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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://htslib.org/

Original SAMTOOLS package has been split into three separate repositories including Samtools, BCFtools and HTSlib. Samtools for manipulating next generation sequencing data used for reading, writing, editing, indexing,viewing nucleotide alignments in SAM,BAM,CRAM format. BCFtools used for reading, writing BCF2,VCF, gVCF files and calling, filtering, summarising SNP and short indel sequence variants. HTSlib used for reading, writing high throughput sequencing data.

Proper citation: SAMTOOLS (RRID:SCR_002105) Copy   

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http://intermine.org/

An open source data warehouse system built for the integration and analysis of complex biological data that enables the creation of biological databases accessed by sophisticated web query tools. Parsers are provided for integrating data from many common biological data sources and formats, and there is a framework for adding data. InterMine includes a user-friendly web interface that works "out of the box" and can be easily customized for specific needs, as well as a powerful, scriptable web-service API to allow programmatic access to data.

Proper citation: InterMine (RRID:SCR_001772) Copy   

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https://github.com/santeripuranen/SpydrPick

Software command line tool for performing direct coupling analysis of aligned categorical datasets. Used for analysis at scale of pan genomes of many bacteria. Incorporates correction for population structure, which adjusts for phylogenetic signal in data without requiring explicit phylogenetic tree.

Proper citation: SpydrPick (RRID:SCR_018176) Copy   

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https://github.com/santeripuranen/SuperDCA

Software tool for global direct coupling analysis of input genome alignments. Implements variant of pseudolikelihood maximization direct coupling analysis, with emphasis on optimizations that enable its use on genome scale. May be used to discover co evolving pairs of loci.Used for genome wide epistasis analysis.

Proper citation: SuperDCA (RRID:SCR_018175) Copy   

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http://www.imperial.ac.uk/research/animallectins

Resource presents information about animal lectins involved in various sugar recognition processes.

Proper citation: genomics resource for animal lectins (RRID:SCR_018122) Copy   

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http://enterobase.warwick.ac.uk/

Integrated software environment that supports identification of global population structures within several bacterial genera that include pathogens. Web service for analyzing and visualizing genomic variation within bacteria. Genome database to enable to identify, analyse, quantify and visualise genomic variation within bacterial genera including Salmonella, Escherichia/Shigella, Clostridioides,Vibrio,Yersinia,Helicobacter,Moraxella.

Proper citation: EnteroBase (RRID:SCR_019019) Copy   

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https://www.ebi.ac.uk/chembl/

Collection of bioactive drug-like small molecules that contains 2D structures, calculated properties and abstracted bioactivities. Used for drug discovery and chemical biology research. Clinical progress of new compounds is continuously integrated into the database.

Proper citation: ChEMBL (RRID:SCR_014042) Copy   

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http://shiny.chemgrid.org/boxplotr/

Web tool written in R for generation of box plots with R packages shiny, beanplot4, vioplot, beeswarm and RColorBrewer, and hosted on shiny server to allow for interactive data analysis. Data are held temporarily and discarded as soon as session terminates.Represents both summary statistics and distribution of primary data. Enables visualization of minimum, lower quartile, median, upper quartile and maximum of any data set.Data matrix can be uploaded as file or pasted into application. May be downloaded to run locally or as virtual machine for VMware and VirtualBox.

Proper citation: BoxPlotR (RRID:SCR_015629) Copy   

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https://github.com/sanger-pathogens/Bio-Tradis

Analysis software for the output from TraDIS (Transposon Directed Insertion Sequencing) analyses of dense transposon mutant libraries. The Bio-Tradis analysis pipeline is implemented as an extensible Perl library which can either be used as is, or as a basis for the development of more advanced analysis tools.

Proper citation: Bio-tradis (RRID:SCR_015993) Copy   

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http://bioconductor.org/packages/release/bioc/html/SC3.html

Software tool for the unsupervised clustering of cells from single cell RNA-Seq experiments. SC3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients.

Proper citation: SC3 (RRID:SCR_015953) Copy   

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https://github.com/neurodroid/stimfit

Software for viewing and analyzing electrophysiological data. It features an embedded Python shell that allows you to extend the program functionality by using numerical libraries such as NumPy and SciPy.

Proper citation: Stimfit (RRID:SCR_016050) Copy   

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http://www.xavierdidelot.xtreemhost.com/clonalframe.htm

Software package for the inference of bacterial microevolution using multilocus sequence data. It is used to identify the clonal relationships between the members of a sample, while also estimating the chromosomal position of homologous recombination events that have disrupted the clonal inheritance.

