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http://pga.gs.washington.edu/VH1.html

Software application for displaying estimated haplotype data (entry from Genetic Analysis Software)

Proper citation: VH (RRID:SCR_013402) Copy   

•   Source: SciCrunch Registry

http://www.stat.washington.edu/thompson/Genepi/Eclipse.shtml

A set of three programs, preproc, eclipse2 and eclipse3 which analyze genetic marker data for genotypic errors and pedigree errors. Using a single preprocessing program (preproc), eclipse2 analyzes data on pairs of individuals, and eclise3 analyzes data jointly on trios. (entry from Genetic Analysis Software)

Proper citation: ECLIPSE (RRID:SCR_013130) Copy   

•   Source: SciCrunch Registry

http://csg.sph.umich.edu//abecasis/GRR/

A graphical tool designed for detection of errors in relationship specification in general pedigrees by use of genome scan marker data. (entry from Genetic Analysis Software)

Proper citation: GRR (RRID:SCR_013496) Copy   

•   Source: SciCrunch Registry

http://www.stat.washington.edu/thompson/Genepi/Pedfiddler.shtml

Software suite of six programs that can be used as a stand-alone extension of the pedigree drawing facilities found in the publicly available version of PEDPACK. (entry from Genetic Analysis Software)

Proper citation: PEDFIDDLER (RRID:SCR_013376) Copy   

•   Source: SciCrunch Registry

http://pga.gs.washington.edu/VG2.html

Software program that presents complete raw datasets of individuals'' genotype data using a display format with samples as rows and polymorphisms as columns. The color code is: (1) blue: homozygous genotype for the common allele; (2) red: heterozygous genotype; (3) yellow: homozygous genotype for the rare allele; and (4) grey: missing data (entry from Genetic Analysis Software)

Proper citation: VG (RRID:SCR_013378) Copy   

•   Source: SciCrunch Registry

http://www.bios.unc.edu/~lin/software/tagIMPUTE/

A command-line program for the imputation of untyped SNPs. tagIMPUTE is based on a few flanking SNPs that can optimally predict the SNP under imputation. (entry from Genetic Analysis Software)

Proper citation: TAGIMPUTE (RRID:SCR_013338) Copy   

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http://www.dynacom.co.jp/u-tokyo.ac.jp/snphitlink/

Software program providing a useful pipeline to directly connect SNP data and linkage analysis program. SNP HiTLink currently supports the data from SNP chips provided by Affymetrix (Mapping 100k/500k array set, Genome-Wide Human SNP array 5.0/6.0) and Illumina (recently supported), carrying out typical linkage analysis programs of MLINK (FASTLINK/ LINKAGE package), Superlink, Merlin and Allegro. (entry from Genetic Analysis Software)

Proper citation: SNP HITLINK (RRID:SCR_013340) Copy   

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http://software.bfh-inst2.de/download3.html

Software application (entry from Genetic Analysis Software)

Proper citation: SGS (RRID:SCR_013460) Copy   

•   Source: SciCrunch Registry

http://www.helsinki.fi/~tsjuntun/autoscan/

A helper program to automate the tedious process of the creation of input files from genotype data of genome-wide scans (entry from Genetic Analysis Software)

Proper citation: AUTOSCAN (RRID:SCR_013510) Copy   

•   Source: SciCrunch Registry

http://amp.pharm.mssm.edu/X2K/

Software tool to produce inferred networks of transcription factors, proteins, and kinases predicted to regulate the expression of the inputted gene list by combining transcription factor enrichment analysis, protein-protein interaction network expansion, with kinase enrichment analysis. It provides the results as tables and interactive vector graphic figures.

Proper citation: eXpression2Kinases (RRID:SCR_016307) Copy   

•   Source: SciCrunch Registry

https://cibersort.stanford.edu/

Software tool to provide an estimation of the abundances of member cell types in a mixed cell population, using gene expression data. Used for characterizing cell composition of complex tissues from their gene expression profiles, large scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets.

Proper citation: CIBERSORT (RRID:SCR_016955) Copy   

•   Source: SciCrunch Registry

http://www.jax.org/mmrrc/

Center for mutant mouse research and distribution. The objectives of the JAX MMRRC are to: identify and evaluate biomedically-significant mice, import/acquire and archive mouse strains, distribute mouse strains, and operate a control program to ensure genetic stability.

