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http://www-genome.stanford.edu/

This resource hyperlinks to systematic analysis projects, resources, laboratories, and departments at Stanford University.

Proper citation: Stanford Genomic Resourses (RRID:SCR_001874) Copy   

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http://www.nitrc.org/projects/plink

Open source whole genome association analysis toolset, designed to perform range of basic, large scale analyses in computationally efficient manner. Used for analysis of genotype/phenotype data. Through integration with gPLINK and Haploview, there is some support for subsequent visualization, annotation and storage of results. PLINK 1.9 is improved and second generation of the software.

Proper citation: PLINK (RRID:SCR_001757) Copy   

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http://www.oege.org/

Portal for researchers to locate information relevant to interpretation and follow-up of human genetic epidemiological discoveries, including: a range of population and case and family genetic epidemiological studies, relevant gene and sequence databases, genetic variation databases, trait measurement, resource labs, journals, software, general information, disease genes and genetic diversity.

Proper citation: Online Encyclopedia for Genetic Epidemiology studies (RRID:SCR_001825) Copy   

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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   

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http://amigo.geneontology.org/

Web tool to search, sort, analyze, visualize and download data of interest. Along with providing details of the ontologies, gene products and annotations, features a BLAST search, Term Enrichment and GO Slimmer tools, the GO Online SQL Environment and a user help guide.Used at the Gene Ontology (GO) website to access the data provided by the GO Consortium. Developed and maintained by the GO Consortium.

Proper citation: AmiGO (RRID:SCR_002143) Copy   

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https://www.snpstats.net/

A web-based application designed from a genetic epidemiology point of view to analyze association studies using single nucleotide polymorphisms (SNPs). For each selected SNP, you will receive: * Allele and genotype frequencies * Test for Hardy-Weinberg equilibrium * Analysis of association with a response variable based on linear or logistic regression * Multiple inheritance models: co-dominant, dominant, recessive, over-dominant and additive * Analysis of interactions (gene-gene or gene-environment) If multiple SNPs are selected: * Linkage disequilibrium statistics * Haplotype frequency estimation * Analysis of association of haplotypes with the response * Analysis of interactions (haplotypes-covariate)

