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http://wwwchg.duhs.duke.edu/research/osa.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 19,2025. Software application that allows the researcher to evaluate evidence for linkage even when heterogeneity is present in a data set. This is not an unusual occurrence when studying diseases of complex origin. Families are ranked by covariate values in order to test evidence for linkage among homogeneous subsets of families. Because families are ranked, a priori covariate cutpoints are not necessary. Covariates may include linkage evidence at other genes, environmental exposures, or biological trait values such as cholesterol, age at onset, and so on.

Proper citation: OSA (RRID:SCR_002016) Copy   

•   Source: SciCrunch Registry

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

Software application designed to facilitate meta-analysis of large datasets (such as several whole genome scans) in a convenient, rapid and memory efficient manner. (entry from Genetic Analysis Software)

Proper citation: METAL (RRID:SCR_002013) Copy   

•   Source: SciCrunch Registry

http://www.omicsexpress.com/sva.php

Software package to annotate, visualize, and analyze the genetic variants identified through next-generation sequencing studies, including whole-genome sequencing (WGS) and exome sequencing studies. SVA aims to provide the research community with a user-friendly and efficient tool to analyze large amount of genetic variants, and to facilitate the identification of the genetic causes of human diseases and related traits.

Proper citation: SVA (RRID:SCR_002155) Copy   

•   Source: SciCrunch Registry

http://www.reading.ac.uk/Statistics/genetics/software.html

Software application (entry from Genetic Analysis Software)

Proper citation: LAMBDAA (RRID:SCR_001128) Copy   

•   Source: SciCrunch Registry

http://www.sanger.ac.uk/science/tools/carol

Software application that is a combined functional annotation score of non-synonymous coding variants. A major challenge in interpreting whole-exome data is predicting which of the discovered variants are deleterious or neutral. To address this question in silico, they have developed a score called Combined Annotation scoRing toOL (CAROL), which combines information from two bioinformatics tools: PolyPhen-2 and SIFT, in order to improve the prediction of the effect of non-synonymous coding variants. The combination of annotation tools can help improve automated prediction of whole-genome/exome non-synonymous variant functional consequences. (entry from Genetic Analysis Software) The software should run on any UNIX or GNU/Linux system.

Proper citation: CAROL (RRID:SCR_001800) Copy   

•   Source: SciCrunch Registry

http://www.reading.ac.uk/Statistics/genetics/software.html

Software application (entry from Genetic Analysis Software)

Proper citation: LDMET (RRID:SCR_001127) Copy   

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http://support.illumina.com/sequencing/sequencing_software/casava.html

Software package that creates genomic builds, calls SNPs, detects indels, and counts reads from data generated from one or more sequencing runs. In addition, CASAVA automatically generates a range of statistics, such as mean depth and percentage chromosome coverage, to enable comparison with previous builds or other samples. CASAVA analyzes sequencing reads in three stages: * FASTQ file generation and demultiplexing * Alignment to a reference genome * Variant detection and counting

Proper citation: CASAVA (RRID:SCR_001802) Copy   

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

Software program that implemented a likelihood-based framework for calling single nucleotide variants and detecting de novo point mutation events in families for next-generation sequencing data. The program takes as input genotype likelihood format (GLF) files which can be generated following the Creation of GLF files instruction and outputs the result in the (VCF) format. The variant calling and de novo mutation detection are modelled jointly within families and can handle both nuclear and extended pedigrees without consanguinity loops. The input is a set of GLF files for each of family members and the relationships are specified through the .ped file. (entry from Genetic Analysis Software)

Proper citation: POLYMUTT (RRID:SCR_002051) Copy   

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https://hsph.harvard.edu/research/price-lab/software/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Software application (entry from Genetic Analysis Software)

Proper citation: EIGENSOFT/EIGENSTRAT (RRID:SCR_001357) Copy   

•   Source: SciCrunch Registry

https://www.broadinstitute.org/birdsuite/birdsuite

Open-source set of tools to detect and report SNP genotypes, common Copy-Number Polymorphisms (CNPs), and novel, rare, or de novo CNVs in samples processed with the Affymetrix platform. While most of the components of the suite can be run individually (for instance, to only do SNP genotyping), the Birdsuite is especially intended for integrated analysis of SNPs and CNVs.