Proper citation: Mutant Mouse Resource and Research Center - Jackson Laboratory (RRID:SCR_016446) Copy   

•   Source: SciCrunch Registry

http://gerg01.gsc.riken.jp/cage/mm5

A web system, which could search and display to current CAGE library information in CAGE Database.

Proper citation: CAGE Basic Viewer for Mus musculus (RRID:SCR_000451) Copy   

•   Source: SciCrunch Registry

http://anya.igsb.anl.gov/Geneways/GeneWays.html

System for automatically extracting, analzying, visualizing and integrating molecular pathway data from the research literature. System focuses on interactions between molecular substances and actions, providing a graphical consensus view on the collected information. GeneWays is designed as open platform, allowing researchers to query, review and critique integrated information.

Proper citation: GeneWays (RRID:SCR_000572) Copy   

•   Source: SciCrunch Registry

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

A gene finder based on a Generalized Hidden Markov Model (GHMM). Although the gene finder conforms to the overall mathematical framework of a GHMM, additionally it incorporates splice site models adapted from the GeneSplicer program and a decision tree adapted from GlimmerM. It also utilizes Interpolated Markov Models for the coding and noncoding models . Currently, GlimmerHMM's GHMM structure includes introns of each phase, intergenic regions, and four types of exons (initial, internal, final, and single).

Proper citation: GlimmerHMM (RRID:SCR_002654) Copy   

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http://bowtie-bio.sourceforge.net/recount/

RNA-seq gene count datasets built using the raw data from 18 different studies. The raw sequencing data (.fastq files) were processed with Myrna to obtain tables of counts for each gene. For ease of statistical analysis, they combined each count table with sample phenotype data to form an R object of class ExpressionSet. The count tables, ExpressionSets, and phenotype tables are ready to use and freely available. By taking care of several preprocessing steps and combining many datasets into one easily-accessible website, we make finding and analyzing RNA-seq data considerably more straightforward.

Proper citation: ReCount - A multi-experiment resource of analysis-ready RNA-seq gene count datasets (RRID:SCR_001774) Copy   

•   Source: SciCrunch Registry

http://www.ucl.ac.uk/cardiovasculargeneontology/

Full Gene Ontology annotation to genes associated with cardiovascular processes. Every GO annotation made, is attributed to an identified source, such as a publication identifier (PMID), and an indication of the type of evidence which supports the association between the gene product and the GO term. Over 4,000 cardiovascular associated genes have been identified. A variety of tools have been provided to enable cardiovascular scientists to review the annotation of their ''''favorite'''' gene and suggest information that may be missing, inaccurate or incomplete in these annotations. Annotation suggestions can be sent through the feedback form or by email. The Gene Ontology (GO) vocabulary is the established standard for the functional annotation of gene products. By using GO to curate scientific literature and by integrating results from high-quality high-throughput experiments they will create an information-rich resource for the cardiovascular-research community, enabling researchers to rapidly evaluate and interpret existing data and generate hypotheses to guide future research.

Proper citation: Cardiovascular Gene Ontology Annotation Initiative (RRID:SCR_004795) Copy   

•   Source: SciCrunch Registry

http://www.broad.mit.edu/mpr/lung

Data set of a molecular taxonomy of lung carcinoma, the leading cause of cancer death in the United States and worldwide. Using oligonucleotide microarrays, researchers analyzed mRNA expression levels corresponding to 12,600 transcript sequences in 186 lung tumor samples, including 139 adenocarcinomas resected from the lung. Hierarchical and probabilistic clustering of expression data defined distinct sub-classes of lung adenocarcinoma. Among these were tumors with high relative expression of neuroendocrine genes and of type II pneumocyte genes, respectively. Retrospective analysis revealed a less favorable outcome for the adenocarcinomas with neuroendocrine gene expression. The diagnostic potential of expression profiling is emphasized by its ability to discriminate primary lung adenocarcinomas from metastases of extra-pulmonary origin. These results suggest that integration of expression profile data with clinical parameters could aid in diagnosis of lung cancer patients.

