a centralized collection of human aging changes and pathologies. To facilitate integrative, system-level studies of aging, the DAA provides a centralized source for aging-related data as well as basic tools to query and visualize the data, including anatomical models. Data in the DAA is manually curated from the literature and retrieved from public databases. For more detailed analyses users are able to download the entire database. More information on how to use the DAA is available on the help page. The DAA primarily focuses on human aging, but also includes supplementary mouse data, in particular gene expression data, to enhance and expand the information on human aging. If you would like to contribute to the database yourself, for instance if you have new data on aging, please use the contribute page to submit your data.

Proper citation: Digital Ageing Atlas (RRID:SCR_009020) Copy   

•   Source: SciCrunch Registry

http://www.eplantsenescence.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 26, 2019. Database of leaf senescence to collect SAGs, mutants, phenotypes and literature references. Leaf senescence has been recognized as the last phase of plant development, a highly ordered process regulated by genes called SAGs. By integrating the data from mutant studies and transgenic analysis, they collected many SAGs related to regulation of the leaf senescence in various species. Additionally, they have categorized SAGs according to their functions in regulation of leaf senescence and used standard criteria to describe senescence associated phenotypes for mutants. Users are welcome to submit the new SAGs.

Proper citation: Leaf Senescence Database (RRID:SCR_010227) Copy   

•   Source: SciCrunch Registry

http://pathways.mcdb.ucla.edu/algal/

Tools to search gene lists for functional term enrichment as well as to dynamically visualize proteins onto pathway maps. Additionally, integrated expression data may be used to discover similarly expressed genes based on a starting gene of interest.

Proper citation: Algal Functional Annotation Tool (RRID:SCR_012034) Copy   

•   Source: SciCrunch Registry

http://www.pedigree-draw.com/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 12,2024. Software application for pedigree drawing (entry from Genetic Analysis Software)

Proper citation: Pedigree-Draw (RRID:SCR_008302) Copy   

•   Source: SciCrunch Registry

http://tikus.gsf.de

THIS RESOURCE IS NO LONGER IN SERVICE, documented on October 23, 2014. Consortium that generated a reference library of gene trap sequence tags (GTST) from insertional mutations generated in mouse embryonic stem (ES) cells. The gene trap database represents a repository of sequences produced in a large scale gene trap screen in mouse ES cells using various gene trapping vectors which are delivered either by electroporation or retroviral infections. A type of retroviral gene trap vector has been developed that can induce conditional mutations in most genes expressed in mouse embryonic stem (ES) cells. The vectors rely on directional site-specific recombination systems that can repair and re-induce gene trap mutations when activated in succession. After the gene traps are inserted into the mouse genome, genetic mutations can be produced at a particular time and place in somatic cells. In addition to their conditional features, the vectors create multipurpose alleles amenable to a wide range of post-insertional modifications. Here they have used these directional recombination vectors to assemble the largest library of ES cell lines with conditional mutations in single genes yet assembled, presently totaling 1,000 unique genes. The trapped ES cell lines, which can be ordered from the German Gene Trap Consortium, are freely available to the scientific community.

Proper citation: German Gene Trap Consortium (RRID:SCR_008532) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/web/packages/ibdreg/index.html

Software package in S-PLUS and R to test genetic linkage with covariates by regression methods with response IBD sharing for relative pairs. Account for correlations of IBD statistics and covariates for relative pairs within the same pedigree. (entry from Genetic Analysis Software)

Proper citation: IBDREG (RRID:SCR_013127) Copy   

•   Source: SciCrunch Registry

https://www.ncbi.nlm.nih.gov/UniGene/help.cgi?item=DDD

Software tool for comparing EST profiles in order to identify genes with significantly different expression levels.

Proper citation: Digital Differential Display (DDD) (RRID:SCR_016638) Copy   

•   Source: SciCrunch Registry

https://www.jax.org/news-and-insights/2013/february/komp2-mice-phenotyping-and-availability

Knockout Mouse Phenotyping Project, JAX information about their contributions to KOMP2 project. Project to generate and phenotype single gene KO mouse strains from KOMP ES cell lines. Strains are phenotyped using protocols in pipeline designed by International Mouse Phenotyping Consortium. There are three NIH-funded phenotyping centers in United States: JAX, BaSH Consortium (Baylor College of Medicine, the Wellcome Trust Sanger Institute and MRC Harwell), and the DTCC Consortium (University of California at Davis, the Toronto Center for Phenogenomics, Children’s Hospital Oakland Research Institute (CHORI) and Charles River ).

Proper citation: KOMP2 (RRID:SCR_017528) Copy   

•   Source: SciCrunch Registry

https://github.com/AlexsLemonade/refinebio

Software tool to uniformly process and normalize large amounts of data. Harmonizes petabytes of publicly available biological data into ready-to-use datasets for cancer researchers and AI/ML scientists.

Proper citation: refine.bio (RRID:SCR_017471) Copy   

•   Source: SciCrunch Registry