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http://t1dbase.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 26,2019. In October 2016, T1DBase has merged with its sister site ImmunoBase (https://immunobase.org). Documented on March 2020, ImmunoBase ownership has been transferred to Open Targets (https://www.opentargets.org). Results for all studies can be explored using Open Targets Genetics (https://genetics.opentargets.org). Database focused on genetics and genomics of type 1 diabetes susceptibility providing a curated and integrated set of datasets and tools, across multiple species, to support and promote research in this area. The current data scope includes annotated genomic sequences for suspected T1D susceptibility regions; genetic data; microarray data; and global datasets, generally from the literature, that are useful for genetics and systems biology studies. The site also includes software tools for analyzing the data.

Proper citation: T1DBase (RRID:SCR_007959) Copy   

•   Source: SciCrunch Registry

http://rfam.xfam.org/

The Rfam database is a collection of RNA families, each represented by multiple sequence alignments, consensus secondary structures and covariance models (CMs). The families in Rfam break down into three broad functional classes: Non-coding RNA genes, structured cis-regulatory elements and self-splicing RNAs. Typically these functional RNAs often have a conserved secondary structure which may be better preserved than the RNA sequence. The CMs used to describe each family are a slightly more complicated relative of the profile hidden Markov models (HMMs) used by Pfam. CMs can simultaneously model RNA sequence and the structure in an elegant and accurate fashion. Rfam is also available via FTP. You can find data in Rfam in various ways... * Analyze your RNA sequence for Rfam matches * View Rfam family annotation and alignments * View Rfam clan details * Query Rfam by keywords * Fetch families or sequences by NCBI taxonomy * Enter any type of accession or ID to jump to the page for a Rfam family, sequence or genome

Proper citation: Rfam (RRID:SCR_007891) Copy   

•   Source: SciCrunch Registry

http://cocomac.g-node.org/main/index.php?

Online access (html or xml) to structural connectivity ("wiring") data on the Macaque brain. The database has become by far the largest of its kind, with data extracted from more than four hundred published tracing studies. The main database, contains data from tracing studies on anatomical connectivity in the macaque cerebral cortex. Also available are a variety of tools including a graphical simulation workbench, map displays and the CoCoMac-Paxinos-3D viewer. Submissions are welcome. To overcome the problem of divergent brain maps ORT (Objective Relational Transformation) was developed, an algorithmic method to convert data in a coordinate- independent way based on logical relations between areas in different brain maps. CoCoMac data is used to analyze the organization of the cerebral cortex, and to establish its structure- function relationships. This includes multi-variate statistics and computer simulation of models that take into account the real anatomy of the primate cerebral cortex. This site * Provides full, scriptable open access to the data in CoCoMac (you must adhere to the citation policy) * Powers the graphical interface to CoCoMac provided by the Scalable Brain Atlas * Sports an extensive search/browse wizard, which automatically constructs complex search queries and lets you further explore the database from the results page. * Allows you to get your hands dirty, by using the custom SQL query service. * Displays connectivity data in tabular form, through the axonal projections service. CoCoMac 2 was initiated at the Donders Institute for Brain, Cognition and Behaviour, and is currently supported by the German neuroinformatics node and the Computational and Systems Neuroscience group at the Juelich research institute.

Proper citation: CoCoMac (RRID:SCR_007277) Copy   

•   Source: SciCrunch Registry

http://caps.ncbs.res.in/imotdb/

Comprehensive collection of spatially interacting motifs in proteins. Interacting motif database lists interacting motifs that are identified for all structural entries in PDB. Conserved patterns or finger prints are identified for individual structural entries and also grouped together for reporting common motifs shared among all superfamily members.

Proper citation: Database of Spatially Interacting Motifs in Proteins (RRID:SCR_007735) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.compbio.dundee.ac.uk/gotcha/gotcha.php

GOtcha provides a prediction of a set of GO terms that can be associated with a given query sequence. Each term is scored independently and the scores calibrated against reference searches to give an accurate percentage likelihood of correctness. These results can be displayed graphically. Why is GOtcha different to what is already out there and why should you be using it? * GOtcha uses a method where it combines information from many search hits, up to and including E-values that are normally discarded. This gives much better sensitivity than other methods. * GOtcha provides a score for each individual term, not just the leaf term or branch. This allows the discrimination between confident assignments that one would find at a more general level and the more specific terms that one would have lower confidence in. * The scores GOtcha provides are calibrated to give a real estimate of correctness. This is expressed as a percentage, giving a result that non-experts are comfortable in interpreting. * GOtcha provides graphical output that gives an overview of the confidence in, or potential alternatives for, particular GO term assignments. The tool is currently web-based; contact David Martin for details of the standalone version. Platform: Online tool

Proper citation: GOtcha (RRID:SCR_005790) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/expressionprofiler/

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. The EP:GO browser is built into EBI's Expression Profiler, a set of tools for clustering, analysis and visualization of gene expression and other genomic data. With it, you can search for GO terms and identify gene associations for a node, with or without associated subnodes, for the organism of your choice.