Proper citation: SNPSTATS (RRID:SCR_002142) Copy   

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http://compbio.dfci.harvard.edu/tgi/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 10, 2017. A pilot effort that has developed a centralized, web-based biospecimen locator that presents biospecimens collected and stored at participating Arizona hospitals and biospecimen banks, which are available for acquisition and use by researchers. Researchers may use this site to browse, search and request biospecimens to use in qualified studies. The development of the ABL was guided by the Arizona Biospecimen Consortium (ABC), a consortium of hospitals and medical centers in the Phoenix area, and is now being piloted by this Consortium under the direction of ABRC. You may browse by type (cells, fluid, molecular, tissue) or disease. Common data elements decided by the ABC Standards Committee, based on data elements on the National Cancer Institute''s (NCI''s) Common Biorepository Model (CBM), are displayed. These describe the minimum set of data elements that the NCI determined were most important for a researcher to see about a biospecimen. The ABL currently does not display information on whether or not clinical data is available to accompany the biospecimens. However, a requester has the ability to solicit clinical data in the request. Once a request is approved, the biospecimen provider will contact the requester to discuss the request (and the requester''s questions) before finalizing the invoice and shipment. The ABL is available to the public to browse. In order to request biospecimens from the ABL, the researcher will be required to submit the requested required information. Upon submission of the information, shipment of the requested biospecimen(s) will be dependent on the scientific and institutional review approval. Account required. Registration is open to everyone.. Documented on August 19,2019.The goal of The Gene Index Project is to use the available Expressed Sequence Transcript (EST) and gene sequences, along with the reference genomes wherever available, to provide an inventory of likely genes and their variants and to annotate these with information regarding the functional roles played by these genes and their products. The promise of genome projects has been a complete catalog of genes in a wide range of organisms. While genome projects have been successful in providing reference genome sequences, the problem of finding genes and their variants in genomic sequence remains an ongoing challenge. TGI has created an inventory that contains genes and their variants together with description. In addition, this resource is attempting to use these catalogs to find links between genes and pathways in different species and to provide lists of features within completed genomes that can aid in the understanding of how gene expression is regulated. DATABASES *Eukaryotic Gene Orthologues (formerly known as TOGA - TIGR Orthologous Gene Alignment): Eukaryotic Gene Orthologues (EGO) at DFGI are generated by pair-wise comparison between the Tentative Consensus (TC) sequences that comprise the Dana Farber Gene Indices from individual organisms. The reciprocal pairs of the best match were clustered into individual groups and multiple sequence alignments were displayed for each group. *GeneChip Oncology Database (GCOD):Cancer gene expression database is a collection of publicly available microarray expression data on Affymetrix GeneChip Arrays related to human cancers. Currently only datasets with available raw data (Affymetrix .CEL files) are processed. All processed datasets were subjected to extensive manual curation, uniform processing and consistent quality control. You can browse the experiments in our collection, perform statistical analysis, and download processed data; or to search gene expression profiles using Entrez gene symbol, Unigene ID, or Affymetrix probeset ID. *Gene Indices: As of July 1, 2008, there are 111 publicly available gene indices. They are separated into 4 categories for better organization and easier access. Animal: 41, Plant: 45, Protist: 15, Fungal: 10 *Genomic Maps: Human, mouse, rat, chicken, drosophila melanogaster, zebrafish, mosquito, caenorhabditis elegans, Arabidopsis thaliana, rice, yeast, fission yeast Dana-Farber Cancer Institute (DFCI) Gene Indices Software Tools: *TGI Clustering tools (TGICL): a software system for fast clustering of large EST datasets. *GICL: this package contains the scripts and all the necessary pre-compiled binaries for 32bit Linux systems. *clview: an assembly file viewer. *SeqClean:a script for automated trimming and validation of ESTs or other DNA sequences by screening for various contaminants, low quality and low-complexity sequences. *cdbfasta/cdbyank: fast indexing/retrieval of fasta records from flat file databases. *DAS/XML Genomic Viewer The Genomic viewer borrows modules from http://www.biodas.org (lstein (at) cshl.org) & http://webreference.com.

Proper citation: Gene Index Project (RRID:SCR_002148) Copy   

•   Source: SciCrunch Registry

http://www.le.ac.uk/genetics/genie/vgec/index.html

Hub of evaluated genetics-related teaching resources for teachers and learners in schools and higher education, health professionals and the general public. Suggest or submit a learning resource to the VGEC. Resources include: * simple experiments suitable for all ages * tutorial material * videos on useful techniques * current and relevant links to other evaluated resources The Virtual Genetics Education Centre (VGEC) * Provides information and genetics education resources for higher education, colleges, schools, health professionals and the general public. * Encourages collaboration in the development, evaluation and sharing of genetics education resources * provides links to, and evaluates, sources of information and educational material about genetics. * Explores innovative approaches to teaching and learning in genetics, such as the SWIFT project for example where Second Life is being used to teach some aspects of genetics in a virtual laboratory.

Proper citation: Virtual Genetics Education Centre (RRID:SCR_001958) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/biomodels-main/

Repository of mathematical models of biological and biomedical systems. Hosts selection of existing literature based physiologically and pharmaceutically relevant mechanistic models in standard formats. Features programmatic access via Web Services. Each model is curated to verify that it corresponds to reference publication and gives proper numerical results. Curators also annotate components of models with terms from controlled vocabularies and links to other relevant data resources allowing users to search accurately for models they need. Models can be retrieved in SBML format and import/export facilities are being developed to extend spectrum of formats supported by resource.

