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http://sourceforge.net/projects/molbiolib/

A compact, portable, and extensively tested C++11 software framework and set of applications tailored to the demands of next-generation sequencing data and applicable to many other applications. It is designed to work with common file formats and data types used both in genomic analysis and general data analysis. A central relational-database-like Table class is a flexible and powerful object to intuitively represent and work with a wide variety of tabular datasets, ranging from alignment data to annotations. MolBioLib includes programs to perform a wide variety of analysis tasks such as computing read coverage, annotating genomic intervals, and novel peak calling with a wavelet algorithm. This package assumes fluency in both UNIX and C++.

Proper citation: MolBioLib (RRID:SCR_005372) Copy   

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http://statgenpro.psychiatry.hku.hk/limx/kggseq/

A biological Knowledge-based mining platform for Genomic and Genetic studies using Sequence data. The software platform, constituted of bioinformatics and statistical genetics functions, makes use of valuable biologic resources and knowledge for sequencing-based genetic mapping of variants / genes responsible for human diseases / traits. It facilitates geneticists to fish for the genetic determinants of human diseases / traits in the big sea of DNA sequences. KGGSeq has paid attention to downstream analysis of genetic mapping. The framework was implemented to filter and prioritize genetic variants from whole exome sequencing data.

Proper citation: KGGSeq (RRID:SCR_005311) Copy   

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http://code.google.com/p/hydra-sv/

Software that detects structural variation (SV) breakpoints by clustering discordant paired-end alignments whose signatures corroborate the same putative breakpoint. Hydra can detect breakpoints caused by all classes of structural variation. Moreover, it was designed to detect variation in both unique and duplicated genomic regions; therefore, it will examine paired-end reads having multiple discordant alignments. Hydra does not attempt to classify SV breakpoints based on the mapping distances and orientations of each breakpoint cluster, it merely detects and reports breakpoints. This is an intentional decision, as it was observed that in loci affected by complex rearrangements, the type of variant suggested by the breakpoint signature is not always correct. Hydra does report the orientations, distances, number of supporting read-pairs, etc., for each breakpoint. It is suggested that downstream methods be used to classify variants based on the genomic features that they overlap and the co-occurrence of other breakpoints. For example, they developed BEDTools for exactly this purpose and the breakpoints reported by Hydra are in the BEDPE format used by BEDTools. Future releases of Hydra will include scripts that assist in the classification process.

Proper citation: Hydra (RRID:SCR_005260) Copy   

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http://202.97.205.78/CpG_MPs/

Tool for identification and analysis of CpG methylation patterns of genomic regions from high-throughput bisulfite sequencing data. It may identify the unmethylated and methylated regions for a single sample, the conserved and differential methylation regions with different methylation patterns for paired or multiple samples. It includes four main modules as follows: # Normalization of the sequencing reads of cytosines following guanines; # Identification of the unmethylated (methylated) regions using hotspot extension algorithm; # Identification of conservatively and differentially methylated regionsby combining the combinatorial algorithm for determination of potentially functional regions with the algorithm of analysis of variance (ANOVA) for assess the statistical significance of differentially methylated regions; # Extraction of sequence features and visualization of these potentially functional regions.

Proper citation: CpG MPs (RRID:SCR_005441) Copy   

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http://www.europeanlung.org/en/projects-and-research/projects/airprom/

Consortium focused on developing computer and physical models of the airway system for patients with asthma and chronic obstructive pulmonary disease (COPD). Developing accurate models will better predict how asthma and COPD develop, since current methods can only assess the severity of disease. They aim to bridge the gaps in clinical management of airways-based disease by providing reliable models that predict disease progression and the response to treatment for each person with asthma or COPD. A data management platform provides a secure and sustainable infrastructure that semantically integrates the clinical, physiological, genetic, and experimental data produced with existing biomedical knowledge from allied consortia and public databases. This resource will be available for analysis and modeling, and will facilitate sharing, collaboration and publication within AirPROM and with the broader community. Currently the AirPROM knowledge portal is only accessible by AirPROM partners.

Proper citation: AirPROM (RRID:SCR_003827) Copy   

•   Source: SciCrunch Registry

http://www.cbioc.com/en/

Commercial organization that discovers, validates & analyzes genomic biomarkers with a focus on body fluid samples. Take advantage of their proven expertise in biomarker signature development and speed up your biomarker studies.

