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http://homes.gersteinlab.org/Khurana-PLoSCompBio-2013/

Software for an integrated network combining multiple biological network database sources into a single human protein interactome. The software package contains gene interaction pairs corresponding to the unified global network.

Proper citation: MultiNet (RRID:SCR_016149) Copy   

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https://github.com/manveru/tkgo

Tk-GO is a GUI wrapping the basic functions of the GO AppHandle library from BDGP. GO terms are presented in an explorer-like browser, and behavior can be configured by altering Perl scripts. All available documentation is included in the download. Tk-GO uses the GO database (connects directly to the BDGP database by default) but is user-configurable. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Tk-GO (RRID:SCR_008855) Copy   

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http://spotfire.tibco.com/

The Spotfire Gene Ontology Advantage Application integrates GO annotations with gene expression analysis in Spotfire DecisionSite for Functional Genomics. Researchers can select a subset of genes in DecisionSite visualizations and display their distribution in the Gene Ontology hierarchy. Similarly, selection of any process, function or cellular location in the Gene Ontology hierarchy automatically marks the corresponding genes in DecisionSite visualizations. Platform: Windows compatible

Proper citation: Spotfire (RRID:SCR_008858) Copy   

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https://wiki.nci.nih.gov/display/caGWAS/caGWAS

Too that allows researchers to integrate, query, report, and analyze significant associations between genetic variations and disease, drug response or other clinical outcomes. SNP array technologies make it possible to genotype hundreds of thousands of single nucleotide polymorphisms (SNPs) simultaneously, enabling whole genome association studies. Within the Clinical Genomic Object Model (CGOM), the caIntegrator team created a domain model for Whole Genome Association Study Analysis. CGOM-caGWAS is a A semantically annotated domain model that captures associations between Study, Study Participant, Disease, SNP Association Analysis, SNP Population Frequency and SNP annotations. caGWAS APIs and web portal provide: * a semantically annotated domain model, database schema with sample data, seasoned middleware, APIs, and web portal for GWAS data; * platform and disease agnostic CGOM-caGWAS model and associated APIs; * the opportunity for developers to customize the look and feel of their GWAS portal; * a foundation of open source technologies; * a well-tested and performance-enhanced platform, as the same software is being used to house the CGEMS data portal; * accelerated analysis of results from various biomedical studies; and * a single application through which researchers and bioinformaticians can access and analyze clinical and experimental data from a variety of data types, as caGWAS objects are part of the CGOM, which includes microarray, genomic, immunohistochemistry, imaging, and clinical data.

Proper citation: caGWAS (RRID:SCR_009617) Copy   

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http://www.illumina.com/

American company incorporated that develops, manufactures and markets integrated systems for the analysis of genetic variation and biological function. Provides a line of products and services that serve the sequencing, genotyping and gene expression and proteomics markets. Its headquarters are located in San Diego, California.

Proper citation: Illumina (RRID:SCR_010233) Copy   

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http://www.gene-quantification.de/bestkeeper.html

Excel-based tool using pair-wise correlations for determination of stable housekeeping genes, differentially regulated target genes and sample integrity. It determines the best suited standards, out of ten candidates, and combines them into an index. The index can be compared with further ten target genes to decide, whether they are differentially expressed under an applied treatment. All data processing is based on crossing points.

Proper citation: BestKeeper (RRID:SCR_003380) Copy   

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http://genecloud.org/

Gene Cloud is a novel tool presenting gene-gene associations based on the scientific literature. It was developed by the Knockout Mouse Repository (www.komp.org) to help our customers find products related to other products they chose. We have built a detailed graph model of gene-gene associations based on how many times two genes are cited in the same article. If two genes are cited in many papers together, they are considered strongly connected. Each instance of Gene Cloud is centered around a specific gene. A list of the top most related genes is plotted as a branching structure from the center. A secondary branch can occur if a gene in the graph is more related a non-central gene than it is to the center gene. The font size of a branched gene indicates the relative strength of connection--always to the center gene. The distribution of genes in space is randomized each time Gene Cloud is run so a different picture will result for the same central gene. Color is used to indicate the availability of Knockout Mouse products at the KOMP Repository. If a gene is colored green in the graph there are products (mutant ES cells, sperm, embryos, or mice) ready to be ordered. Blue colored genes do not yet have products available, but you can follow the links back to the KOMP Repository and register interest to be alerted when products do become available. Gene Cloud is driven by a database of gene-gene associations that currently contains 82,000 genes and other biotypes, 113,000 annotated publications, and 467 million connections. The latest gene symbols, names and gene-publication annotation information is updated daily from the Mouse Genome Informatics database. The graphing is accomplished through the use of a modified version of jsViz.

Proper citation: Gene Cloud: Exploring Connections in the Mouse Genome (RRID:SCR_003503) Copy   

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http://www.c2b2.columbia.edu/danapeerlab/html/jistic.html

Software tool for analyzing datasets of genome-wide copy number variation to identify driver aberrations in cancer.