Proper citation: BIRDSUITE (RRID:SCR_001794) Copy   

•   Source: SciCrunch Registry

http://www.sanger.ac.uk/science/tools/dindel

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on March 7,2024. Software program for calling small indels from short-read sequence data ("next generation sequence data"). It is currently designed to handle only Illumina data. Dindel takes BAM files with mapped Illumina read data and enables researchers to detect small indels and produce a VCF file of all the variant calls. It has been written in C++ and can be used on Linux-based and Mac computers (it has not been tested on Windows operating systems).

Proper citation: DINDEL (RRID:SCR_001827) Copy   

•   Source: SciCrunch Registry

http://animalgene.umn.edu/pedigraph/

A pedigree visualization program specifically designed to draw large, complex pedigrees. (entry from Genetic Analysis Software) Options include: * Full pedigree * Summarization * Extraction of individual pedigrees * Inbreeding calculation * Coancestry coefficient calculation * Color control * Drawing size * Page size and margins * Drawing styles

Proper citation: PEDIGRAPH (RRID:SCR_001938) Copy   

•   Source: SciCrunch Registry

http://www.nbrc.nite.go.jp/e/index.html

Collection of microbial resources and perform taxonomic characterization of individual microorganisms such as bacteria including actinomycetes and archaea, yeasts, fungi, algaes, bacteriophages and DNA resources for academic research and industrial applications. NBRC is a member of WFCC, OECD Global BRC Network, ACM and JSCC. They are certified by quality management system ISO 9001. To provide attractive biological resources with useful information attached, they actively collect potentially useful biological resources (microorganisms and cloned genes) and distributes them to promote basic research as well as industrial applications. At the Biological Resource Center, they explore, isolate and identify microorganisms from various natural environments and at the same time accept scientifically and industrially useful microorganisms from researchers in academic and industrial sectors. The microbial DNA library constructed at the Biotechnology Development Center is also part of their collection. To improve and expand the collection, new methodologies for the isolation, identification and preservation of microorganisms and DNA will be investigated and developed so as to provide biological resources of higher quality. Their resources serve, for example, as the standard for determining antimicrobial activity, in aseptic tests as well as for the production of pharmaceutical substances and will be constantly reinforced for wider distribution to researchers in academia and industries. Please refer to the catalog shown at the NBRC website for details.

Proper citation: NBRC (RRID:SCR_002660) Copy   

•   Source: SciCrunch Registry

http://ftp://morgan.med.utah.edu/pub/Mim

Software application using multipoint IBD method for partitioning genetic variance of quantitative traits to specific chromosome regions using data on nuclear families. (entry from Genetic Analysis Software)

Proper citation: Multipoint Identical-by-descent Method (RRID:SCR_004676) Copy   

•   Source: SciCrunch Registry

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

Software tool that allows scripting of simple modifications to pedigree files. (entry from Genetic Analysis Software)

Proper citation: PEDSCRIPT (RRID:SCR_004571) Copy   

•   Source: SciCrunch Registry

http://www.chg.duke.edu/software/pedplot.html

Pedigree Plotting Program for the Pedfile Format (entry from Genetic Analysis Software)

Proper citation: PEDPLOT (RRID:SCR_003843) Copy   

•   Source: SciCrunch Registry

http://www.peroxisomedb.org/

The aim of the PEROXISOME database (PeroxisomeDB) is to gather, organize and integrate curated information on peroxisomal genes, their encoded proteins, their molecular function and metabolic pathway they belong to, and their related disorders. PeroxisomeDB contains the complete peroxisomal proteome of Homo sapiens (encoded by 85 genes) and Saccharomyces cerevisiae (encoded by 61 genes). Now, we have included 34 new organism genomes with the acquisition of 2426 new peroxisomal homolog proteins. PeroxisomeDB 2.0 integrates the peroxisomal metabolome of whole microbody family by the new incorporation of the glycosome proteomes of trypanosomatids and the glyoxysome proteome of Arabidopsis thaliana. The site also provides a Peroxisome Metabolome of peroxisomal genes and proteins, their molecular interactions and metabolic pathways, tools for comparative genomics, predictive tools. Sponsors: Preoxisome Database is funded by Institut de Gntique et deBiologie Molculaire et Cellulaire.