Proper citation: Comprehensive Biomarker Center (RRID:SCR_003901) Copy   

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http://www.chargeconsortium.com/

Consortium formed to facilitate genome-wide association study meta-analyses and replication opportunities among multiple large and well-phenotyped longitudinal cohort studies. A bibliography of CHARGE publications is available. Its founding member cohorts include: * Age, Gene, Environment, Susceptibility Study -- Reykjavik * Atherosclerosis Risk in Communities Study * Cardiovascular Health Study * Framingham Heart Study * Rotterdam Study Additional core cohorts include: * Coronary Artery Risk Development in Young Adults * Family Heart Study * Health, Aging, and Body Composition Study * Jackson Heart Study * Multi-Ethnic Study of Atherosclerosis

Proper citation: Cohorts for Heart and Aging Research in Genomic Epidemiology (RRID:SCR_004034) Copy   

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http://neuronalarchitects.com/ibiofind.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 17, 2016. C#.NET 4.0 WPF / OWL / REST / JSON / SPARQL multi-threaded, parallel desktop application enables the construction of biomedical knowledge through PubMed, ScienceDirect, EndNote and NIH Grant repositories for tracking the work of medical researchers for ranking and recommendations. Users can crawl web sites, build latent semantic indices to generate literature searches for both Clinical Translation Science Award and non-CTSA institutions, examine publications, build Bayesian networks for neural correlates, gene to gene interactions, protein to protein interactions and as well drug treatment hypotheses. Furthermore, one can easily access potential researcher information, monitor and evolve their networks and search for possible collaborators and software tools for creating biomedical informatics products. The application is designed to work with the ModelMaker, R, Neural Maestro, Lucene, EndNote and MindGenius applications to improve the quality and quantity of medical research. iBIOFind interfaces with both eNeoTutor and ModelMaker 2013 Web Services Implementation in .NET for eNeoTutor to aid instructors to build neuroscience courses as well as rare diseases. Added: Rare Disease Explorer: The Visualization of Rare Disease, Gene and Protein Networks application module. Cinematics for the Image Finder from Yale. The ability to automatically generate and update websites for rare diseases. Cytoscape integration for the construction and visualization of pathways for Molecular targets of Model Organisms. Productivity metrics for medical researchers in rare diseases. iBIOFind 2013 database now includes over 150 medical schools in the US along with Clinical Translational Science Award Institutions for the generation of biomedical knowledge, biomedical informatics and Researcher Profiles.

Proper citation: iBIOFind (RRID:SCR_001587) Copy   

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http://www.nitrc.org/projects/plink

Open source whole genome association analysis toolset, designed to perform range of basic, large scale analyses in computationally efficient manner. Used for analysis of genotype/phenotype data. Through integration with gPLINK and Haploview, there is some support for subsequent visualization, annotation and storage of results. PLINK 1.9 is improved and second generation of the software.

Proper citation: PLINK (RRID:SCR_001757) Copy   

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http://www.aspgd.org/

Database of genetic and molecular biological information about the filamentous fungi of the genus Aspergillus including information about genes and proteins of Aspergillus nidulans and Aspergillus fumigatus; descriptions and classifications of their biological roles, molecular functions, and subcellular localizations; gene, protein, and chromosome sequence information; tools for analysis and comparison of sequences; and links to literature information; as well as a multispecies comparative genomics browser tool (Sybil) for exploration of orthology and synteny across multiple sequenced Sgenus species. Also available are Gene Ontology (GO) and community resources. Based on the Candida Genome Database, the Aspergillus Genome Database is a resource for genomic sequence data and gene and protein information for Aspergilli. Among its many species, the genus contains an excellent model organism (A. nidulans, or its teleomorph Emericella nidulans), an important pathogen of the immunocompromised (A. fumigatus), an agriculturally important toxin producer (A. flavus), and two species used in industrial processes (A. niger and A. oryzae). Search options allow you to: *Search AspGD database using keywords. *Find chromosomal features that match specific properties or annotations. *Find AspGD web pages using keywords located on the page. *Find information on one gene from many databases. *Search for keywords related to a phenotype (e.g., conidiation), an allele (such as veA1), or an experimental condition (e.g., light). Analysis and Tools allow you to: *Find similarities between a sequence of interest and Aspergillus DNA or protein sequences. *Display and analyze an Aspergillus sequence (or other sequence) in many ways. *Navigate the chromosomes set. View nucleotide and protein sequence. *Find short DNA/protein sequence matches in Aspergillus. *Design sequencing and PCR primers for Aspergillus or other input sequences. *Display the restriction map for a Aspergillus or other input sequence. *Find similarities between a sequence of interest and fungal nucleotide or protein sequences. AspGD welcomes data submissions.

