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http://bioweb.ensam.inra.fr/esther

Database and tools for analysis of protein and nucleic acid sequences belonging to superfamily of alpha/beta hydrolases homologous to cholinesterases. Covers multiple species, including human, mouse caenorhabditis and drosophila., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: ESTHER (RRID:SCR_002621) Copy   

•   Source: SciCrunch Registry

http://www.cbil.upenn.edu/RAD

THIS RESOURCE IS NO LONGER IN SERVICE, Documented on March 24, 2014. A resource for gene expression studies, storing highly curated MIAME-compliant studies (i.e. experiments) employing a variety of technologies such as filter arrays, 2-channel microarrays, Affymetrix chips, SAGE, MPSS and RT-PCR. Data were available for querying and downloading based on the MGED ontology, publications or genes. Both public and private studies (the latter viewable only by users having appropriate logins and permissions) were available from this website. Specific details on protocols, biomaterials, study designs, etc., are collected through a user-friendly suite of web annotation forms. Software has been developed to generate MAGE-ML documents to enable easy export of studies stored in RAD to any other database accepting data in this format. RAD is part of a more general Genomics Unified Schema (http://gusdb.org), which includes a richly annotated gene index (http://allgenes.org), thus providing a platform that integrates genomic and transcriptomic data from multiple organisms. NOTE: Due to changes in technology and funding, the RAD website is no longer available. RAD as a schema is still very much active and incorporated in the GUS (Genomics Unified Schema) database system used by CBIL (EuPathDB, Beta Cell Genomics) and others. The schema for RAD can be viewed along with the other GUS namespaces through our Schema Browser.

Proper citation: RNA Abundance Database (RRID:SCR_002771) Copy   

•   Source: SciCrunch Registry

http://learn.genetics.utah.edu/content/addiction/

A physiologic and molecular look at drug addiction involving many factors including: basic neurobiology, a scientific examination of drug action in the brain, the role of genetics in addiction, and ethical considerations. Designed to be used by students, teachers and members of the public, the materials meet selected US education standards for science and health. Drug addiction is a chronic disease characterized by changes in the brain which result in a compulsive desire to use a drug. A combination of many factors including genetics, environment and behavior influence a person's addiction risk, making it an incredibly complicated disease. The new science of addiction considers all of these factors - from biology to family - to unravel the complexities of the addicted brain. * Natural Reward Pathways Exist in the Brain: The reward pathway is responsible for driving our feelings of motivation, reward and behavior. * Drugs Alter the Brain's Reward Pathway: Drugs work over time to change the reward pathway and affect the entire brain, resulting in addiction. * Genetics Is An Important Factor In Addiction: Genetic susceptibility to addiction is the result of the interaction of many genes. * Timing and Circumstances Influence Addiction: If you use drugs when you are an adolescent, you are more likely to develop lifetime addiction. An individual's social environment also influences addiction risk. * Challenges and Issues in Addiction: Addiction impacts society with many ethical, legal and social issues.

Proper citation: New Science of Addiction: Genetics and the Brain (RRID:SCR_002770) Copy   

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http://uwaging.org/genesdb/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 17,2023. A database of genes and interventions connected with aging phenotypes including those with respect to their effects on life-span or age-related neurological diseases. Information includes: organism, aging phenotype, allele type, strain, gene function, phenotypes, mutant, and homologs. If you know of published data (or your own unpublished data that you'd like to share) not currently in the database, please use the Submit a Gene/Intervention link.