Proper citation: BioModels (RRID:SCR_001993) Copy   

•   Source: SciCrunch Registry

http://www.aspgd.org/

Database of genetic and molecular biological information about the filamentous fungi of the genus Aspergillus including information about genes and proteins of Aspergillus nidulans and Aspergillus fumigatus; descriptions and classifications of their biological roles, molecular functions, and subcellular localizations; gene, protein, and chromosome sequence information; tools for analysis and comparison of sequences; and links to literature information; as well as a multispecies comparative genomics browser tool (Sybil) for exploration of orthology and synteny across multiple sequenced Sgenus species. Also available are Gene Ontology (GO) and community resources. Based on the Candida Genome Database, the Aspergillus Genome Database is a resource for genomic sequence data and gene and protein information for Aspergilli. Among its many species, the genus contains an excellent model organism (A. nidulans, or its teleomorph Emericella nidulans), an important pathogen of the immunocompromised (A. fumigatus), an agriculturally important toxin producer (A. flavus), and two species used in industrial processes (A. niger and A. oryzae). Search options allow you to: *Search AspGD database using keywords. *Find chromosomal features that match specific properties or annotations. *Find AspGD web pages using keywords located on the page. *Find information on one gene from many databases. *Search for keywords related to a phenotype (e.g., conidiation), an allele (such as veA1), or an experimental condition (e.g., light). Analysis and Tools allow you to: *Find similarities between a sequence of interest and Aspergillus DNA or protein sequences. *Display and analyze an Aspergillus sequence (or other sequence) in many ways. *Navigate the chromosomes set. View nucleotide and protein sequence. *Find short DNA/protein sequence matches in Aspergillus. *Design sequencing and PCR primers for Aspergillus or other input sequences. *Display the restriction map for a Aspergillus or other input sequence. *Find similarities between a sequence of interest and fungal nucleotide or protein sequences. AspGD welcomes data submissions.

Proper citation: ASPGD (RRID:SCR_002047) Copy   

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http://www.ncbi.nlm.nih.gov/HTGS/

Database of high-throughput genome sequences from large-scale genome sequencing centers, including unfinished and finished sequences. It was created to accommodate a growing need to make unfinished genomic sequence data rapidly available to the scientific community in a coordinated effort among the International Nucleotide Sequence databases, DDBJ, EMBL, and GenBank. Sequences are prepared for submission by using NCBI's software tools Sequin or tbl2asn. Each center has an FTP directory into which new or updated sequence files are placed. Sequence data in this division are available for BLAST homology searches against either the htgs database or the month database, which includes all new submissions for the prior month. Unfinished HTG sequences containing contigs greater than 2 kb are assigned an accession number and deposited in the HTG division. A typical HTG record might consist of all the first-pass sequence data generated from a single cosmid, BAC, YAC, or P1 clone, which together make up more than 2 kb and contain one or more gaps. A single accession number is assigned to this collection of sequences, and each record includes a clear indication of the status (phase 1 or 2) plus a prominent warning that the sequence data are unfinished and may contain errors. The accession number does not change as sequence records are updated; only the most recent version of a HTG record remains in GenBank.

Proper citation: High Throughput Genomic Sequences Division (RRID:SCR_002150) Copy   

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http://www.doe-mbi.ucla.edu/

The UCLA-DOE Institute for Genomics and Proteomics carries out research in bioenergy, structural biology, genomics and proteomics, consistent with the research mission of the United States Department of Energy. Major interests of the 12 Principal Investigators and 9 Associate Members include systems approaches to organisms, structural biology, bioinformatics, and bioenergetic systems. The Institute sponsors 5 Core Technology Centers, for X-ray and NMR structural determination, bioinformatics and computation, protein expression and purification, and biochemical instrumentation. Services offered by this Institute: - Databases: * DIP (The Database of Interacting Proteins): The DIPTM database catalogs experimentally determined interactions between proteins. It combines information from a variety of sources to create a single, consistent set of protein-protein interactions. * ProLinks Database of Functional Linkages: The Prolinks database is a collection of inference methods used to predict functional linkages between proteins. These methods include the Phylogenetic Profile method which uses the presence and absence of proteins across multiple genomes to detect functional linkages; the Gene Cluster method, which uses genome proximity to predict functional linkage; Rosetta Stone, which uses a gene fusion event in a second organism to infer functional relatedness; and the Gene Neighbor method, which uses both gene proximity and phylogenetic distribution to infer linkage. - Data-to-Structure Servers: * SAVEs Structure Verification Server * Merohedral Twinning Test Server * SER Surface Entropy Reduction Server * VERIFY3D Structure Verification Server * ERRAT Structure Verification Server - Structure-to-Function Servers: * ProKnow Protein Functionator * Hot Patch Functional Site Locator

Proper citation: University of California at Los Angeles - Department of Energy Institute for Genomics and Proteomics (RRID:SCR_001921) Copy   

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http://www.proteinlounge.com