Proper citation: JISTIC (RRID:SCR_003482) Copy   

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http://snpeff.sourceforge.net/

Genetic variant annotation and effect prediction software toolbox that annotates and predicts effects of variants on genes (such as amino acid changes). By using standards, such as VCF, SnpEff makes it easy to integrate with other programs.

Proper citation: SnpEff (RRID:SCR_005191) Copy   

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http://www.ebi.ac.uk/Rebholz-srv/ebimed/

A web application that combines Information Retrieval and Extraction from Medline. EBIMed finds Medline abstracts in the same way PubMed does. Then it goes a step beyond and analyses them to offer a complete overview on associations between UniProt protein/gene names, GO annotations, Drugs and Species. The results are shown in a table that displays all the associations and links to the sentences that support them and to the original abstracts. By selecting relevant sentences and highlighting the biomedical terminology EBIMed enhances your ability to acquire knowledge, relate facts, discover implications and, overall, have a good overview economizing the effort in reading.

Proper citation: EBIMed (RRID:SCR_005314) Copy   

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http://ikmbio.csie.ncku.edu.tw/coin/home.php

A web-based system that assess articles according to their term correlations among sentences. It employs the co-occurrence relations and their network centralities to evaluate the influence of biomedical terms from Comparative Toxicogenomics Database (CTD)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: CoIN (RRID:SCR_005332) Copy   

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http://en.wikipedia.org/wiki/Gene_Wiki

The Gene Wiki is a project that facilitates transferring information on human genes to Wikipedia article stubs with the goal of promoting collaboration and expansion of the articles. Number of gene articles The human genome contains an estimated 20,00025,000 protein-coding genes. The goal of the Gene Wiki project is to create seed articles for every notable human gene, that is, every gene whose function has been assigned in the peer-reviewed scientific literature. Approximately half of human genes have assigned function, therefore the total number of articles seeded by the Gene Wiki project would be expected to be in the range of 10,000 - 15,000. To date, approximately 10,271 articles have been created or augmented to include Gene Wiki project content. Expansion Once seed articles have been established, the hope and expectation is that these will be annotated and expanded by editors ranging in experience from the lay audience to students to professionals and academics. Proteins encoded by genes The majority of genes encode proteins hence understanding the function of a gene generally requires understanding of the function of the corresponding protein. In addition to including basic information about the gene, the project therefore also includes information about the protein encoded by the gene. Stubs for the Gene Wiki project are created by a bot and contain links to the following primary gene/protein databases * HUGO Gene Nomenclature Committee official gene name * Entrez Gene database * OMIM (Mendelian Inheritance in Man) database that catalogues all the known diseases with a genetic component * Amigo Gene Ontology * HomoloGene gene homologs in other species * SymAtlasRNA gene expression pattern in tissues * Protein Data Bank 3D structure of protein encoded by the gene * Uniprot (universal protein resource) a central repository of protein data

Proper citation: Gene Wiki (RRID:SCR_005317) Copy   

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http://manatee.sourceforge.net/

Manatee is a web-based gene evaluation and genome annotation tool; Manatee can store and view annotation for prokaryotic and eukaryotic genomes. The Manatee interface allows biologists to quickly identify genes and make high quality functional assignments, such as GO classifications, using search data, paralogous families, and annotation suggestions generated from automated analysis. Manatee can be downloaded and installed to run under the CGI area of a web server, such as Apache. Platform: Online tool, Linux compatible, Solaris

Proper citation: Manatee (RRID:SCR_005685) Copy   

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https://github.com/transmart

tranSMART is a knowledge management platform that enables scientists to develop and refine research hypotheses by investigating correlations between genetic and phenotypic data, and assessing their analytical results in the context of published literature and other work. tranSMART is licensed through GPL 3. The integration, normalization, and alignment of data in tranSMART permits users to explore data very efficiently to formulate new research strategies. Some of tranSMART''s specific applications include: * Revalidating previous hypotheses * Testing and refining novel hypotheses * Conducting cross-study meta-analysis * Searching across multiple data sources to find associations of concepts, such as a gene''s involvement in biological processes or experimental results * Comparing biological processes and pathways among multiple data sets from related diseases or even across multiple therapeutic areas Data Repository The tranSMART Data Repository combines a data warehouse with access to federated sources of open and commercial databases. tranSMART accommodates: * Phenotypic data, such as demographics, clinical observations, clinical trial outcomes, and adverse events * High content biomarker data, such as gene expression, genotyping, pharmacokinetic and pharmaco-dynamics markers, metabolomics data, and proteomics data * Unstructured text-data, such as published journal articles, conference abstracts and proceedings, and internal studies and white papers * Reference data from sources such as MeSH, UMLS, Entrez, GeneGo, Ingenuity, etc. * Metadata providing context about datasets, allowing users to assess the relevance of results delivered by tranSMART Data in tranSMART is aligned to allow identification and analysis of associations between phenotypic and biomarker data, and it is normalized to conform with CDISC and other standards to facilitate search and analysis across different data sources. tranSMART also enables investigators to search published literature and other text sources to evaluate their analysis in the context of the broader universe of reported research. External data can also be integrated into the tranSMART data repository, either from open data projects like GEO, EBI Array Express, GCOD, or GO, or from commercially available data sources. Making data accessible in tranSMART enables organizations to leverage investments in manual curation, development costs of automated ETL tools, or commercial subscription fees across multiple research groups. Dataset Explorer tranSMART''s Dataset Explorer provides flexible, powerful search and analysis capabilities. The core of the Dataset Explorer integrates and extends the open source i2b2 application, Lucene text indexing, and GenePattern analytical tools. Connections to other open source and commercial analytical tools such as Galaxy, Integrative Genomics Viewer, Plink, Pathway Studio, GeneGo, Spotfire, R, and SAS can be established to expand tranSMART''s capabilities. tranSMART''s design allows organizations flexibility in selecting analytical tools accessible through the Dataset Explorer, and provides file export capabilities to enable researchers to use tools not accessible in the tranSMART portal.