Proper citation: Clonalframe (RRID:SCR_016060) Copy   

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https://sanger-pathogens.github.io/gubbins/

Software application as an algorithm that iteratively identifies loci containing elevated densities of base substitutions while concurrently constructing a phylogeny based on the putative point mutations outside of these regions. It is used for phylogenetic analysis of genome sequences and generating highly accurate reconstructions under realistic models of short-term bacterial evolution., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Gubbins (RRID:SCR_016131) Copy   

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http://bids.neuroimaging.io

Standard specification for organizing and describing outputs of neuroimaging experiments. Used to organize and describe neuroimaging and behavioral data by neuroscientific community as standard to organize and share data. BIDS prescribes file naming conventions and folder structure to store data in set of already existing file formats. Provides standardized templates to store associated metadata in form of Javascript Object Notation (JSON) and tab-separated value (TSV) files. Facilitates data sharing, metadata querying, and enables automatic data analysis pipelines. System to curate, aggregate, and annotate neuroimaging databases. Intended for magnetic resonance imaging data, magnetoencephalography data, electroencephalography data, and intracranial encephalography data.

Proper citation: Brain Imaging Data Structure (BIDs) (RRID:SCR_016124) Copy   

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http://cancer.sanger.ac.uk/cancergenome/projects/cosmic/

Database to store and display somatic mutation information and related details and contains information relating to human cancers. The mutation data and associated information is extracted from the primary literature. In order to provide a consistent view of the data a histology and tissue ontology has been created and all mutations are mapped to a single version of each gene. The data can be queried by tissue, histology or gene and displayed as a graph, as a table or exported in various formats.
Some key features of COSMIC are:
* Contains information on publications, samples and mutations. Includes samples which have been found to be negative for mutations during screening therefore enabling frequency data to be calculated for mutations in different genes in different cancer types.
* Samples entered include benign neoplasms and other benign proliferations, in situ and invasive tumours, recurrences, metastases and cancer cell lines.

Proper citation: COSMIC - Catalogue Of Somatic Mutations In Cancer (RRID:SCR_002260) Copy   

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http://www.sharmuk.org/

A not for profit organization to accelerate research into aging by sharing resources: providing access to cost and time effective, aged murine tissue through a biorepository and database of live ageing colonies, as well as promoting the networking of researchers and dissemination of knowledge through its online collaborative environment; MiCEPACE. ShARM will provide valuable resources for the scientific community while helping to reduce the number of animals used in vital research into aging. The biobank of tissue and networking facility will enable scientists to access shared research material and data. By making use of collective resources, the number of individual animals required in research experiments can be minimized. The project also has the added value of helping to reduce the costs of research by connecting scientists, pooling resource and combining knowledge. ShARM works in partnership with MRC Harwell and the Centre for Intergrated Research into Musculoskeletal Ageing (CIMA).

Proper citation: ShARM (RRID:SCR_003120) Copy   

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http://caps.ncbs.res.in/3dswap/index.html

Curated knowledegbase of protein structures that are reported to be involved in 3-dimensional domain swapping. 3DSwap provides literature curated information and structure related information about 3D domain swapping in proteins. Information about swapping, hinge region, swapped region, extent of swapping, etc. are extracted from original research publications after extensive literature curation.

Proper citation: 3DSwap (RRID:SCR_004133) Copy   

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http://www.hgsc.bcm.tmc.edu/content/hapmap-3-and-encode-3

Draft release 3 for genome-wide SNP genotyping and targeted sequencing in DNA samples from a variety of human populations (sometimes referred to as the HapMap 3 samples). This release contains the following data: * SNP genotype data generated from 1184 samples, collected using two platforms: the Illumina Human1M (by the Wellcome Trust Sanger Institute) and the Affymetrix SNP 6.0 (by the Broad Institute). Data from the two platforms have been merged for this release. * PCR-based resequencing data (by Baylor College of Medicine Human Genome Sequencing Center) across ten 100-kb regions (collectively referred to as ENCODE 3) in 712 samples. Since this is a draft release, please check this site regularly for updates and new releases. The HapMap 3 sample collection comprises 1,301 samples (including the original 270 samples used in Phase I and II of the International HapMap Project) from 11 populations, listed below alphabetically by their 3-letter labels. Five of the ten ENCODE 3 regions overlap with the HapMap-ENCODE regions; the other five are regions selected at random from the ENCODE target regions (excluding the 10 HapMap-ENCODE regions). All ENCODE 3 regions are 100-kb in size, and are centered within each respective ENCODE region. The HapMap 3 and ENCORE 3 data are downloadable from the ftp site.

Proper citation: HapMap 3 and ENCODE 3 (RRID:SCR_004563) Copy   

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http://pfam.xfam.org/

A database of protein families, each represented by multiple sequence alignments and hidden Markov models (HMMs). Users can analyze protein sequences for Pfam matches, view Pfam family annotation and alignments, see groups of related families, look at the domain organization of a protein sequence, find the domains on a PDB structure, and query Pfam by keywords. There are two components to Pfam: Pfam-A and Pfam-B. Pfam-A entries are high quality, manually curated families that may automatically generate a supplement using the ADDA database. These automatically generated entries are called Pfam-B. Although of lower quality, Pfam-B families can be useful for identifying functionally conserved regions when no Pfam-A entries are found. Pfam also generates higher-level groupings of related families, known as clans (collections of Pfam-A entries which are related by similarity of sequence, structure or profile-HMM).

Proper citation: Pfam (RRID:SCR_004726) Copy   

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