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http://www.tcd.ie/IMM/trinity-biobank/index.php

The Trinity Biobank was established in 2005 to serve the needs of researchers in the area of genetic epidemiology, population genetics and pharmacogenomics. Its services are available to researchers not only in Trinity College but to other institutions at home and abroad. We provide an automated DNA extraction service purifying large volumes blood (up to 10mL whole blood) and tissue DNA for archival and other purposes. In addition it makes available purified DNA and associated GWAS data from 2000 healthy donors for research use. A key requirement for reliable downstream use of DNA is purity and strand size. The quality of DNA in blood and tissue deteriorates upon storage without purification even at -80 degrees C. We ensure rapid turnaround of biological samples through automated extraction using the Qiagen Autopure system based on optimized ''salting out'' chemistry. The purified DNA sample may then be stored safely at -20 degrees C without deterioration thus freeing up valuable -80 degree C freezer space and the associated capital and maintenance cost as well as security and lab space provision. Automated DNA extraction is particularly suitable for high-throughput sample processing called for in epidemiological studies or simply for clearing sample inventory backlogs. The Trinity Biobank distributes control DNA to researchers as part of its remit to enhance the level of research activity and to synergize molecular medicine research nationally and internationally. The buffy coat collection has been made possible with the cooperation of the Irish Blood Transfusion Service (IBTS). An important requirement to access the collection is that the use of the samples relates only to ethically-approved research and to specifically-nominated research projects. The DNA collection consists of high quality human genomic DNA. Each of the available 2,000 samples is from a single individual and each sample comes with the age and gender data of the donor. The buffy coat sample is derived from the total white cell compliment (50mL buffy coat) of a blood donation (c 400mL). We will endeavor to fulfill samples number requests based on age and gender as best as possible. This collection has also been genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0, featuring 1.8 million genetic markers, including more than 906,600 single nucleotide polymorphisms (SNPs) and more than 946,000 probes for the detection of copy number variation (CNV). The DNA comes available as a 100ng/uL in 100uL of TE Buffer, ie in 10ug amounts in a separate screw-cap ampoule. The ampoules are shipped in 100-tube boxes (Sarstedt). Corresponding plasma (ACD) is also available on request. Genotype data is supplied in PLINK binary PED files format (http://pngu.mgh.harvard.edu/~purcell/plink/ ).

Proper citation: Trinity Biobank (RRID:SCR_013279) Copy   

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http://vipbg.vcu.edu/vipbg/trimhap//

Software application for linkage disequilibrium mapping based on ancestral founder haplotypes. Method uses haplotype data from general pedigrees. (entry from Genetic Analysis Software)

Proper citation: TRIMHAP (RRID:SCR_013512) Copy   

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http://cuke.hort.ncsu.edu/cucurbit/wehner/software.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 24,2023. SAS software program to estimate genetic effects and heritabilities of quantitative traits in breeding populations consisting of six related generations (entry from Genetic Analysis Software)

Proper citation: SASQUANT (RRID:SCR_013122) Copy   

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http://www.aps.uoguelph.ca/~msargol/qmsim/

Software application designed to simulate a wide range of genetic architectures and population structures in livestock. Large scale genotyping data and complex pedigrees can be efficiently simulated. QMSim is a family based simulator, which can also take into account predefined evolutionary features, such as LD, mutation, bottlenecks and expansions. The simulation is basically carried out in two steps: In the first step, a historical population is simulated to establish mutation-drift equilibrium and, in the second step, recent population structures are generated, which can be complex. QMSim allows for a wide range of parameters to be incorporated in the simulation models in order to produce appropriate simulated data. (entry from Genetic Analysis Software)

Proper citation: QMSIM (RRID:SCR_013123) Copy   

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

Software application that constructs feasible haplotype configurations and the corresponding segregation types on pedigrees. the haplotype configuration minimizes recombinations on the pedigree. (entry from Genetic Analysis Software)

Proper citation: INSEGT (RRID:SCR_013126) Copy   

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http://genome.sph.umich.edu/wiki/GlfSingle

Software application that is a GLF-based variant caller for next-generation sequencing data. It takes one/three/multiple GLF format genotype likelihood files as input and generates a VCF-format set of variant calls as output. (entry from Genetic Analysis Software)

Proper citation: GLFSINGLE/GLFTRIO/GLFMULTIPLES (RRID:SCR_013128) Copy   

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https://sourceforge.net/projects/ggsd/

Web-based, relational database driven data management software package for the management of large scale genetic studies. (entry from Genetic Analysis Software)

Proper citation: GGSD (RRID:SCR_013129) Copy   

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http://www.som.soton.ac.uk/research/geneticsdiv/epidemiology/chromscan/