Proper citation: Aging Genes and Interventions Database (RRID:SCR_002701) Copy   

•   Source: SciCrunch Registry

http://www.flymine.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 14,2026. Integrated database of genomic, expression and protein data for Drosophila, Anopheles, C. elegans and other organisms. You can run flexible queries, export results and analyze lists of data. FlyMine presents data in categories, with each providing information on a particular type of data (for example Gene Expression or Protein Interactions). Template queries, as well as the QueryBuilder itself, allow you to perform searches that span data from more than one category. Advanced users can use a flexible query interface to construct their own data mining queries across the multiple integrated data sources, to modify existing template queries or to create your own template queries. Access our FlyMine data via our Application Programming Interface (API). We provide client libraries in the following languages: Perl, Python, Ruby and & Java API

Proper citation: FlyMine (RRID:SCR_002694) Copy   

•   Source: SciCrunch Registry

http://www.pharmgkb.org/

Database and central repository for genetic, genomic, molecular and cellular phenotype data and clinical information about people who have participated in pharmacogenomics research studies. The data includes, but is not limited to, clinical and basic pharmacokinetic and pharmacogenomic research in the cardiovascular, pulmonary, cancer, pathways, metabolic and transporter domains. PharmGKB welcomes submissions of primary data from all research into genes and genetic variation and their effects on drug and disease phenotypes. PharmGKB collects, encodes, and disseminates knowledge about the impact of human genetic variations on drug response. They curate primary genotype and phenotype data, annotate gene variants and gene-drug-disease relationships via literature review, and summarize important PGx genes and drug pathways. PharmGKB is part of the NIH Pharmacogenomics Research Network (PGRN), a nationwide collaborative research consortium. Its aim is to aid researchers in understanding how genetic variation among individuals contributes to differences in reactions to drugs. A selected subset of data from PharmGKB is accessible via a SOAP interface. Downloaded data is available for individual research purposes only. Drugs with pharmacogenomic information in the context of FDA-approved drug labels are cataloged and drugs with mounting pharmacogenomic evidence are listed.

Proper citation: PharmGKB (RRID:SCR_002689) Copy   

•   Source: SciCrunch Registry

http://www.geneontology.org/

Computable knowledge regarding functions of genes and gene products. GO resources include biomedical ontologies that cover molecular domains of all life forms as well as extensive compilations of gene product annotations to these ontologies that provide largely species-neutral, comprehensive statements about what gene products do. Used to standardize representation of gene and gene product attributes across species and databases.

Proper citation: Gene Ontology (RRID:SCR_002811) Copy   

•   Source: SciCrunch Registry

http://nbc.jhu.edu/

This center provides routine behavioral/cognitive testing of mice with phenotypes that are expressed as a consequence of alterations at the level of gene function, and that are relevant to basic neuroscience and to animal models of neurological and psychiatric disorders. Current Research Behavioral testing within the center involves a collaborative component in which mice provided by users are assessed for behavioral/cognitive functions. All research includes behavioral assessment of a variety of genetically altered mice provided by users. Services Provided The objective of the center is to provide a link between genetic and molecular analysis of neural function and the study of integrative systems and clinical conditions through behavioral assessment of animal models, and mouse behavioral phenotypes generated by genetic modification. Sponsors: This resource is supported by the National Center of Research Resources (Grant Number: P40 RR017688).

Proper citation: Neurogenetics and Behavior Center (RRID:SCR_002851) Copy   

•   Source: SciCrunch Registry

http://www.ambystoma.org/

Portal that supports Ambystoma-related research and educational efforts. It is composed of several resources: Salamander Genome Project, Ambystoma EST Database, Ambystoma Gene Collection, Ambystoma Map and Marker Collection, Ambystoma Genetic Stock Center, and Ambystoma Research Coordination Network.

Proper citation: Sal-Site (RRID:SCR_002850) Copy   

•   Source: SciCrunch Registry

http://www.jax.org/smsr/index.html

Resource of special strains of mice that are valuable tools for genetic analysis of complex diseases. They include panels of recombinant inbred (RI) and chromosome substitution (CS) strains.

