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http://great.stanford.edu/public/html/splash.php

Data analysis service that predicts functions of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. The utility of GREAT extends to data generated for transcription-associated factors, open chromatin, localized epigenomic markers and similar functional data sets, and comparative genomics sets. Platform: Online tool

Proper citation: GREAT: Genomic Regions Enrichment of Annotations Tool (RRID:SCR_005807) Copy   

•   Source: SciCrunch Registry

http://www.hormone.org/

A portal for hormone-related health information for the public, physicians, allied health professionals and the media. It serves as a resource for the public by promoting the prevention, treatment and cure of hormone-related conditions through outreach and education. It provides free educational materials, public forums, physician referral service, and media education campaigns. It offers a library of educational materials and programs covering a wide range of endocrine topics, including adrenal disorders, breast cancer, diabetes, osteoporosis, stress, thyroid disease and cancer.

Proper citation: Hormone Health Network (RRID:SCR_005765) Copy   

•   Source: SciCrunch Registry

https://www.google.com/docs/about/

Authoring tool to create, share, and collaborate on the web with documents, spreadsheets, presentations, and more in real time. All your changes are saved automatically in Drive.

Proper citation: Google Docs (RRID:SCR_005886) Copy   

•   Source: SciCrunch Registry

http://manuals.bioinformatics.ucr.edu/home/R_BioCondManual#GOHyperGAll

To test a sample population of genes for overrepresentation of GO terms, the R/BioC function GOHyperGAll computes for all GO nodes a hypergeometric distribution test and returns the corresponding p-values. A subsequent filter function performs a GO Slim analysis using default or custom GO Slim categories. Basic knowledge about R and BioConductor is required for using this tool. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GOHyperGAll (RRID:SCR_005766) Copy   

•   Source: SciCrunch Registry

https://gene-atlas.brainminds.jp/

Database of gene expression in the marmoset brain.Comparative anatomy of marmoset and mouse cortex from genomic expression. Atlas comparing brain of neonatal marmoset with mouse using in situ hybridization.

Proper citation: Expression Atlas of the Marmoset (RRID:SCR_005760) Copy   

•   Source: SciCrunch Registry

http://www.vectorbase.org

Bioinformatics Resource Center for invertebrate vectors. Provides web-based resources to scientific community conducting basic and applied research on organisms considered potential agents of biowarfare or bioterrorism or causing emerging or re-emerging diseases.

Proper citation: VectorBase (RRID:SCR_005917) Copy   

•   Source: SciCrunch Registry

https://adaptivedisclosure.wordpress.com/aida/

A generic set of components that can perform a variety of tasks, such as learn new pattern recognition models, perform specialized search on resource collections, and store knowledge in a repository. W3C standards are used to make data accessible and manageable with semantic web technologies such as OWL, RDF(S), and SKOS. The AIDA Toolkit is directed at groups of knowledge workers that cooperatively search, annotate, interpret, and enrich large collections of heterogeneous documents from diverse locations. The server offers services for: text indexing and statistics, metadata storage and querying, thesaurus reasoning, annotation, text retrieval, spelling correction, synonym detection, and model learning.

Proper citation: AIDA Toolkit (RRID:SCR_005914) Copy   

•   Source: SciCrunch Registry

http://llama.mshri.on.ca/

The Roth Laboratory is designing and interpreting large-scale experiments to understand pathway structure and its relationship to phenotype and human disease. Software for research focused on a specific research goal is available. Current experimental interests: * Exploiting parallel sequencing technology to phenotype all pairwise gene deletion combinations in S. cerevisiae, with initial application to genes involved in transcription. * Generation of S. cerevisiae strains carrying dozens of chosen targeted deletions, with initial application to delete all ABC transporters imparting multidrug resistance. * Targeted insertion of gene sets encoding entire human pathways into S. cerevisiae, with initial application to genes involved in drug metabolism. Current computational interests: * Systematic analysis of genetic interaction to reveal redundant systems and order of action in genetic pathways * Integrating large-scale studies - including phenotype, genetic epistasis, protein-protein and transcription-regulatory interactions and sequence patterns - to quantitatively assign function to genes and guide experimentation and disease association studies. * Alternative splicing and its relationship to protein interaction networks.

Proper citation: Roth Laboratory (RRID:SCR_005711) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/Tools/pfa/iprscan/

Software package for functional analysis of sequences by classifying them into families and predicting presence of domains and sites. Scans sequences against InterPro's signatures. Characterizes nucleotide or protein function by matching it with models from several different databases. Used in large scale analysis of whole proteomes, genomes and metagenomes. Available as Web based version and standalone Perl version and SOAP Web Service.