Proper citation: Peroxisome Database (RRID:SCR_008352) Copy   

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http://cgap.nci.nih.gov/Chromosomes/Mitelman

The web site includes genomic data for humans and mice, including transcript sequence, gene expression patterns, single-nucleotide polymorphisms, clone resources, and cytogenetic information. Descriptions of the methods and reagents used in deriving the CGAP datasets are also provided. An extensive suite of informatics tools facilitates queries and analysis of the CGAP data by the community. One of the newest features of the CGAP web site is an electronic version of the Mitelman Database of Chromosome Aberrations in Cancer. The data in the Mitelman Database is manually culled from the literature and subsequently organized into three distinct sub-databases, as follows: -The sub-database of cases contains the data that relates chromosomal aberrations to specific tumor characteristics in individual patient cases. It can be searched using either the Cases Quick Searcher or the Cases Full Searcher. -The sub-database of molecular biology and clinical associations contains no data from individual patient cases. Instead, the data is pulled from studies with distinct information about: -Molecular biology associations that relate chromosomal aberrations and tumor histologies to genomic sequence data, typically genes rearranged as a consequence of structural chromosome changes. -Clinical associations that relate chromosomal aberrations and/or gene rearrangements and tumor histologies to clinical variables, such as prognosis, tumor grade, and patient characteristics. It can be searched using the Molecular Biology and Clinical (MBC) Associations Searcher -The reference sub-database contains all the references culled from the literature i.e., the sum of the references from the cases and the molecular biology and clinical associations. It can be searched using the Reference Searcher. CGAP has developed six web search tools to help you analyze the information within the Mitelman Database: -The Cases Quick Searcher allows you to query the individual patient cases using the four major fields: aberration, breakpoint, morphology, and topography. -The Cases Full Searcher permits a more detailed search of the same individual patient cases as above, by including more cytogenetic field choices and adding search fields for patient characteristics and references. -The Molecular Biology Associations Searcher does not search any of the individual patient cases. It searches studies pertaining to gene rearrangements as a consequence of cytogenetic aberrations. -The Clinical Associations Searcher does not search any of the individual patient cases. It searches studies pertaining to clinical associations of cytogenetic aberrations and/or gene rearrangements. -The Recurrent Chromosome Aberrations Searcher provides a way to search for structural and numerical abnormalities that are recurrent, i.e., present in two or more cases with the same morphology and topography. -The Reference Searcher queries only the references themselves, i.e., the references from the individual cases and the molecular biology and clinical associations. Sponsors: This database is sponsored by the University of Lund, Sweden and have support from the Swedish Cancer Society and the Swedish Children''s Cancer Foundation

Proper citation: Mitelman Database of Chromosome Aberrations in Cancer (RRID:SCR_012877) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org/

Community model organism database for laboratory mouse and authoritative source for phenotype and functional annotations of mouse genes. MGD includes complete catalog of mouse genes and genome features with integrated access to genetic, genomic and phenotypic information, all serving to further the use of the mouse as a model system for studying human biology and disease. MGD is a major component of the Mouse Genome Informatics.Contains standardized descriptions of mouse phenotypes, associations between mouse models and human genetic diseases, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information. Data are obtained and integrated via manual curation of the biomedical literature, direct contributions from individual investigators and downloads from major informatics resource centers. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology.

Proper citation: Mouse Genome Database (RRID:SCR_012953) Copy   

•   Source: SciCrunch Registry

http://cello.life.nctu.edu.tw/

A subCELlular LOcalization predictor based on a multi-class support vector machine (SVM) classification system. CELLO uses 4 types of sequence coding schemes: the amino acid composition, the di-peptide composition, the partitioned amino acid composition and the sequence composition based on the physico-chemical properties of amino acids. They combine votes from these classifiers and use the jury votes to determine the final assignment.

Proper citation: CELLO (RRID:SCR_011968) Copy   

•   Source: SciCrunch Registry