Complete siRNA target database, complete Peptide-Antigen target database and a Kinase-Phosphatase database. They have also developed the largest database of illustrated signal transduction pathways, which are interconnected to their extensive protein database and online gene / protein analysis tools. The interactive web-based databases and software help life-scientists understand the complexity of systems biology. Systems biology efforts focus on understanding cellular networks, protein interactions involved in cell signaling, mechanisms of cell survival and apoptosis leading to development or identification of drug candidates against a variety of diseases. In the post-genomic era, one of the major concerns for life-science researchers is the organization of gene / protein data. Protein Lounge has met this concern by organizing all necessary data about genes / proteins into one portal.

Proper citation: Protein Lounge (RRID:SCR_002117) Copy   

•   Source: SciCrunch Registry

http://fulxie.0fees.us/?type=reference&ckattempt=1

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 1,2023. Web-based tool for evaluating and screening reference genes from extensive experimental datasets. It integrates major computational programs (geNorm, Normfinder, BestKeeper, and the comparative delta-Ct method) to compare and rank the tested candidate reference genes. Based on the rankings from each program, it assigns an appropriate weight to an individual gene and calculated the geometric mean of their weights for the overall final ranking., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: RefFinder (RRID:SCR_000472) Copy   

•   Source: SciCrunch Registry

http://deweylab.biostat.wisc.edu/rsem/

Software package for quantifying gene and isoform abundances from single end or paired end RNA Seq data. Accurate transcript quantification from RNA Seq data with or without reference genome. Used for accurate quantification of gene and isoform expression from RNA-Seq data.

Proper citation: RSEM (RRID:SCR_000262) Copy   

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http://teddy.epi.usf.edu/

International consortium of six centers assembled to participate in the development and implementation of studies to identify infectious agents, dietary factors, or other environmental agents, including psychosocial factors, that trigger type 1 diabetes in genetically susceptible people. The coordinating centers recruit and enroll subjects, obtaining informed consent from parents prior to or shortly after birth, genetic and other types of samples from neonates and parents, and prospectively following selected neonates throughout childhood or until development of islet autoimmunity or T1DM. The study tracks child diet, illnesses, allergies and other life experiences. A blood sample is taken from children every 3 months for 4 years. After 4 years, children will be seen every 6 months until the age of 15 years. Children are tested for 3 different autoantibodies. The study will compare the life experiences and blood and stool tests of the children who get autoantibodies and diabetes with some of those children who do not get autoantibodies or diabetes. In this way the study hopes to find the triggers of T1DM in children with higher risk genes.

Proper citation: TEDDY (RRID:SCR_000383) Copy   

•   Source: SciCrunch Registry

http://www.epilepsygenetics.eu/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 16,2023. Group of clinical care and epilepsy research centers who are committed to improving the lives of people with epilepsy through an understanding of the genetics of epilepsy. The consoritum was in an effort to speed discovery to epilepsy genetics by pooling the resources of several research centres., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: EPIGEN (RRID:SCR_000093) Copy   

•   Source: SciCrunch Registry

http://discover.nci.nih.gov/gominer/GoCommandWebInterface.jsp

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. A web program that organizes lists of genes of interest (for example, under- and overexpressed genes from a microarray experiment) for biological interpretation in the context of the Gene Ontology and automates the analysis of multiple microarrays then integrates the results across all of them in exportable output files and visualizations. High-Throughput GoMiner is an enhancement of GoMiner and is implemented with both a command line interface and a web interface. The program can also: efficiently perform automated batch processing of an arbitrary number of microarrays; produce a human- or computer-readable report that rank-orders the multiple microarray results according to the number of significant GO categories; integrate the multiple microarray results by providing organized, global clustered image map visualizations of the relationships of significant GO categories; provide a fast form of false discovery rate multiple comparisons calculation; and provide annotations and visualizations for relating transcription factor binding sites to genes and GO categories.

Proper citation: High-Throughput GoMiner (RRID:SCR_000173) 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.flybrain.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Interactive database of Drosophila melanogaster nervous system. Used by drosophila neuroscience community and by other researchers studying arthropod brain structure.

Proper citation: FlyBrain (RRID:SCR_000706) Copy   

•   Source: SciCrunch Registry