Proper citation: InterProScan (RRID:SCR_005829) Copy   

•   Source: SciCrunch Registry

http://genemania.org/

Data analysis service to predict the function of your favorite genes and gene sets. Indexing 1,421 association networks containing 266,984,699 interactions mapped to 155,238 genes from 7 organisms. GeneMANIA interaction networks are available for download in plain text format. GeneMANIA finds other genes that are related to a set of input genes, using a very large set of functional association data. Association data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity. You can use GeneMANIA to find new members of a pathway or complex, find additional genes you may have missed in your screen or find new genes with a specific function, such as protein kinases. Your question is defined by the set of genes you input. If members of your gene list make up a protein complex, GeneMANIA will return more potential members of the protein complex. If you enter a gene list, GeneMANIA will return connections between your genes, within the selected datasets. GeneMANIA suggests annotations for genes based on Gene Ontology term enrichment of highly interacting genes with the gene of interest. GeneMANIA is also a gene recommendation system. GeneMANIA is also accessible via a Cytoscape plugin, designed for power users. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GeneMANIA (RRID:SCR_005709) Copy   

•   Source: SciCrunch Registry

http://bishopw.loni.ucla.edu/AIR5/

A tool for automated registration of 3D (and 2D) images within and across subjects and within and sometimes across imaging modalities. The AIR library can easily incorporate automated image registration into site specific programs adapted to your particular needs.

Proper citation: Automated Image Registration (RRID:SCR_005944) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/webservices/whatizit/info.jsf

A text processing system that allows you to do textmining tasks on text. It is great at identifying molecular biology terms and linking them to publicly available databases. Whatizit is also a Medline abstracts retrieval/search engine. Instead of providing the text by Copy&Paste, you can launch a Medline search. The abstracts that match your search criteria are retrieved and processed by a pipeline of your choice. Whatizit is also available as 1) a webservice and as 2) a streamed servlet. The webservice allows you to enrich content within your website in a similar way as in the wikipedia. The streamed servlet allows you to process large amounts of text.

Proper citation: Whatizit (RRID:SCR_005824) Copy   

•   Source: SciCrunch Registry

http://umbbd.msi.umn.edu/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 27, 2014. Database containing information on microbial biocatalytic reactions and biodegradation pathways for primarily xenobiotic, chemical compounds. Its goal is to provide information on microbial enzyme-catalyzed reactions that are important for biotechnology. The reactions covered are studied for basic understanding of nature, biocatalysis leading to specialty chemical manufacture, and biodegradation of environmental pollutants. Individual reactions and metabolic pathways are presented with information on the starting and intermediate chemical compounds, the organisms that transform the compounds, the enzymes, and the genes. The present database has been successfully used to teach enzymology and use of biochemical Internet information resources to advanced undergraduate and graduate students, and is being expanded primarily with the help of such students. In addition to reactions and pathways, this database also contains Biochemical Periodic Tables and a Pathway Prediction System. * Search the UM-BBD for compound, enzyme, microorganism, pathway, or BT rule name; chemical formula; chemical structure; CAS Registry Number; or EC code. * Go to Pathways and Metapathways in the UM-BBD * Lists of 203 pathways; 1400 reactions; 1296 compounds; 916 enzymes; 510 microorganism entries; 245 biotransformation rules; 50 organic functional groups; 76 reactions of naphthalene 1,2-dioxygenase; 109 reactions of toluene dioxygenase; Graphical UM-BBD Overview; and Other Graphics (Metapathway and Pathway Maps and Reaction Mechanisms).

Proper citation: UM-BBD (RRID:SCR_005787) Copy   

•   Source: SciCrunch Registry

http://bio-bigdata.hrbmu.edu.cn/diseasemeth/

Human disease methylation database. DiseaseMeth version 2.0 is focused on aberrant methylomes of human diseases. Used for understanding of DNA methylation driven human diseases.

Proper citation: DiseaseMeth (RRID:SCR_005942) Copy   

•   Source: SciCrunch Registry

http://distild.jensenlab.org/

The DistiLD database aims to increase the usage of existing genome-wide association studies (GWAS) results by making it easy to query and visualize disease-associated SNPs and genes in their chromosomal context. The database performs three important tasks: # published GWAS are collected from several sources and linked to standardized, international disease codes ICD10 codes) # data from the International HapMap Project are analyzed to define linkage disequilibrium (LD) blocks onto which SNPs and genes are mapped # the web interface makes it easy to query and visualize disease-associated SNPs and genes within LD blocks. Users can query the database by diseases, SNPs or genes. No matter which of the three query modes was used, an intermediate page will be shown listing all the studies that matched the search with a link to the corresponding publication. The user can select either all studies related to a certain disease or one specific study for which to view the related LD blocks. The DistiLD resource integrates information on: * Associations between Single Nucleotide Polymorphisms (SNPs) and diseases from genome-wide association studies (GWAS) * Links between SNPs and genes based on linkage disequilibrium (LD) data from HapMap For convenience, we provide the complete datasets as two (zipped) tab-delimited files. The first file contains GWAS results mapped to LD blocks. The second file contains all SNPs and genes assigned to each LD block.