Proper citation: ASPGD (RRID:SCR_002047) 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://www.doe-mbi.ucla.edu/

The UCLA-DOE Institute for Genomics and Proteomics carries out research in bioenergy, structural biology, genomics and proteomics, consistent with the research mission of the United States Department of Energy. Major interests of the 12 Principal Investigators and 9 Associate Members include systems approaches to organisms, structural biology, bioinformatics, and bioenergetic systems. The Institute sponsors 5 Core Technology Centers, for X-ray and NMR structural determination, bioinformatics and computation, protein expression and purification, and biochemical instrumentation. Services offered by this Institute: - Databases: * DIP (The Database of Interacting Proteins): The DIPTM database catalogs experimentally determined interactions between proteins. It combines information from a variety of sources to create a single, consistent set of protein-protein interactions. * ProLinks Database of Functional Linkages: The Prolinks database is a collection of inference methods used to predict functional linkages between proteins. These methods include the Phylogenetic Profile method which uses the presence and absence of proteins across multiple genomes to detect functional linkages; the Gene Cluster method, which uses genome proximity to predict functional linkage; Rosetta Stone, which uses a gene fusion event in a second organism to infer functional relatedness; and the Gene Neighbor method, which uses both gene proximity and phylogenetic distribution to infer linkage. - Data-to-Structure Servers: * SAVEs Structure Verification Server * Merohedral Twinning Test Server * SER Surface Entropy Reduction Server * VERIFY3D Structure Verification Server * ERRAT Structure Verification Server - Structure-to-Function Servers: * ProKnow Protein Functionator * Hot Patch Functional Site Locator

Proper citation: University of California at Los Angeles - Department of Energy Institute for Genomics and Proteomics (RRID:SCR_001921) Copy   