Proper citation: tranSMART (RRID:SCR_005586) Copy   

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http://www.softpedia.com/get/Science-CAD/DynGO.shtml

DynGO is a client-server application that provides several advanced functionalities in addition to the standard browsing capability. DynGO allows users to conduct batch retrieval of GO annotations for a list of genes and gene products, and semantic retrieval of genes and gene products sharing similar GO annotations (which requires more disk and memory to handle the semantic retrieval). The result are shown in an association tree organized according to GO hierarchies and supported with many dynamic display options such as sorting tree nodes or changing orientation of the tree. For GO curators and frequent GO users, DynGO provides fast and convenient access to GO annotation data. DynGO is generally applicable to any data set where the records are annotated with GO terms, as illustrated by two examples. Requirements: Java Platform: Windows compatible, Linux compatible, Unix compatible

Proper citation: DynGO (RRID:SCR_007009) Copy   

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http://www.bioconductor.org/packages/release/bioc/html/rbsurv.html

Software package that selects genes associated with survival.

Proper citation: rbsurv (RRID:SCR_001175) Copy   

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http://sherlock.ucsf.edu/

Service to discover disease genes in GWAS using eQTL signature matching by simply submitting your list of GWAS associations (SNPs and p-values). It is important to upload all SNPs in your association study, not just the top hits. Sherlock may be able to group multiple lower-confidence SNPs to discover functionally-important genes.

Proper citation: Sherlock (RRID:SCR_001628) Copy   

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http://www-personal.umich.edu/~jianghui/rseqdiff/

An R package that can detect differential gene and isoform expressions from RNA-seq data of multiple biological conditions. The approach considers three cases for each gene: 1) no differential expression, 2) differential expression without differential splicing and 3) differential splicing.

Proper citation: rSeqDiff (RRID:SCR_001683) Copy   

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https://cran.r-project.org/src/contrib/Archive/QuasiSeq/

Software package to apply the QL, QLShrink and QLSpline methods to quasi-Poisson or quasi-negative binomial models for identifying differentially expressed genes in RNA-seq data.

Proper citation: QuasiSeq (RRID:SCR_001715) Copy   

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http://incf.org/about/programs/modeling/blue-gene-access

Through this site, INCF provides he neuroinformatics community with access to an IBM Blue Gene/L supercomputer. INCF owns a share of a BlueGene/L (BG/L) supercomputer located at the Parallel Computer Center (PDC) at The Royal Institute of Technology (KTH) in Stockholm. Allocations are now available through the INCF Secretariat. During an initial evaluation phase, a limited numbers of large-scale computing projects will be selected, based on the suitability of the project for supercomputing. Research groups with limited access to supercomputers at their home institutions are given priority. Approved projects are regularly re-evaluated. New projects are approved based on availability and usage load of the BG/L. The Blue Gene/L supercomputer project is aimed at expanding the horizon of high-performance computing to unprecedented levels of scale and performance. Blue Gene/L is the first supercomputer in the Blue Gene family. The full Blue Gene/L consists of 64 racks containing 65,536 high-performance compute nodes. Each node (nodes and chips are the same in the Blue Gene system) contains two embedded 32-bit PowerPC processors. Furthermore, the same chip that is used for compute nodes is also used for the 1,024 I/O nodes. A three-dimensional torus network and a collective network are used to interconnect all nodes. The full system contains 33 terabytes of main memory; it is designed to achieve 183.5 teraflops peak performance using one of the processors of each node for computation and the other processor for communication, and 367 teraflops using both processors for computation. Another key architectural feature of this supercomputer is the link chip component and five Blue Gene/L networks, the PowerPC 440 core and floating-point enhancements, the on-chip and off-chip distributed memory system, the node- and system-level design for high reliability, and the comprehensive approach to fault isolation. One of the key objectives in Blue Gene/L design is to achieve cost/performance comparable to the COTS (Commodity Off The Shelf) approach, while at the same time incorporating a processor and network combination so powerful that it revolutionizes the performance of supercomputer systems. Sponsors: This resource is supported by the INCF.

Proper citation: International Neuroinformatics Coordinating Facility: Blue Gene/L Access (RRID:SCR_001755) Copy   

•   Source: SciCrunch Registry