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http://sourceforge.net/projects/dnaclust/

Software program for clustering large number of short similar DNA sequences. It was originally designed for clustering targeted 16S rRNA pyrosequencing reads.

Proper citation: DNACLUST (RRID:SCR_001771) Copy   

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http://snpper.chip.org/

Retrieve known single-nucleotide polymorphisms (SNPs) by position or by association with a gene; save, filter, analyze, display or export SNP sets; explore known genes using names or chromosome positions.

Proper citation: SNPper (RRID:SCR_001963) Copy   

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https://www.wtccc.org.uk/

Consortium of 50 research groups across the UK to harness the power of newly-available genotyping technologies to improve our understanding of the aetiological basis of several major causes of global disease. The consortium has gathered genotype data for up to 500,000 sites of genome sequence variation (single nucleotide polymorphisms or SNPs) in samples ascertained for the disease phenotypes. Analysis of the genome-wide association data generated has lead to the identification of many SNPs and genes showing evidence of association with disease susceptibility, some of which will be followed up in future studies. In addition, the Consortium has gained important insights into the technical, analytical, methodological and biological aspects of genome-wide association analysis. The core of the study comprised an analysis of 2,000 samples from each of seven diseases (type 1 diabetes, type 2 diabetes, coronary heart disease, hypertension, bipolar disorder, rheumatoid arthritis and Crohn's disease). For each disease, the case samples have been ascertained from sites widely distributed across Great Britain, allowing us to obtain considerable efficiencies by comparing each of these case populations to a common set of 3,000 nationally-ascertained controls also from England, Scotland and Wales. These controls come from two sources: 1,500 are representative samples from the 1958 British Birth Cohort and 1,500 are blood donors recruited by the three national UK Blood Services. One of the questions that the WTCCC study has addressed relates to the relative merits of these alternative strategies for the generation of representative population cohorts. Genotyping for this main Case Control study was conducted by Affymetrix using the (commercial) Affymetrix 500K chip. As part of this study a total of 17,000 samples were typed for 500,000 SNPs. There are two additional components to the study. First, the WTCCC award is part-funding a study of host resistance to infectious diseases in African populations. The same approach has been used to type 2,000 cases of tuberculosis (TB) and 2,000 cases of malaria, as well as 2,000 shared controls. As well as addressing diseases of major global significance, and extending WTCCC coverage into the area of infectious disease, the inclusion of samples of African origin has obvious benefits with respect to methodological aspects of genome-wide association analysis. Second, the WTCCC has, for four additional diseases (autoimmune thyroid disease, breast cancer, ankylosing spondylitis, multiple sclerosis), completed an analysis of 15,000 SNPs designed to represent a large proportion of the known non-synonymous coding SNPs across the genome. This analysis has been performed at the WTSI using a custom Infinium chip (Illumina). Data release The genotypic data of the control samples (1958 British Birth Cohort and UK Blood Service) and from seven diseases analyzed in the main study are now available to qualified researchers. Summary genotype statistics for these collections are available directly from the website. Access to the individual-level genotype data and summary genotype statistics is by application to the Consortium Data Access Committee (CDAC) and approval subject to a Data Access Agreement. WTCCC2: A further round of GWA studies were funded in April 2008. These include 15 WTCCC-collaborative studies and 12 independent studies be supported totaling approximately 120,000 samples. Many of the studies represent major international collaborative networks that have together assembled large sample collections. WTCCC2 will perform genome-wide association studies in 13 disease conditions: Ankylosing spondylitis, Barrett's oesophagus and oesophageal adenocarcinoma, glaucoma, ischaemic stroke, multiple sclerosis, pre-eclampsia, Parkinson's disease, psychosis endophenotypes, psoriasis, schizophrenia, ulcerative colitis and visceral leishmaniasis. WTCCC2 will also investigate the genetics of reading and mathematics abilities in children and the pharmacogenomics of statin response. Over 60,000 samples will be analyzed using either the Affymetrix v6.0 chip or the Illumina 660K chip. The WTCCC2 will also genotype 3,000 controls each from the 1958 British Birth cohort and the UK Blood Service control group, and the 6,000 controls will be genotyped on both the Affymetrix v6.0 and Illumina 1.2M chips. WTCCC3: The Wellcome Trust has provided support for a further round of GWA studies in January 2009. These include 5 WTCCC-collaborative studies to be carried out in WTCCC3 and 5 independent studies, across a range of diseases. Many of the studies represent major international collaborative networks that have together assembled large sample collections. WTCCC3 will perform genome-wide association studies in the following 4 disease conditions: primary biliary cirrhosis, anorexia nervosa, pre-eclampsia in UK subjects, and the interactions between donor and recipient DNA related to early and late renal transplant dysfunction. The WTCCC3 will also carry out a pilot in a study of the genetics of host control of HIV-1 infection. Over 40,000 samples will be analyzed using the Illumina 660K chip. The WTCCC3 will utilize the 6,000 control genotypes generated by the WTCCC2.