•   Source: SciCrunch Registry

http://www.nervenet.org/main/dictionary.html

A mouse-related portal of genomic databases and tables of mouse brain data. Most files are intended for you to download and use on your own personal computer. Most files are available in generic text format or as FileMaker Pro databases. The server provides data extracted and compiled from: The 2000-2001 Mouse Chromosome Committee Reports, Release 15 of the MIT microsatellite map (Oct 1997), The recombinant inbred strain database of R.W. Elliott (1997) and R. W. Williams (2001), and the Map Manager and text format chromosome maps (Apr 2001). * LXS genotype (Excel file): Updated, revised positions for 330 markers genotyped using a panel of 77 LXS strain. * MIT SNP DATABASE ONLINE: Search and sort the MIT Single Nucleotide Polymorphism (SNP) database ONLINE. These data from the MIT-Whitehead SNP release of December 1999. * INTEGRATED MIT-ROCHE SNP DATABASE in EXCEL and TEXT FORMATS (1-3 MB): Original MIT SNPs merged with the new Roche SNPs. The Excel file has been formatted to illustrate SNP haplotypes and genetic contrasts. Both files are intended for statistical analyses of SNPs and can be used to test a method outlined in a paper by Andrew Grupe, Gary Peltz, and colleagues (Science 291: 1915-1918, 2001). The Excel file includes many useful equations and formatting that will help in navigating through this large database and in testing the in silico mapping method. * Use of inbred strains for the study of individual differences in pain related phenotypes in the mouse: Elissa J. Chesler''s 2002 dissertation, discussing issues relevant to the integration of genomic and phenomic data from standard inbred strains including genetic interactions with laboratory environmental conditions and the use of various in silico inbred strain haplotype based mapping algorithms for QTL analysis. * SNP QTL MAPPER in EXCEL format (572 KB, updated January 2002 by Elissa Chesler): This Excel workbook implements the Grupe et al. mapping method and outputs correlation plots. The main spreadsheet allows you to enter your own strain data and compares them to haplotypes. Be very cautious and skeptical when using this spreadsheet and the technique. Read all of the caveates. This excel version of the method was developed by Elissa Chesler. This updated version (Jan 2002) handles missing data. * MIT SNP Database (tab-delimited text format): This file is suitable for manipulation in statistics and spreadsheet programs (752 KB, Updated June 27, 2001). Data have been formatted in a way that allows rapid acquisition of the new data from the Roche Bioscience SNP database. * MIT SNP Database (FileMaker 5 Version): This is a reformatted version of the MIT Single Nucleotide Polymorphism (SNP) database in FileMaker 5 format. You will need a copy of this application to open the file (Mac and Windows; 992 KB. Updated July 13, 2001 by RW). * Gene Mapping and Map Manager Data Sets: Genetic maps of mouse chromosomes. Now includes a 10th generation advanced intercross consisting of 500 animals genetoyped at 340 markers. Lots of older files on recombinant inbred strains. * The Portable Dictionary of the Mouse Genome, 21,039 loci, 17,912,832 bytes. Includes all 1997-98 Chromosome Committee Reports and MIT Release 15. * FullDict.FMP.sit: The Portable Dictionary of the Mouse Genome. This large FileMaker Pro 3.0/4.0 database has been compressed with StuffIt. The Dictionary of the Mouse Genome contains data from the 1997-98 chromosome committee reports and MIT Whitehead SSLP databases (Release 15). The Dictionary contains information for 21,039 loci. File size = 4846 KB. Updated March 19, 1998. * MIT Microsatellite Database ONLINE: A database of MIT microsatellite loci in the mouse. Use this FileMaker Pro database with OurPrimersDB. MITDB is a subset of the Portable Dictionary of the Mouse Genome. ONLINE. Updated July 12, 2001. * MIT Microsatellite Database: A database of MIT microsatellite loci in the mouse. Use this FileMaker Pro database with OurPrimersDB. MITDB is a subset of the Portable Dictionary of the Mouse Genome. File size = 3.0 MB. Updated March 19, 1998. * OurPrimersDB: A small database of primers. Download this database if you are using numerous MIT primers to map genes in mice. This database should be used in combination with the MITDB as one part of a relational database. File size = 149 KB. Updated March 19, 1998. * Empty copy (clone) of the Portable Dictionary in FileMaker Pro 3.0 format. Download this file and import individual chromosome text files from the table into the database. File size = 231 KB. Updated March 19, 1998. * Chromosome Text Files from the Dictionary: The table lists data on gene loci for individual chromosomes.

Proper citation: Mouse Genome Databases (RRID:SCR_007147) Copy   

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http://www.genoscope.cns.fr/externe/tetraodon/

The initial objective of Genoscope was to compare the genomic sequences of this fish to that of humans to help in the annotation of human genes and to estimate their number. This strategy is based on the common genetic heritage of the vertebrates: from one species of vertebrate to another, even for those as far apart as a fish and a mammal, the same genes are present for the most part. In the case of the compact genome of Tetraodon, this common complement of genes is contained in a genome eight times smaller than that of humans. Although the length of the exons is similar in these two species, the size of the introns and the intergenic sequences is greatly reduced in this fish. Furthermore, these regions, in contrast to the exons, have diverged completely since the separation of the lineages leading to humans and Tetraodon. The Exofish method, developed at Genoscope, exploits this contrast such that the conserved regions which can be identified by comparing genomic sequences of the two species, correspond only to coding regions. Using preliminary sequencing results of the genome of Tetraodon in the year 2000, Genoscope evaluated the number of human genes at about 30,000, whereas much higher estimations were current. The progress of the annotation of the human genome has since supported the Genoscope hypothesis, with values as low as 22,000 genes and a consensus of around 25,000 genes. The sequencing of the Tetraodon genome at a depth of about 8X, carried out as a collaboration between Genoscope and the Whitehead Institute Center for Genome Research (now the Broad Institute), was finished in 2002, with the production of an assembly covering 90 of the euchromatic region of the genome of the fish. This has permitted the application of Exofish at a larger scale in comparisons with the genome of humans, but also with those of the two other vertebrates sequenced at the time (Takifugu, a fish closely related to Tetraodon, and the mouse). The conserved regions detected in this way have been integrated into the annotation procedure, along with other resources (cDNA sequences from Tetraodon and ab initio predictions). Of the 28,000 genes annotated, some families were examined in detail: selenoproteins, and Type 1 cytokines and their receptors. The comparison of the proteome of Tetraodon with those of mammals has revealed some interesting differences, such as a major diversification of some hormone systems and of the collagen molecules in the fish. A search for transposable elements in the genomic sequences of Tetraodon has also revealed a high diversity (75 types), which contrasts with their scarcity; the small size of the Tetraodon genome is due to the low abundance of these elements, of which some appear to still be active. Another factor in the compactness of the Tetraodon genome, which has been confirmed by annotation, is the reduction in intron size, which approaches a lower limit of 50-60 bp, and which preferentially affects certain genes. The availability of the sequences from the genomes of humans and mice on one hand, and Takifugu and Tetraodon on the other, provide new opportunities for the study of vertebrate evolution. We have shown that the level of neutral evolution is higher in fish than in mammals. The protein sequences of fish also diverge more quickly than those of mammals. A key mechanism in evolution is gene duplication, which we have studied by taking advantage of the anchoring of the majority of the sequences from the assembly on the chromosomes. The result of this study speaks strongly in favor of a whole genome duplication event, very early in the line of ray-finned fish (Actinopterygians). An even stronger evidence came from synteny studies between the genomes of humans and Tetraodon. Using a high-resolution synteny map, we have reconstituted the genome of the vertebrate which predates this duplication - that is, the last common ancestor to all bony vertebrates (most of the vertebrates apart from cartilaginous fish and agnaths like lamprey). This ancestral karyotype contains 12 chromosomes, and the 21 Tetraodon chromosomes derive from it by the whole genome duplication and a surprisingly small number of interchromosomal rearrangements. On the contrary, exchanges between chromosomes have been much more frequent in the lineage that leads to humans. Sponsors: The project was supported by the Consortium National de Recherche en Genomique and the National Human Genome Research Institute.

