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http://www.ncbi.nlm.nih.gov/genomes/GenomesHome.cgi?taxid=2759&hopt=html

Curated sequence data and related information on organelles from NCBI Refseq for the community to use as a standard. The animal mitochondrial records are considered reviewed; that is, they have been manually curated by the NCBI staff. Other mitochondrial and chloroplast genome records are provisional and are presented with varying levels of review compared to the primary record used to build the RefSeq. Additionally, protein clusters for the metazoan and plastid genomes proteins can be reviewed with Entrez Protein Clusters.

Proper citation: Organelle Genome Resources (RRID:SCR_007838) Copy   

•   Source: SciCrunch Registry

http://statgen.ncsu.edu/asg/

Alternative splicing essentially increases the diversity of the transcriptome and has important implications for physiology, development and the genesis of diseases. This resource uses a different approach to investigate alternative splicing (instead of the conventional case-by case fashion) and integrates all transcripts derived from a gene into a single splicing graph. ASG is a database of splicing graphs for human genes, using transcript information from various major sources (Ensembl, RefSeq, STACK, TIGR and UniGene). Each transcript corresponds to a path in the graph, and alternative splicing is displayed by bifurcations. This representation preserves the relationships between different splicing variants and allows us to investigate systematically all possible putative transcripts. Web interface allows users to display the splicing graphs, to interactively assemble transcripts and to access their sequences as well as neighboring genomic regions. ASG also provide for each gene, an exhaustive pre-computed catalog of putative transcriptsin total more than 1.2 million sequences. It has found that ~65 of the investigated genes show evidence for alternative splicing, and in 5 of the cases, a single gene might produce over 100 transcripts.

Proper citation: Alternate splicing gallery (RRID:SCR_008129) Copy   

•   Source: SciCrunch Registry

https://wiki.cgb.indiana.edu/display/DGC/Home

The Daphnia Genomics Consortium (DGC) is an international network of investigators committed to mounting the freshwater crustacean Daphnia as a model system for ecology, evolution and the environmental sciences. Along with research activities, the DGC is: (1) coordinating efforts towards developing the Daphnia genomic toolbox, which will then be available for use by the general community; (2) facilitating collaborative cross-disciplinary investigations; (3) developing bioinformatic strategies for organizing the rapidly growing genome database; and (4) exploring emerging technologies to improve high throughput analyses of molecular and ecological samples. If we are to succeed in creating a new model system for modern life-sciences research, it will need to be a community-wide effort. Research activities of the DGC are primarily focused on creating genomic tools and information. When completed, the current projects will offer a first view of the Daphnia genome''s topography, including regions of high and low recombination, the distribution of transposable, repetitive and regulatory elements, the size and structure of genes and of their neighborhoods. This information is crucial in formulating testable hypotheses relating genetics and demographics to the evolutionary potential or constraints of natural populations. Projects aiming to compile identifiable genes with their function are also underway, together with robust methods to verify these findings. Finally, these tools are being tested, by exploring their uses in key ecological and toxicological investigations. Each project benefits from the leadership and expertise of many individuals. For further details, begin by contacting the project directors. The DGC consists of biologists from a broad spectrum of subdisciplines, including limnology, ecotoxicology, quantitative and population genetics, systematics, molecular biology and evolution, developmental biology, genomics and bioinformatics. In many regards, the rapid early success of the consortium results from its grass-roots origin promoting an international composition, under a cooperative model, with significant scientific breadth. We hold to this approach in building this network and encourage more people to participate. All the while, the DGC is structured to effectively reach specific goals. The consortium includes an advisory board (composed of experts of the various subdisciplines), whose responsibility is to act as the research community''s agent in guiding the development of Daphnia genomic resources. The advisors communicate directly to DGC members, who are either contributing genomic tools or actively seeking funds for this function. The consortium''s main body (given the widespread interest in applying genomic tools in environmental studies) are the affiliates, who make use of these tools for their research and who are soliciting support.

Proper citation: Daphnia genomics consortium (RRID:SCR_008148) Copy   

•   Source: SciCrunch Registry

http://baygenomics.ucsf.edu/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 29, 2016. The BayGenomics gene-trap resource provides researchers with access to thousands of mouse embryonic stem (ES) cell lines harboring characterized insertional mutations in both known and novel genes. The major goal of BayGenomics is to identify genes relevant to cardiovascular and pulmonary disease.