Proper citation: Special Mouse Strains Resource (RRID:SCR_002885) Copy   

•   Source: SciCrunch Registry

http://cbio.mskcc.org/

Computational biology research at Memorial Sloan-Kettering Cancer Center (MSKCC) pursues computational biology research projects and the development of bioinformatics resources in the areas of: sequence-structure analysis; gene regulation; molecular pathways and networks, and diagnostic and prognostic indicators. The mission of cBio is to move the theoretical methods and genome-scale data resources of computational biology into everyday laboratory practice and use, and is reflected in the organization of cBio into research and service components ~ the intention being that new computational methods created through the process of scientific inquiry should be generalized and supported as open-source and shared community resources. Faculty from cBio participate in graduate training provided through the following graduate programs: * Gerstner Sloan-Kettering Graduate School of Biomedical Sciences * Graduate Training Program in Computational Biology and Medicine Integral to much of the research and service work performed by cBio is the creation and use of software tools and data resources. The tools that we have created and utilize provide evidence of our involvement in the following areas: * Cancer Genomics * Data Repositories * iPhone & iPod Touch * microRNAs * Pathways * Protein Function * Text Analysis * Transcription Profiling

Proper citation: Computational Biology Center (RRID:SCR_002877) Copy   

•   Source: SciCrunch Registry

http://harvard.eagle-i.net/i/0000012e-58c7-d44f-55da-381e80000000

Core to provide gene expression data analysis service. Activities range from the provision of services to fully collaborative grant funded investigations.

Proper citation: Harvard Partners HealthCare Center for Personalized Genetic Medicine Bioinformatics Core Facility (RRID:SCR_000882) Copy   

•   Source: SciCrunch Registry

http://www.yandell-lab.org/software/index.html

Sequenced genomes contain a treasure trove of information about how genes function and evolve. Getting at this information, however, is challenging and requires novel approaches that combine computer science and experimental molecular biology. My lab works at the intersection of both domains, and research in our group can be summarized as follows: generate hypotheses concerning gene function and evolution by computational means, and then test these hypotheses at the bench. This is easier said than done, as serious barriers still exist to using sequenced genomes and their annotations as starting points for experimental work. Some of these barriers lie in the computational domain, others in the experimental. Though challenging, overcoming these barriers offers exciting training opportunities in both computer science and molecular genetics, especially for those seeking a future at the intersection of both fields. Ongoing projects in the lab are centered on genome annotation and comparative genomics; exploring the relationships between sequence variation and human disease; and high-throughput biological image analysis. Current software tools available: VAAST (the Variant Annotation, Analysis & Search Tool) is a probabilistic search tool for identifying damaged genes and their disease-causing variants in personal genome sequences. VAAST builds upon existing amino acid substitution (AAS) and aggregative approaches to variant prioritization, combining elements of both into a single unified likelihood-framework that allows users to identify damaged genes and deleterious variants with greater accuracy, and in an easy-to-use fashion. VAAST can score both coding and non-coding variants, evaluating the cumulative impact of both types of variants simultaneously. VAAST can identify rare variants causing rare genetic diseases, and it can also use both rare and common variants to identify genes responsible for common diseases. VAAST thus has a much greater scope of use than any existing methodology. MAKER 2 (updated 01-16-2012) MAKER is a portable and easily configurable genome annotation pipeline. It's purpose is to allow smaller eukaryotic and prokaryotic genomeprojects to independently annotate their genomes and to create genome databases. MAKER identifies repeats, aligns ESTs and proteins to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations having evidence-based quality values. MAKER is also easily trainable: outputs of preliminary runs can be used to automatically retrain its gene prediction algorithm, producing higher quality gene-models on seusequent runs. MAKER's inputs are minimal and its ouputs can be directly loaded into a GMOD database. They can also be viewed in the Apollo genome browser; this feature of MAKER provides an easy means to annotate, view and edit individual contigs and BACs without the overhead of a database. MAKER should prove especially useful for emerging model organism projects with minimal bioinformatics expertise and computer resources. RepeatRunner RepeatRunner is a CGL-based program that integrates RepeatMasker with BLASTX to provide a comprehensive means of identifying repetitive elements. Because RepeatMasker identifies repeats by means of similarity to a nucleotide library of known repeats, it often fails to identify highly divergent repeats and divergent portions of repeats, especially near repeat edges. To remedy this problem, RepeatRunner uses BLASTX to search a database of repeat encoded proteins (reverse transcriptases, gag, env, etc...). Because protein homologies can be detected across larger phylogenetic distances than nucleotide similarities, this BLASTX search allows RepeatRunner to identify divergent protein coding portions of retro-elements and retro-viruses not detected by RepeatMasker. RepeatRunner merges its BLASTX and RepeatMasker results to produce a single, comprehensive XML-based output. It also masks the input sequence appropriately. In practice RepeatRunner has been shown to greatly improve the efficacy of repeat identifcation. RepeatRunner can also be used in conjunction with PILER-DF - a program designed to identify novel repeats - and RepeatMasker to produce a comprehensive system for repeat identification, characterization, and masking in the newly sequenced genomes. CGL CGL is a software library designed to facilitate the use of genome annotations as substrates for computation and experimentation; we call it CGL, an acronym for Comparitive Genomics Library, and pronounce it Seagull. The purpose of CGL is to provide an informatics infrastructure for a laboratory, department, or research institute engaged in the large-scale analysis of genomes and their annotations.