Proper citation: Wellcome Trust Case Control Consortium (RRID:SCR_001973) Copy   

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http://icbi.at/software/gpviz/gpviz.shtml

A versatile Java-based software used for dynamic gene-centered visualization of genomic regions and/or variants.

Proper citation: GPViz (RRID:SCR_000346) Copy   

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http://www.arb-home.de/

Software environment for maintaining databases of molecular sequences and additional information, and for analyzing the sequence data, with emphasis on phylogeny reconstruction. Programs have primarily been developed for ribosomal ribonucleic acid (rRNA) sequences and, therefore, contain special tools for alignment and analysis of these structures. However, other molecular sequence data can also be handled. Protein gene sequences and predicted protein primary structures as well as protein secondary structures can be stored in the same database. ARB package is designed for graphical user interface. Program control and data display are available in a hierarchical set of windows and subwindows. Majority of operations can be controlled using mouse for moving pointer and the left mouse button for initiating and performing operations.

Proper citation: ARB project (RRID:SCR_000515) Copy   

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http://www.psb.ugent.be/esb/PiNGO/

A Java-based tool to easily find unknown genes in a network that are significantly associated with user-defined target Gene Ontology (GO) categories. PiNGO is implemented as a plugin for Cytoscape, a popular open source software platform for visualizing and integrating molecular interaction networks. PiNGO predicts the categorization of a gene based on the annotations of its neighbors, using the enrichment statistics of its sister tool BiNGO. Networks can either be selected from the Cytoscape interface or uploaded from file. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: PiNGO (RRID:SCR_000692) Copy   

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http://www.rostlab.org/cms/

A lab organization which has bases in Munich, Germany and at Columbia University and focuses its research on protein structure and function using sequence and evolutionary information. They utilize machine learning and statistical methods to analyze genetic material and its gene products. Research goals of the lab involve using protein and DNA sequences along with evolutionary information to predict aspects of the proteins relevant to the advance of biomedical research.

Proper citation: ROSTLAB (RRID:SCR_000792) Copy   

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http://cran.r-project.org/src/contrib/Archive/iFad/

An R software package implementing a bayesian sparse factor model for the joint analysis of paired datasets, the gene expression and drug sensitivity profiles, measured across the same panel of samples, e.g. cell lines.

Proper citation: iFad (RRID:SCR_000271) Copy   

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http://genome.sph.umich.edu/wiki/Mach2dat:_Association_with_MACH_output

Software that performs logistic regression, using imputed SNP dosage data and adjusting for covariates.

Proper citation: Mach2dat (RRID:SCR_009599) Copy   

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http://www.unc.edu/~yunmli/MaCH-Admix/

A genotype imputation software that is an extension to MaCH for faster and more flexible imputaiton, especially in admixed populations. It has incorporated a novel piecewise reference selection method to create reference panels tailored for target individual(s). This reference selection method generates better imputation quality in shorter running time. MaCH-Admix also separates model parameter estimation from imputation. The separation allows users to perform imputation with standard reference panels + pre-calibrated parameters in a data independent fashion. Alternatively, if one works with study-specific reference panels, or isolated target population, one has the option to simultaneously estimate these model parameters while performing imputation. MaCH-Admix has included many other useful options and supports VCF input files. All existing MaCH documentation applies to MaCH-Admix.

Proper citation: MaCH-Admix (RRID:SCR_009598) Copy   

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http://archive.broadinstitute.org/mpg/tagger/

Software application (entry from Genetic Analysis Software)

Proper citation: TAGGER (RRID:SCR_009419) Copy   

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https://watson.hgen.pitt.edu/docs/splink108.html

Software application for linkage analysis using affected sib pairs (entry from Genetic Analysis Software)

Proper citation: SPLINK (RRID:SCR_009414) Copy   

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http://www.sanger.ac.uk/science/tools/ssahasnp-0

A polymorphism detection tool that detects homozygous SNPs and indels by aligning shotgun reads to the finished genome sequence. Highly repetitive elements are filtered out by ignoring those kmer words with high occurrence numbers. For those less repetitive or non-repetitive reads, we place them uniquely on the reference genome sequence and find the best alignment according to the pair-wise alignment score if there are multiple seeded regions. From the best alignment, SNP candidates are screened, taking into account the quality value of the bases with variation as well as the quality values in the neighbouring bases, using neighbourhood quality standard (NQS). For insertions/deletions, we check if the same indel is mapped by more than one read, ensuring the detected indel with high confidence. (entry from Genetic Analysis Software)