A statistical based program for association mapping of disease genes. It utilises the Malecot model and the linkage disequilibrium (LD) map for the candidate region to analyse the genotypes derive from large sample of matched cases and controls. (entry from Genetic Analysis Software)

Proper citation: CHROMSCAN (RRID:SCR_013131) Copy   

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http://mayoresearch.mayo.edu/mayo/research/schaid_lab/software.cfm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 24,2023. Software application to compute composite measures of linkage disequilibrium, their variances and covariances, and statistical tests, for all pairs of alleles from two loci when linkage phase is unkown. An extension of Weir and Cockerham (1989) to apply to multi-allelic loci. (entry from Genetic Analysis Software)

Proper citation: COMPOSITELD (RRID:SCR_013132) Copy   

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http://faculty.washington.edu/eathomp/Anonftp/PANGAEA/BOREL/

Software application for inference of genealogical relationships from genetic data, including sibship inference.

Proper citation: BOREL (RRID:SCR_013135) Copy   

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http://mayoresearch.mayo.edu/mayo/research/schaid_lab/software.cfm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 24,2023. Software application for statistical methods for disease and genetic marker associations using cases and their parents. These methods include an extension of the transmission/disequilibrium test (TDT) for multiple marker alleles, as well as additional general tests sensitive to associations that depend on dominant or recessive genetic mechanisms. (entry from Genetic Analysis Software)

Proper citation: GASSOC (RRID:SCR_013136) Copy   

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http://www.bio.unc.edu/faculty/vision/lab/mappop/

Software application that selects high resolution mapping subsamples and performs bin mapping (entry from Genetic Analysis Software)

Proper citation: MAPPOP (RRID:SCR_013490) Copy   

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http://dlin.web.unc.edu/software/SNPMStat/

A command-line program for the statistical analysis of SNP-disease association in case-control/cohort/cross-sectional studies with potentially missing genotype data. SNPMStat allows the user to estimate or test SNP effects and SNP-environment interactions by maximizing the (observed-data) likelihood that properly accounts for phase uncertainty, study design and gene-environment dependence. For SNPs without missing data, the program performs the standard association analysis. For typed SNPs with missing data or untyped SNPs, the program performs the maximum-likelihood analysis. (entry from Genetic Analysis Software)

Proper citation: SNPMSTAT (RRID:SCR_013339) Copy   

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http://www.cbil.ece.vt.edu/ResearchOngoingSNP.htm

Software application (entry from Genetic Analysis Software)

Proper citation: MECPM (RRID:SCR_013341) Copy   

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

Software application that implements valid and efficient statistical methods for meta-analysis of genomewide association studies with overlapping subjects. The current release performs logistic regression analysis of individual level data under the additive mode of inheritance. Data from genome-wide association studies are often analyzed jointly for the purposes of combining information from multiple studies of the same disease or comparing results across different disorders. In many instances, the same subjects appear in multiple studies. Failure to account for overlapping subjects can greatly inflate type I error when combining results from multiple studies of the same disease and can drastically reduce power when comparing results across different disorders. (entry from Genetic Analysis Software)

Proper citation: MAOS (RRID:SCR_013351) Copy   

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http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/

Database to store and display somatic mutation information and related details and contains information relating to human cancers. The mutation data and associated information is extracted from the primary literature. In order to provide a consistent view of the data a histology and tissue ontology has been created and all mutations are mapped to a single version of each gene. The data can be queried by tissue, histology or gene and displayed as a graph, as a table or exported in various formats.
Some key features of COSMIC are:
* Contains information on publications, samples and mutations. Includes samples which have been found to be negative for mutations during screening therefore enabling frequency data to be calculated for mutations in different genes in different cancer types.
* Samples entered include benign neoplasms and other benign proliferations, in situ and invasive tumours, recurrences, metastases and cancer cell lines.

Proper citation: COSMIC - Catalogue Of Somatic Mutations In Cancer (RRID:SCR_002260) Copy   

