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http://www.yeastgenome.org/cgi-bin/GO/goSlimMapper.pl

The GO Slim Mapper (aka GO Term Mapper) maps the specific, granular GO terms used to annotate a list of budding yeast gene products to corresponding more general parent GO slim terms. Uses the SGD GO Slim sets. Three GO Slim sets are available at SGD: * Macromolecular complex terms: protein complex terms from the Cellular Component ontology * Yeast GO-Slim: GO terms that represent the major Biological Processes, Molecular Functions, and Cellular Components in S. cerevisiae * Generic GO-Slim: broad, high level GO terms from the Biological Process and Cellular Component ontologies selected and maintained by the Gene Ontology Consortium (GOC) Platform: Online tool

Proper citation: SGD Gene Ontology Slim Mapper (RRID:SCR_005784) Copy   

•   Source: SciCrunch Registry

http://gtlinker.cnb.csic.es/

Web application that filters and links enriched output data identifying sets of associated genes and terms, producing metagroups of coherent biological significance. The method uses fuzzy reciprocal linkage between genes and terms to unravel their functional convergence and associations. It can also be accessed through its web service.

Proper citation: GeneTerm Linker (RRID:SCR_006385) Copy   

•   Source: SciCrunch Registry

http://chgr.mc.vanderbilt.edu/page/gist

Software package to test if a marker can account in part for the linkage signal in its region. There are two versions of the software: Windows and Linux/Unix.

Proper citation: Genotype-IBD Sharing Test (RRID:SCR_006257) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.intec.ugent.be/magic/

Web based interface for exploring and analyzing a comprehensive maize-specific cross-platform expression compendium. This compendium was constructed by collecting, homogenizing and formally annotating publicly available microarrays from Gene Expression Omnibus (GEO), and ArrayExpress.

Proper citation: Magic (RRID:SCR_006406) Copy   

•   Source: SciCrunch Registry

http://omniBiomarker.bme.gatech.edu

omniBiomarker is a web-application for analysis of high-throughput -omic data. Its primary function is to identify differentially expressed biomarkers that may be used for diagnostic or prognostic clinical prediction. Currently, omniBiomarker allows users to analyze their data with many different ranking methods simultaneously using a high-performance compute cluster. The next release of omniBiomarker will automatically select the most biologically relevant ranking method based on user input regarding prior knowledge. The omniBiomarker workflow * Data: Gene Expression * Algorithms: Knowledge-Driven Gene Ranking * Differentially expressed Genes * Clinical / Biological Validation * Knowledge: NCI Thesaurus of Cancer, Cancer Gene Index * back to Algorithms

Proper citation: omniBiomarker (RRID:SCR_005750) Copy   

•   Source: SciCrunch Registry

http://amp.pharm.mssm.edu/l2n/upload/register.php

A web-based software system that allows users to upload lists of mammalian genes/proteins onto a server-based program for integrated analysis. The system includes web-based tools to manipulate lists with different set operations, to expand lists using existing mammalian networks of protein-protein interactions, co-expression correlation, or background knowledge co-annotation correlation, as well as to apply gene-list enrichment analyses against many gene-list libraries of prior biological knowledge such as pathways, gene ontology terms, kinase-substrate, microRNA-mRAN, and protein-protein interactions, metabolites, and protein domains. Such analyses can be applied to several lists at once against many prior knowledge libraries of gene-lists associated with specific annotations. The system also contains features that allow users to export networks and share lists with other users of the system.

Proper citation: Lists2Networks (RRID:SCR_006323) Copy   

•   Source: SciCrunch Registry

http://www003.upp.so-net.ne.jp/pub/publications.html#sl

Software application for inkage disequilibrium grouping of single nucleotide polymorphisms (SNPs) reflecting haplotype phylogeny for efficient selection of tag SNPs. (entry from Genetic Analysis Software)

Proper citation: LDGROUP (RRID:SCR_006282) Copy   

•   Source: SciCrunch Registry

http://xldb.fc.ul.pt/biotools/rebil/ssm/

FuSSiMeG is being discontinued, may not be working properly. Please use our new tool ProteinOn. Functional Semantic Similarity Measure between Gene Products (FuSSiMeG) provides a functional similarity measure between two proteins using the semantic similarity between the GO terms annotated with the proteins. Platform: Online tool

Proper citation: FuSSiMeG: Functional Semantic Similarity Measure between Gene-Products (RRID:SCR_005738) Copy   

•   Source: SciCrunch Registry

https://sc.edu/study/colleges_schools/pharmacy/research_and_practice/research_centers_and_facilities/functional_genomics_core/index.php

Core offers resources and solutions for conducting genomics, transcriptomics, epigenomics and functional genomics projects. We work with researchers to determine project goals and design custom solutions. We assist at all stages of the project, from support in grant development to generation of publication-quality data. Services include Consultations, Bioinformatics, Nucleic Acids purification, quantification and QC DNA Sequencing (SANGER and NGS), Library Constructions for NGS applications,Microarray Hybridization, Real Time PCR, Custom epigenomics applications, Lentiviral vectors and lentiviruses construction and production, CRIPR-CAS9sgRNAs and RNAi/shRNAs knockdown of individual genes and functional screening of sgRNA and shRNA libraries for target identification.