Proper citation: SSAHASNP (RRID:SCR_009415) Copy   

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https://swfsc.noaa.gov/textblock.aspx?Division=FED&id=3434

Software application that simulate pedigrees and genetic data in age-structured populations (entry from Genetic Analysis Software)

Proper citation: SPIP (RRID:SCR_009410) Copy   

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http://www.joslinresearch.org/LabSites/Krolewski/splat/

Software application that can calculate virtually any linkage test statistic under several sib pair study designs: affected, discordant, unaffected, and pairs defined by threshold values for quantitative traits, such as extreme discordant sib pairs. It uses the EM algorithm to compute maximum likelihood estimates of sharing (subject to any user-specified domain restrictions or null hypotheses) and then plots lod scores versus chromosomal position. It includes a novel grid scanning capability that enables simultaneous visualization of multiple test statistics. Phenotype definitions can be modified without recalculating inheritance vectors, thereby providing considerable analytical flexibility. (entry from Genetic Analysis Software), THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: SPLAT (RRID:SCR_009411) Copy   

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https://plant-breeding.uni-hohenheim.de/software.html#jfmulticontent_c110647-2

Software application (entry from Genetic Analysis Software)

Proper citation: PLABQTL (RRID:SCR_012789) Copy   

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http://econpapers.repec.org/software/bocbocode/s438902.htm

Software application (entry from Genetic Analysis Software)

Proper citation: HAPBLOCK 2 (RRID:SCR_012788) Copy   

•   Source: SciCrunch Registry

http://www.openbioinformatics.org/annovar/

An efficient software tool to utilize update-to-date information to functionally annotate genetic variants detected from diverse genomes (including human genome hg18, hg19, as well as mouse, worm, fly, yeast and many others). Given a list of variants with chromosome, start position, end position, reference nucleotide and observed nucleotides, ANNOVAR can perform: 1. gene-based annotation. 2. region-based annotation. 3. filter-based annotation. 4. other functionalities. (entry from Genetic Analysis Software)

Proper citation: ANNOVAR (RRID:SCR_012821) Copy   

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https://www.lifegene.se/In-english/

Swedish study to get a better understanding of how genes, environment and way of life affect health that will enable access to the longitudinal data on 500,000 participants after ethical approval. Half a million people in Sweden between the ages of 0 and 45 will be recruited as volunteers for 6 to 8 years. People between 18 and 45 will be invited and they may, in turn, bring children and other people that they live with into the project. Participants will be followed for many years with regular online surveys and health checks. Their blood and urine samples will also be stored in a biobank. All the data will form a very large information base, where researchers can follow what happens with people''''s health. The LifeGene test center will measure height, hip, waist and chest measurements. A so-called spirometry test will be conducted which measures lung function, a hearing test and bioimpedance measurement (includes weight, BMI and distribution of body fat and muscle mass). They also take blood and urine samples and measure blood pressure and pulse. LifeGene foresees a lot of different research cooperation. Everything from simple withdrawal of longitudinal data, leverage of LifeGene infrastructure and cooperation between LifeGene and complementing scientific projects covering specific areas in more depth. LifeGene will enable access to unique longitudinal data on 500,000 participants available for researchers after ethical approval. LifeGene is also an infrastructure with Test Centers covering most of Sweden, logistics for sample management from arm-to-freezer and state-of-the-art large scale automatic biobanking enabling low cost, high quality, fast withdrawal of biological samples.

Proper citation: LifeGene (RRID:SCR_010524) Copy   

•   Source: SciCrunch Registry

https://www.dkfz.de/en/epidemiologie-krebserkrankungen/software/software.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May24,2023. Software program that implements the Mantel statistics as proposed by Beckmann et al. (2005) to test for association between genetic markers and phenotypes in case-control studies using haplotype information. The potential value of haplotypes has attracted widespread interest in the mapping of complex traits. Haplotype sharing methods take into account linkage disequilibrium information between multiple markers, and may have good power to detect predisposing genes. We present a new approach based on Mantel statistics for space time clustering, which we developed in order to improve the power of haplotype sharing analysis for gene mapping in complex disease. The new statistic correlates genetic similarity and phenotypic similarity across pairs of haplotypes for case-only and case-control studies. The genetic similarity is measured as the shared length between haplotypes around a putative disease locus. Alternative measures for the phenotypic similarity were implemented. (entry from Genetic Analysis Software)

Proper citation: TOMCAT (RRID:SCR_013120) Copy   

•   Source: SciCrunch Registry