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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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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://discover.nci.nih.gov/gominer/

GoMiner is a tool for biological interpretation of "omic" data including data from gene expression microarrays. Omic experiments often generate lists of dozens or hundreds of genes that differ in expression between samples, raising the question, What does it all mean biologically? To answer this question, GoMiner leverages the Gene Ontology (GO) to identify the biological processes, functions and components represented in these lists. Instead of analyzing microarray results with a gene-by-gene approach, GoMiner classifies the genes into biologically coherent categories and assesses these categories. The insights gained through GoMiner can generate hypotheses to guide additional research. GoMiner displays the genes within the framework of the Gene Ontology hierarchy in two ways: * In the form of a tree, similar to that in AmiGO * In the form of a "Directed Acyclic Graph" (DAG) The program also provides: * Quantitative and statistical analysis * Seamless integration with important public databases GoMiner uses the databases provided by the GO Consortium. These databases combine information from a number of different consortium participants, include information from many different organisms and data sources, and are referenced using a variety of different gene product identification approaches.

Proper citation: GoMiner (RRID:SCR_002360) Copy   

•   Source: SciCrunch Registry

http://galton.uchicago.edu/~junzhang/LAPSTRUCT.html

Software application to describe population structure using biomarker data ( typically SNPs, CNVs etc.) available in a population sample. The main features different from PCA are: (1) geometrically motivated and graphic model based; (2)robustness of outliers. (entry from Genetic Analysis Software)

Proper citation: LAPSTRUCT (RRID:SCR_007550) Copy   

•   Source: SciCrunch Registry

http://www.pedigree-draw.com/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on April 12,2024. Software application for pedigree drawing (entry from Genetic Analysis Software)

Proper citation: Pedigree-Draw (RRID:SCR_008302) Copy   

•   Source: SciCrunch Registry

http://bnmc.caltech.edu/

The Beckman Institute BNMC brings together researchers from many disciplines at Caltech to address problems in the mechanistic modeling of coupled genomic, intercellular and intracellular processes. It represents an attempt to encourage closer interaction and collaboration between groups in Biology, Control and Dynamical Systems, and the Center for Advanced Computing Research. The focus of BNMC is biochemical phenomena occurring within and between cells, in particular the mechanistic modeling of molecular networks of all kinds (e.g., transcriptional, regulatory, metabolic, signal transduction, mechanical, etc.) with and without spatial variation and intercellular communication. BNMC is formed as a coordinated effort aimed at (1) applying existing capabilities to collaboratively solve biological modeling problems that arise in answering scientific questions in Caltech laboratories, (2) exploring a diversity of novel approaches in order to achieve fundamental advances necessary to address the classes of modeling problems biologists want to solve, and (3) organizing projects to better share human experience as well as common infrastructure to avoid duplication and maximize solution interoperability.

Proper citation: Caltech, The Beckman Institute: The Biological Network Modeling Center (RRID:SCR_008060) Copy   

•   Source: SciCrunch Registry

http://dunham.gs.washington.edu/protocols.shtml

A portal for Maitreya Dunham's lab, which works on the genomic analysis of experimental evolution in yeast using microarrays and the chemostat. Research interests of the lab include experimental evolution of genetic networks in yeast, aneuploidy and copy number variation, comparative genomics, technology development and human genetics in yeast.

Proper citation: Maitreya Dunham's Lab (RRID:SCR_000784) Copy   

•   Source: SciCrunch Registry

http://www.fged.org/

Society that develop standards for biological research data quality, annotation and exchange. They facilitate the creation and use of software tools that build on these standards and allow researchers to annotate and share their data easily. They promote scientific discovery that is driven by genome wide and other biological research data integration and meta-analysis. Historically, FGED began with a focus on microarrays and gene expression data. However, the scope of FGED now includes data generated using any technology when applied to genome-scale studies of gene expression, binding, modification and other related applications.

Proper citation: FGED (RRID:SCR_001897) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/web/packages/ibdreg/index.html

Software package in S-PLUS and R to test genetic linkage with covariates by regression methods with response IBD sharing for relative pairs. Account for correlations of IBD statistics and covariates for relative pairs within the same pedigree. (entry from Genetic Analysis Software)

Proper citation: IBDREG (RRID:SCR_013127) Copy   

•   Source: SciCrunch Registry

https://nels.bioinfo.no

Web portal for the administration of Norwegian e-Infrastructure for Life Sciences. Enables Norwegian life scientists and their international collaborators to store, share, archive, and analyse their genomics scale data. NeLS is one of the packages of the ELIXIR.NO project.