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http://www.ebi.ac.uk/swissprot/hpi/hpi.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 03, 2011. IT HAS BEEN REPLACED BY A NEW UniProtKB/Swiss-Prot ANNOTATION PROGRAM CALLED UniProt Chordata protein annotation program. The Human Proteome Initiative (HPI) aims to annotate all known human protein sequences, as well as their orthologous sequences in other mammals, according to the quality standards of UniProtKB/Swiss-Prot. In addition to accurate sequences, we strive to provide, for each protein, a wealth of information that includes the description of its function, domain structure, subcellular location, similarities to other proteins, etc. Although as complete as currently possible, the human protein set they provide is still imperfect, it will have to be reviewed and updated with future research results. They will also create entries for newly discovered human proteins, increase the number of splice variants, explore the full range of post-translational modifications (PTMs) and continue to build a comprehensive view of protein variation in the human population. The availability of the human genome sequence has enabled the exploration and exploitation of the human genome and proteome to begin. Research has now focused on the annotation of the genome and in particular of the proteome. With expert annotation extracted from the literature by biologists as the foundation, it has been possible to expand into the areas of data mining and automatic annotation. With further development and integration of pattern recognition methods and the application of alignments clustering, proteome analysis can now be provided in a meaningful way. These various approaches have been integrated to attach, extract and combine as much relevant information as possible to the proteome. This resource should be valuable to users from both research and industry. We maintain a file containing all human UniProtKB/Swiss-Prot entries. This file is updated at every biweekly release of UniProt and can be downloaded by FTP download, HTTP download or by using a mirroring program which automatically retrieves the file at regular intervals.

Proper citation: Human Proteomics Initiative (RRID:SCR_002373) Copy   

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http://www.HGPD.jp

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 4,2023.The Human Gene and Protein Database presents SDS-PAGE patterns and other informations of human genes and proteins. The HGPD was constructed from full-length cDNAs. For conversion to Gateway entry clones, we first determined an open reading frame (ORF) region in each cDNA meeting the criteria. Those ORF regions were PCR-amplified utilizing selected resource cDNAs as templates. All the details of the construction and utilization of entry clones will be published elsewhere. Amino acid and nucleotide sequences of an ORF for each cDNA and sequence differences of Gateway entry clones from source cDNAs are presented in the GW: Gateway Summary window. Utilizing those clones with a very efficient cell-free protein synthesis system featuring wheat germ, we have produced a large number of human proteins in vitro. Expressed proteins were detected in almost all cases. Proteins in both total and supernatant fractions are shown in the PE: Protein Expression window. In addition, we have also successfully expressed proteins in HeLa cells and determined subcellular localizations of human proteins. These biological data are presented on the frame of cDNA clusters in the Human Gene and Protein Database. To build the basic frame of HGPD, sequences of FLJ full-length cDNAs and others deposited in public databases (Human ESTs, RefSeq, Ensembl, MGC, etc.) are assembled onto the genome sequences (NCBI Build 35 (UCSC hg17)). The majority of analysis data for cDNA sequences in HGPD are shared with the FLJ Human cDNA Database (http://flj.hinv.jp/) constructed as a human cDNA sequence analysis database focusing on mRNA varieties caused by variations in transcription start site (TSS) and splicing.

Proper citation: Human Gene and Protein Database (HGPD) (RRID:SCR_002889) Copy   

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http://www.ecoli-york.org/

A database that curates new experimental and bioinformatic information about the genes and gene products of the model bacterium Escherichia coli K-12 strain MG1655. It has been created to integrate information from post-genomic experiments into a single resource with the aim of providing functional predictions for the 1500 or so gene products for which we have no knowledge of their physiological function. While EchoBASE provides a basic annotation of the genome, taken from other databases, its novelty is in the curation of post-genomic experiments and their linkage to genes of unknown function. Experiments published on E. coli are curated to one of two levels. Papers dealing with the determination of function of a single gene are briefly described, while larger dataset are actually included in the database and can be searched and manipulated. This includes data for proteomics studies, protein-protein interaction studies, microarray data, functional genomic approaches (looking at multiple deletion strains for novel phenotypes) and a wide range of predictions that come out of in silico bioinformatic approaches. The aim of the database is to provide hypothesis for the functions of uncharacterized gene products that may be used by the E. coli research community to further our knowledge of this model bacterium.

Proper citation: EchoBASE (RRID:SCR_002430) Copy   

•   Source: SciCrunch Registry

http://weatherby.genetics.utah.edu/cgi-bin/Phevor/PhevorWeb.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on October 28,2025. Tool that integrates phenotype, gene function, and disease information with personal genomic data for improved power to identify disease-causing alleles. It works by combining knowledge resident in multiple biomedical ontologies with the outputs of variant prioritization tools. It does so using an algorithm that propagates information across and between ontologies. This process enables Phevor to accurately reprioritize potentially damaging alleles identified by variant prioritization tools in light of gene function, disease, and phenotype knowledge. Phevor is especially useful for single exome and family trio-based diagnostic analyses, the most commonly occurring clinical scenarios, and ones for which existing personal-genomes diagnostic tools are most inaccurate and underpowered. Phevor not only improves diagnostic accuracy for individuals presenting with established disease phenotypes, but also for those with previously undescribed and atypical disease presentations. Importantly, Phevor is not limited to known diseases, or known disease-causing alleles.

Proper citation: Phevor (RRID:SCR_002273) Copy   

•   Source: SciCrunch Registry