Proper citation: BayGenomics (RRID:SCR_008168) Copy   

•   Source: SciCrunch Registry

http://www.uni-wh.de/pcogr

THIS RESOURCE IS NO LONGER IN SERVICE, documented on August 20,2019.The COG-database has become a powerful tool in the field of comparative genomics. The construction of this data-base is based on sequence homologies of proteins from different completely sequenced genomes. Highly homologous proteins are assigned to clusters of orthologous groups. The updated collection of orthologous protein sets for prokaryotes and eukaryotes is expected to be a useful platform for functional annotation of newly sequenced genomes, including those of complex eukaryotes, and genome-wide evolutionary studies. The availability of multiple, essentially complete genome sequences of prokaryotes and eukaryotes spurred both the demand and the opportunity for the construction of an evolutionary classification of genes from these genomes. Such a classification system based on orthologous relationships between genes appears to be a natural framework for comparative genomics and should facilitate both functional annotation of genomes and large-scale evolutionary studies. Here is a major update of the previously developed system for delineation of Clusters of Orthologous Groups of proteins (COGs) from the sequenced genomes of prokaryotes and unicellular eukaryotes and the construction of clusters of predicted orthologs for 7 eukaryotic genomes, which we named KOGs after eukaryotic orthologous groups. The COG collection currently consists of 138,458 proteins, which form 4873 COGs and comprise 75% of the 185,505 (predicted) proteins encoded in 66 genomes of unicellular organisms. The eukaryotic orthologous groups (KOGs) include proteins from 7 eukaryotic genomes: three animals (the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster and Homo sapiens), one plant, Arabidopsis thaliana, two fungi (Saccharomyces cerevisiae and Schizosaccharomyces pombe), and the intracellular microsporidian parasite Encephalitozoon cuniculi. The current KOG set consists of 4852 clusters of orthologs, which include 59,838 proteins, or approximately 54% of the analyzed eukaryotic 110,655 gene products. Compared to the coverage of the prokaryotic genomes with COGs, a considerably smaller fraction of eukaryotic genes could be included into the KOGs; addition of new eukaryotic genomes is expected to result in substantial increase in the coverage of eukaryotic genomes with KOGs. Examination of the phyletic patterns of KOGs reveals a conserved core represented in all analyzed species and consisting of approximately 20% of the KOG set. This conserved portion of the KOG set is much greater than the ubiquitous portion of the COG set (approximately 1% of the COGs). In part, this difference is probably due to the small number of included eukaryotic genomes, but it could also reflect the relative compactness of eukaryotes as a clade and the greater evolutionary stability of eukaryotic genomes.

Proper citation: Phylogenetic Clusters of Orthologous Groups Ranking (RRID:SCR_008223) Copy   

•   Source: SciCrunch Registry

http://www.nisc.nih.gov/projects/comp_seq.html

Generates data for use in developing and refining computational tools for comparing genomic sequence from multiple species. The NISC Comparative Sequencing Program's goal is to establish a data resource consisting of sequences for the same set of targeted genomic regions derived from multiple animal species. The broader program includes plans for a diverse set of analytical studies using the generated sequence and the publication of a series of papers describing the results of those analysis in peer-reviewed journals in a timely fashion. Experimentally, this project involves the shotgun sequencing of mapped BAC clones. For each BAC, an assembly is first performed when a sufficient number of sequence reads have been generated to provide full shotgun coverage of the clone. At that time, the assembled sequence is submitted to the HTGS division of GenBank. Subsequent refinements of the sequence, including the generation of higher-accuracy finished sequence, results in the updating of the sequence record in GenBank. By immediately submitting our BAC-derived sequences to GenBank, it makes their data available as a public service to allow colleagues to speed up their research, consistent with the now well-established routine of sequencing centers participating in the Human Genome Project. However, at the same time, it has made considerable investment in acquiring these mapping and sequence data, including sizable efforts of graduate students, postdoctoral fellows, and other trainees. Furthermore, in most cases, large data sets involving multiple BAC sequences from multiple species must first be generated, often taking many months to accumulate, before the planned analysis can be performed and the resulting papers written and submitted for publication.