Proper citation: NeLS (RRID:SCR_016301) Copy   

•   Source: SciCrunch Registry

https://kona.nhgri.nih.gov/mnemiopsis/

Portal to obtain genomic information on Mnemiopsis. Data available provide annotations and other key biological information not available elsewhere. Used to advance research projects aimed at understanding phylogenetic diversity and evolution of proteins that play fundamental role in metazoan development. Collection of sequenced, assembled, annotated, and performed preliminary analysis of genome of Mnemiopsis.

Proper citation: Mnemiopsis Genome Project Portal (RRID:SCR_018293) Copy   

•   Source: SciCrunch Registry

https://github.com/INTABiotechMJ/MITE-Tracker

Open source software tool for identifying miniature inverted repeat transposable elements in large genomes. Used to process large scale genomes, to find and classify MITEs using an efficient alignment strategy to retrieve nearby inverted repeat sequences.

Proper citation: MITE-Tracker (RRID:SCR_017030) Copy   

•   Source: SciCrunch Registry

https://bioservices.uncc.edu

Core to assist with analyzing and interpreting data produced by genomic technologies.

Proper citation: University of North Carolina Charlotte Bioinformatics Services Division (RRID:SCR_017182) Copy   

•   Source: SciCrunch Registry

https://www.cityofhope.org/research/shared-resources/integrative-genomics-and-bioinformatics

Core provides genomic and bioinformatics services to City of Hope Comprehensive Cancer Center (COHCCC) investigators.

Proper citation: City of Hope National Medical Center Integrative Genomics and Bioinformatics Core Facility (RRID:SCR_017188) Copy   

•   Source: SciCrunch Registry

https://github.com/ZSI-Bio/bdg-sequila

Software tool for genomic intervals querying and processing built on top of Apache Spark. Elastic, fast and scalable SQL oriented solution for processing and querying genomic intervals.

Proper citation: SeQuiLa (RRID:SCR_017220) Copy   

•   Source: SciCrunch Registry

https://www.bmh.manchester.ac.uk/research/facilities/bioinformatics/

Core provides assistance in integrative analysis of genomic datasets to support faculty.

Proper citation: University of Manchester Bioinformatics Core Facility (RRID:SCR_017171) Copy   

•   Source: SciCrunch Registry

https://glomcon.org

Consortium to bring together clinicians, pathologists, researchers, and biotech innovators to create scalable network of stakeholders interested in helping patients with glomerular kidney disease. Makes collective expertise of its members available for discussion of individual cases, provides infrastructure for biomarker studies, enables genomic research, and facilitates clinical trials.

Proper citation: Glomerular Disease Study & Trial Consortium (RRID:SCR_017264) Copy   

•   Source: SciCrunch Registry

https://anvilproject.org/

Portal to facilitate integration and computing on and across large datasets generated by NHGRI programs, as well as initiatives funded by National Institutes of Health or by other agencies that support human genomics research. Resource for genomic scientific community, that leverages cloud based infrastructure for democratizing genomic data access, sharing and computing across large genomic, and genomic related data sets. Component of federated data ecosystem, and is expected to collaborate and integrate with other genomic data resources through adoption of FAIR (Findable, Accessible, Interoperable, Reusable) principles, as their specifications emerge from scientific community. Will provide collaborative environment, where datasets and analysis workflows can be shared within consortium and be prepared for public release to broad scientific community through AnVIL user interfaces.

Proper citation: Analysis, Visualization, and Informatics Lab-space (AnVIL) (RRID:SCR_017469) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/TCGAbiolinks/

Software R Bioconductor package for integrative analysis with TCGA data.TCGAbiolinks is able to access National Cancer Institute Genomic Data Commons thorough its GDC Application Programming Interface to search, download and prepare relevant data for analysis in R.

Proper citation: TCGAbiolinks (RRID:SCR_017683) Copy   

•   Source: SciCrunch Registry