Proper citation: Yandell Lab Portal (RRID:SCR_000807) Copy   

•   Source: SciCrunch Registry

http://www.rostlab.org/cms/

A lab organization which has bases in Munich, Germany and at Columbia University and focuses its research on protein structure and function using sequence and evolutionary information. They utilize machine learning and statistical methods to analyze genetic material and its gene products. Research goals of the lab involve using protein and DNA sequences along with evolutionary information to predict aspects of the proteins relevant to the advance of biomedical research.

Proper citation: ROSTLAB (RRID:SCR_000792) Copy   

•   Source: SciCrunch Registry

http://fantom.gsc.riken.jp/

International collaborative research project and database of annotated mammalian genome. Used to improve estimates of total number of genes and their alternative transcript isoforms in both human and mouse. Consortium to assign functional annotations to full length cDNAs that were collected during Mouse Encyclopedia Project at RIKEN.

Proper citation: Functional Annotation of the Mammalian Genome (RRID:SCR_000788) Copy   

•   Source: SciCrunch Registry

http://www.genome.jp/kegg/expression/

Database for mapping gene expression profiles to pathways and genomes. Repository of microarray gene expression profile data for Synechocystis PCC6803 (syn), Bacillus subtilis (bsu), Escherichia coli W3110 (ecj), Anabaena PCC7120 (ana), and other species contributed by the Japanese research community.

Proper citation: Kyoto Encyclopedia of Genes and Genomes Expression Database (RRID:SCR_001120) Copy   

•   Source: SciCrunch Registry

http://ccb.jhu.edu/software/sim4cc/

Software tool as cross species spliced alignment program.Heuristic sequence alignment tool for comparing cDNA sequence with genomic sequence containing homolog of gene in another species.

Proper citation: sim4cc (RRID:SCR_001204) Copy   

•   Source: SciCrunch Registry

http://www.omixon.com/data-analysis-and-pro/

Software application suite to help clinical labs adopt next generation sequencing for the analysis of diagnostic gene targets.

Proper citation: Omixon Target Data Analysis (RRID:SCR_001207) Copy   

•   Source: SciCrunch Registry

https://rdrr.io/bioc/betr/man/betr.html#heading-0

Software package that implements the Bayesian Estimation of Temporal Regulation algorithm to identify differentially expressed genes in microarray time-course data.

Proper citation: betr (RRID:SCR_001332) Copy   

•   Source: SciCrunch Registry

http://www.well.ox.ac.uk/happy/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Software package for Multipoint QTL Mapping in Genetically Heterogeneous Animals (entry from Genetic Analysis Software) The method is implemented in a C-program and there is now an R version of HAPPY. You can run HAPPY remotely from their web server using your own data (or try it out on the data provided for download).

Proper citation: Happy (RRID:SCR_001395) Copy   

•   Source: SciCrunch Registry