Proper citation: Tetraodon Genome Browser (RRID:SCR_007079) Copy   

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http://www.broadinstitute.org/

Biomedical and genomic research center located in Cambridge, Massachusetts, United States. Nonprofit research organization under the name Broad Institute Inc., and is partners with Massachusetts Institute of Technology, Harvard University, and the five Harvard teaching hospitals. Dedicated to advance understanding of biology and treatment of human disease to improve human health.

Proper citation: Broad Institute (RRID:SCR_007073) Copy   

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http://www.aneurist.org/

Project focused on cerebral aneurysms and provides integrated decision support system to assess risk of aneurysm rupture in patients and to optimize their treatments. IT infrastructure has been developeded for management and processing of vast amount of heterogeneous data acquired during diagnosis.

Proper citation: aneurIST (RRID:SCR_007427) Copy   

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http://human.brain-map.org/static/brainexplorer

Multi modal atlas of human brain that integrates anatomic and genomic information, coupled with suite of visualization and mining tools to create open public resource for brain researchers and other scientists. Data include magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), histology and gene expression data derived from both microarray and in situ hybridization (ISH) approaches. Brain Explorer 2 is desktop software application for viewing human brain anatomy and gene expression data in 3D.

Proper citation: Allen Human Brain Atlas (RRID:SCR_007416) Copy   

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http://www.hapsample.org/

Web application for simulating SNP genotypes for case-control and affected-child trio studies by resampling from Phase I/II HapMap SNP data. The user provides a list of SNPs to be genotyped, along with a disease model file that describes causal SNPs and their effect sizes. The simulation tool is appropriate for candidate regions or whole-genome scans. (entry from Genetic Analysis Software)

Proper citation: HAP-SAMPLE (RRID:SCR_009234) Copy   

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http://pages.stat.wisc.edu/~yandell/qtl/software/qtlbim/

Software library for QTL Bayesian Interval Mapping that provides a Bayesian model selection approach to map multiple interacting QTL. It works on experimentally inbred lines and performs a genome-wide search to locate multiple potential QTL. The package can handle continuous, binary and ordinal traits. (entry from Genetic Analysis Software)

Proper citation: R/QTLBIM (RRID:SCR_009375) Copy   

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http://bio2rdf.blogspot.com/

This is a blog about post genomic knowledge. The website''s goal is to make public datasets from the bioinformatics community available in RDF format via standard SPARQL endpoints.

Proper citation: Bio2RDF atlas of post genomic knowledge (RRID:SCR_007991) Copy   

•   Source: SciCrunch Registry