Proper citation: DistiLD - Diseases and Traits in LD (RRID:SCR_005943) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

http://genome.ucsc.edu/

Portal to interactively visualize genomic data. Provides reference sequences and working draft assemblies for collection of genomes and access to ENCODE and Neanderthal projects. Includes collection of vertebrate and model organism assemblies and annotations, along with suite of tools for viewing, analyzing and downloading data.

Proper citation: UCSC Genome Browser (RRID:SCR_005780) Copy   

•   Source: SciCrunch Registry

http://vbrc.org/index.asp

One of eight Bioinformatics Resource Centers nationwide providing comprehensive web-based genomics resources including a relational database and web application supporting data storage, annotation, analysis, and information exchange to support scientific research directed at viruses belonging to the Arenaviridae, Bunyaviridae, Filoviridae, Flaviviridae, Paramyxoviridae, Poxviridae, and Togaviridae families. These centers serve the scientific community and conduct basic and applied research on microorganisms selected from the NIH/NIAID Category A, B, and C priority pathogens that are regarded as possible bioterrorist threats or as emerging or re-emerging infectious diseases. The VBRC provides a variety of analytical and visualization tools to aid in the understanding of the available data, including tools for genome annotation, comparative analysis, whole genome alignments, and phylogenetic analysis. Each data release contains the complete genomic sequences for all viral pathogens and related strains that are available for species in the above-named families. In addition to sequence data, the VBRC provides a curation for each virus species, resulting in a searchable, comprehensive mini-review of gene function relating genotype to biological phenotype, with special emphasis on pathogenesis.

Proper citation: VBRC (RRID:SCR_005971) Copy   

•   Source: SciCrunch Registry

https://biit.cs.ut.ee/

The Bioinformatics, Algorithmics, and Data Mining group BIIT lead by prof. Jaak Vilo is a joint research group between the Department of Computer Science (University of Tartu), Quretec, and the Estonian Biocenter. Our main research topics and capabilities include the gene regulation, gene expression data analysis, biological data mining, systems biology, combinatorial pattern matching, developing software for biomedical research databases, as well as partnering in stem cell and cancer related projects. Software * MEM - Multi-Experiment-Matrix -- large-scale gene expression data queries and mining (Genome Biology 2009) * g:Profiler family of tools for functional assessment of gene groups, gene ID mappings, orthology and expression similarity searches. (NAR web server issue 2007) * KEGGanim - visualisation of high-throughput data on biological pathway charts (Bioinformatics, 2007) * GraphWeb - a tool for mining large biological networks (NAR Web server issue 2008) * FunGenES data atlas * More software tools

Proper citation: BIIT - Bioinformatics Algorithmics and Data Mining Group (RRID:SCR_005690) Copy   

•   Source: SciCrunch Registry

http://bioinfo.cipf.es/

Biomedicine can only be understood in the context of genomics and with the concourse of bioinformatics. Our department aims to tackle biomedical problems from a system's biology perspective. Following this, the general objective we seek through the main lines of research is to relate the mutations (Pharmacogenomics and Comparative Genomics) to their effect at cellular and phenotypic level (Functional Genomics) trying to understand the mechanism of action (Structural Genomics). Systems Biology Genes operate within an intricate network of interactions that we have only recently started to envisage. Many higher-order levels of interaction are continuously being discovered. In this scenario we are interested in developing methods and tools which can help to understand large-scale experiments from a systems biology perspective. Comparative genomics We are interested in the analysis of patterns and processes occurred during the evolution of our genome, and in the application of the evolutionary thought in human health and disease. * Adaptive Human Evolution * Evolutionary Pharmacogenetics * SNP's and Human Disease Structural genomics Our Unit aims to develop and apply computational methods for understanding the molecular mechanisms of cell regulation beyond proteins. In particular, we apply our methods to study the interaction of small chemical compounds with proteins and to characterize their molecular actions. We are also developing methods for RNA 3D structure prediction with the aim of applying them to understand the effects of non-coding RNA molecules. Finally, in collaboration with experimentalists, we are working in determining the first ever 3D structure of a genomic domain in human.

Proper citation: CIPF Bioinformatics and Genomics Department (RRID:SCR_005692) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/devel/bioc/html/SeqGSEA.html

Software package that provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene''s differential expression and splicing, respectively.

Proper citation: SeqGSEA (RRID:SCR_005724) Copy   

•   Source: SciCrunch Registry