Proper citation: Comparative Vertebrate Sequencing (RRID:SCR_008213) Copy   

•   Source: SciCrunch Registry

http://iomics.us/

A genomics data analysis platform which generates decision models for healthcare organizations and medical research. This service is meant to utilize data through machine learning methods.

Proper citation: iOMICS (RRID:SCR_000239) Copy   

•   Source: SciCrunch Registry

https://github.com/wtsi-npg/Illuminus

A fast and accurate algorithm for assigning single nucleotide polymorphism (SNP) genotypes to microarray data from the Illumina BeadArray technology.

Proper citation: ILLUMINUS (RRID:SCR_000388) Copy   

•   Source: SciCrunch Registry

http://cedar.genetics.soton.ac.uk/pub/PROGRAMS/ldb;

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. Software application that integrate genetic linkage map and physical map (entry from Genetic Analysis Software)

Proper citation: LDB/LDB+ (RRID:SCR_000839) Copy   

•   Source: SciCrunch Registry

http://research.calit2.net/hap/

Software application (entry from Genetic Analysis Software)

Proper citation: HAP 1 (RRID:SCR_000837) Copy   

•   Source: SciCrunch Registry

http://www.biostat.harvard.edu/complab/dchip/snp.htm

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 22, 2016.

Proper citation: DCHIP LINKAGE (RRID:SCR_000835) Copy   

•   Source: SciCrunch Registry

http://faculty.washington.edu/browning/floss/floss.htm

Software application that performs ordered subset analysis using MERLIN's ouput .lod file created with the --perFamily option. Ordered subset analysis uses covariate information to identify a more homogenous subset of families for linkage analysis. The homogeneous subset of families does not need to be specified a priori, and the covariates can include environmental exposures, quantitative traits, or linkage scores at another locus in the genome. The evidence for linkage is evaluated with a permutation test. (entry from Genetic Analysis Software)

Proper citation: FLOSS (RRID:SCR_000836) Copy   

•   Source: SciCrunch Registry

http://null

Software application for calculating the heterozygosity, PIC, and LIC values for polymorphic markers (entry from Genetic Analysis Software), THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: POLYMORPHISM (RRID:SCR_000828) Copy   

•   Source: SciCrunch Registry

https://github.com/gaow/genetic-analysis-software/blob/master/pages/EDAC.md

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 22, 2016.

Proper citation: EDAC (RRID:SCR_000829) Copy   

•   Source: SciCrunch Registry

https://github.com/gaow/genetic-analysis-software/blob/master/pages/2LD.md

Software program for calculating linkage disequilibrium (LD) measures between two polymorphic markers.

Proper citation: 2LD (RRID:SCR_000826) Copy   

•   Source: SciCrunch Registry

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

Software application (entry from Genetic Analysis Software)

Proper citation: SQTL (RRID:SCR_000827) Copy   

•   Source: SciCrunch Registry

http://solar-eclipse-genetics.org

A flexible and extensive software package for genetic variance components analysis, including linkage analysis, quantitative genetic analysis, and covariate screening. Operations are included for calculation of marker-specific or multipoint identity-by-descent (IBD) matrices in pedigrees of arbitrary size and complexity, and for linkage analysis of quantitative traits which may involve multiple loci (oligogenic analysis), dominance effects, and epistasis. (entry from Genetic Analysis Software)

Proper citation: SOLAR (RRID:SCR_000850) Copy   

•   Source: SciCrunch Registry

http://www-rcf.usc.edu/~gqian/software.htm (not available)

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

Proper citation: MRH (RRID:SCR_000841) Copy   

•   Source: SciCrunch Registry

http://www.biosciences-labs.bham.ac.uk/Kearsey/

Software application providing a user freiendly way to perform QTL analysis. The software currently allows 3 types of QTL analysis: (1) single marker ANOVA. (2) marker regression. (3) interval mapping by regression. (entry from Genetic Analysis Software)

Proper citation: QTL CAFE (RRID:SCR_000844) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/fdudbridge/software/pelican

Software utility for graphically editing the pedigree data files used by programs such as FASTLINK, VITESSE, GENEHUNTER and MERLIN. It can read in and write out pedigree files, saving changes that have been made to the structure of the pedigree. Changes are made to the pedigree via a graphical display interface. The resulting display can be saved as a pedigree file and as a graphical image file.

Proper citation: PELICAN (RRID:SCR_001695) Copy   

•   Source: SciCrunch Registry