Proper citation: Classification of Human Lung Carcinomas by mRNA Expression Profiling Reveals Distinct Adenocarcinoma Sub-classes (RRID:SCR_003010) Copy   

•   Source: SciCrunch Registry

http://www.cdc.gov/genomics/hugenet/default.htm

Human Genome Epidemiology Network, or HuGENet, is a global collaboration of individuals and organizations committed to the assessment of the impact of human genome variation on population health and how genetic information can be used to improve health and prevent disease. Its goals include: establishing an information exchange that promotes global collaboration in developing peer-reviewed information on the relationship between human genomic variation and health and on the quality of genetic tests for screening and prevention; providing training and technical assistance to researchers and practitioners interested in assessing the role of human genomic variation on population health and how such information can be used in practice; developing an updated and accessible knowledge base on the World Wide Web; and promoting the use of this knowledge base by health care providers, researchers, industry, government, and the public for making decisions involving the use of genetic information for disease prevention and health promotion. HuGENet collaborators come from multiple disciplines such as epidemiology, genetics, clinical medicine, policy, public health, education, and biomedical sciences. Currently, there are 4 HuGENet Coordinating Centers for the implementation of HuGENet activities: CDC''s Office of Public Health Genomics, Atlanta, Georgia; HuGENet UK Coordinating Center, Cambridge, UK; University of Ioannina, Greece; University of Ottawa , Ottawa, Canada. HuGENet includes: HuGE e-Journal Club: The HuGE e-Journal Club is an electronic discussion forum where new human genome epidemiologic (HuGE) findings, published in the scientific literature in the CDC''s Office of Public Health Genomics Weekly Update, will be abstracted, summarized, presented, and discussed via a newly created HuGENet listserv. HuGE Reviews: A HuGE Review identifies human genetic variations at one or more loci, and describes what is known about the frequency of these variants in different populations, identifies diseases that these variants are associated with and summarizes the magnitude of risks and associated risk factors, and evaluates associated genetic tests. Reviews point to gaps in existing epidemiologic and clinical knowledge, thus stimulating further research in these areas. HuGE Fact Sheets: HuGE Fact Sheets summarize information about a particular gene, its variants, and associated diseases. HuGE Case Studies: An on-line presentation designed to sharpen your epidemiological skills and enhance your knowledge on genomic variation and human diseases. Its purpose is to train health professionals in the practical application of human genome epidemiology (HuGE), which translates gene discoveries to disease prevention by integrating population-based data on gene-disease relationships and interventions. Students will acquire conceptual and practical tools for critically evaluating the growing scientific literature in specific disease areas. HUGENet Publications: Articles related to the HuGENet movement written by our HuGENet collaborators. HuGE Navigator: An integrated, searchable knowledge base of genetic associations and human genome epidemiology, including information on population prevalence of genetic variants, gene-disease associations, gene-gene and gene- environment interactions, and evaluation of genetic tests. HuGE Workshops: HuGENet has sponsored meetings and workshops with national and international partners since 2001. Available are detailed summaries, agendas or the ability to download speaker slides. HuGE Book: Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease. (The findings and conclusions in this book are those of the author(s) and do not necessarily represent the views of the funding agency.) HuGENet Collaborators: HuGENet is interested in establishing collaborations with individuals and organizations working on population based research involving genetic information. HuGE Funding: Funding opportunities for specific population-based genetic epidemiology research projects are available. Research initiatives whose aims include assessing the prevalence of human genetic variation, the association between genetic variants and human diseases, the measurement of gene-gene or gene-environment interaction, and the evaluation of genetic tests for screening and prevention are compiled to create a posted listing. Additional information and application details can be found by clicking on the respective links.

Proper citation: Human Genome Epidemiology Network (RRID:SCR_013117) Copy   

•   Source: SciCrunch Registry

http://david.abcc.ncifcrf.gov/content.jsp?file=/ease/ease1.htm&type=1

Windows(c) desktop software application, customizable and standalone, that facilitates the biological interpretation of gene lists derived from the results of microarray, proteomic, and SAGE experiments. Provides statistical methods for discovering enriched biological themes within gene lists, generates gene annotation tables, and enables automated linking to online analysis tools. Offers statistical models to deal with multi-test comparison problem. Platform: Windows compatible

Proper citation: EASE: the Expression Analysis Systematic Explorer (RRID:SCR_013361) Copy   

•   Source: SciCrunch Registry