Proper citation: University of South Carolina Functional Genomics Core Facility (RRID:SCR_026178) 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

http://cran.r-project.org/web/packages/fcros/

A fold change ranks ordering statistics based software for detecting differentially expressed genes.

Proper citation: FCROS (RRID:SCR_006195) Copy   

•   Source: SciCrunch Registry

http://www2.warwick.ac.uk/fac/sci/systemsbiology/staff/ott/tools_and_software/wigwams

A computational tool for analyzing multiple gene expression time series data sets for the same organism. The goal is to determine if there is evidence for gene regulatory mechanisms that are shared by multiple different expression responses.

Proper citation: Wigwams (RRID:SCR_006400) Copy   

•   Source: SciCrunch Registry

http://www.affymetrix.com/support/developer/powertools/apt_archive.affx

Affymetrix Power Tools (APT) are a set of cross-platform command line programs that implement algorithms for analyzing and working with Affymetrix GeneChip arrays. APT programs are intended for power users who prefer programs that can be utilized in scripting environments and are sophisticated enough to handle the complexity of extra features and functionality. APT provides platform for developing and deploying new algorithms without waiting for the GUI implementations. This resource is supported by Affymetrix, Inc.

Proper citation: Affymetrix Power Tools (RRID:SCR_008401) Copy   

•   Source: SciCrunch Registry

http://portal.ncibi.org/gateway/gin.html

GIN-IE is a high precision system for extracting protein/gene interactions, interaction cue words, and directionality from the literature. Syntax-aware inferences about the roles of the entities are made by using the syntactic and dependency parse tree structures of the sentences. Negation and speculation are frequently occurring language phenomena that modify the factuality of the information contained in text. GIN-IE detects and distinguishes interactions that are extracted from negated or speculative sentences. GIN-IE has been integrated with the NCIBI PubMed daily update and processing pipeline. The extracted interactions are accessible through MimiWeb.

Proper citation: Gene Interaction Extraction from the Literature (RRID:SCR_008660) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

http://www.ingenuity.com/products/pathways_analysis.html

A web-based software application that enables users to analyze, integrate, and understand data derived from gene expression, microRNA, and SNP microarrays, metabolomics, proteomics, and RNA-Seq experiments, and small-scale experiments that generate gene and chemical lists. Users can search for targeted information on genes, proteins, chemicals, and drugs, and build interactive models of experimental systems. IPA allows exploration of molecular, chemical, gene, protein and miRNA interactions, creation of custom molecular pathways, and the ability to view and modify metabolic, signaling, and toxicological canonical pathways. In addition to the networks and pathways that can be created, IPA can provide multiple layering of additional information, such as drugs, disease genes, expression data, cellular functions and processes, or a researchers own genes or chemicals of interest.

Proper citation: Ingenuity Pathway Analysis (RRID:SCR_008653) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.litinspector.org/

A literature search tool providing gene and signal transduction pathway mining within NCBI''''s PubMed database. Its sophisticated gene recognition and intuitive color coding increase the readability of abstracts and lets you analyze signal transduction pathways, diseases and tissue associations in a snap. Note: LitInspector has become part of the Literature & Pathways module of the Genomatix Software Suite.

Proper citation: LitInspector (RRID:SCR_011870) Copy   

•   Source: SciCrunch Registry

http://www.dnaform.jp/products/cage_e.html

Expression profiling and promoter identification software tool for transcriptional network analysis and transcriptome characterization. DeepCAGE, the combination of next-generation sequencing with next generation expression profiling provides unsurpassed solutions for expression profiling and genome annotation. CAGE will be the experimental approach at need to link gene expression and control regions in the genome. With the availability of next-generation sequencing methods, DNAFORM now offers DeepCAGE services. DeepCAGE libraries are prepared for direct analysis by an Illumina/Solexa Sequencer. One sequencing run using one channel on an Illumina/Solexa Sequencer can yield in over 4,000,000 reads per sample. CAGE is based on our full-length cDNA library technology, where an adaptor is ligated to the 5''''-end of full-length cDNAs, which introduces a recognition site for a Class IIs restriction endonuclease adjacent to the 5''''-end of the cDNA. The Class IIs restriction endonuclease, here MmeI, allows for the cloning of short tags as derived from the 5''''-end of transcripts into concatemers for high-throughput sequencing. CAGE tags are further characterized by mapping to genomic sequences, which enables the identification of transcriptional start sites. As such CAGE can contribute to projects in Gene Discovery, Gene Expression, and Promoter Identification. After the genome sequencing projects have provided us with the genetic blueprints for many organisms, new questions have to be answered on how to correlate the observed genotypes with related phenotypes, and how to understand the regulation of genetic information in time and space. The dynamics of living systems and the functional behavior of cells in multicellular organisms has thus become the subject of the emerging field of system biology. Integration of experimental approaches and computer aided theories on a system level will be the fundamental principle to drive systems biology in order to understand the principles behind complex regulatory networks, which will be an ambitious goal requiring new approaches in life sciences. For ordering and additional information, please contact us under contact_at_dnaform.jp

Proper citation: CAGE (RRID:SCR_007574) Copy   

•   Source: SciCrunch Registry