Proper citation: Expression Profiler (RRID:SCR_005821) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/thornton-srv/databases/FunTree/

FunTree provides a range of data resources to detect the evolution of enzyme function within distant structurally related clusters within domain super families as determined by CATH. To access the resource enter a specific CATH superfamily code or search for a structure / sequence / function (either via a EC code or KEGG ligand / reaction ID, PDB ID or UniProtKB ID). Or browse the resource via superfamily / function / structure / metabolites & reactions via the menu on the left panel. FunTree is a new resource that brings together sequence, structure, phylogenetic, chemical and mechanistic information for structurally defined enzyme superfamilies. Gathering together this range of data into a single resource allows the investigation of how novel enzyme functions have evolved within a structurally defined superfamily as well as providing a means to analyse trends across many superfamilies. This is done not only within the context of an enzyme''''s sequence and structure but also the relationships of their reactions. Developed in tandem with the CATH database, it currently comprises 276 superfamilies covering 1800 (70%) of sequence assigned enzyme reactions. Central to the resource are phylogenetic trees generated from structurally informed multiple sequence alignments using both domain structural alignments supplemented with domain sequences and whole sequence alignments based on commonality of multi-domain architectures. These trees are decorated with functional annotations such as metabolite similarity as well as annotations from manually curated resources such the catalytic site atlas and MACiE for enzyme mechanisms.

Proper citation: FunTree (RRID:SCR_006014) Copy   

•   Source: SciCrunch Registry

http://www.genedb.org/Homepage/Pfalciparum

Database of the most recent sequence updates and annotations for the P. falciparum genome. New annotations are constantly being added to keep up with published manuscripts and feedback from the Plasmodium research community. You may search by Protein Length, Molecular Mass, Gene Type, Date, Location, Protein Targeting, Transmembrane Helices, Product, GO, EC, Pfam ID, Curation and Comments, and Dbxrefs. BLAST and other tools are available. The P. falciparum 3D7 nuclear genome is 23.3 Mb in size, with a karyotype of 14 chromosomes. The G+C content is approximately 19%. The P. falciparum genome is undergoing re-annotation. This process started in October 2007 with a weeklong workshop co-organized by staff from the Wellcome Trust Sanger Intistute and the EuPathDB team. Ongoing curation and sequence checking is being carried out by the Pathogen Genomics group. Plasmodium falciparum is the most deadly of the five Plasmodium species that cause human malaria. Malaria has a massive impact on human health; it is the worlds second biggest killer after tuberculosis. Around 300 million clinical cases occur each year resulting in between 1.5 - 2.7 million deaths annually, the majority in sub-saharan Africa. It is estimated that 3,000 children under the age of five years fall victim to malaria each day. Around 40% of the worlds population are at risk. In collaboration with EuPathDB, genomic sequence data and annotations are regularly deposited on PlasmoDB where they can be integrated with other datasets and queried using customized queries.

Proper citation: GeneDB Pfalciparum (RRID:SCR_006567) Copy   

•   Source: SciCrunch Registry

http://www.wwpdb.org/

Public global Protein Data Bank archive of macromolecular structural data overseen by organizations that act as deposition, data processing and distribution centers for PDB data. Members are: RCSB PDB (USA), PDBe (Europe) and PDBj (Japan), and BMRB (USA). This site provides information about services provided by individual member organizations and about projects undertaken by wwPDB. Data available via websites of its member organizations.

Proper citation: Worldwide Protein Data Bank (wwPDB) (RRID:SCR_006555) Copy   

•   Source: SciCrunch Registry

http://www.cancerrxgene.org/

A genomics database project is an academic research program to identify molecular features of cancers that predict response to anti-cancer drugs.

Proper citation: Genomics of Drug Sensitivity in Cancer (RRID:SCR_011956) Copy   

•   Source: SciCrunch Registry

http://old.genedb.org/genedb/pombe/index.jsp

THIS RESOURCE IS NO LONGER IN SERVICE documented June 6, 2013 Database of all S. pombe (fission yeast) known and predicted protein coding genes, pseudogenes, transposons, tRNAs, rRNAs, snRNAs, snoRNAs and other known and predicted non-coding RNAs. Curation of new and existing literature is ongoing and changes are incorporated weekly. User feedback is welcome. The genome of fission yeast (Schizosaccharomyces pombe), which contains the smallest number of protein-coding genes yet recorded for a eukaryote: 4,824, has been sequenced and annotated. The centromeres are between 35 and 110 kilobases (kb) and contain related repeats including a highly conserved 1.8-kb element. Regions upstream of genes are longer than in budding yeast (Saccharomyces cerevisiae), possibly reflecting more-extended control regions. Some 43% of the genes contain introns, of which there are 4,730. Fifty genes have significant similarity with human disease genes; half of these are cancer related. We identify highly conserved genes important for eukaryotic cell organization including those required for the cytoskeleton, compartmentation, cell-cycle control, proteolysis, protein phosphorylation and RNA splicing. These genes may have originated with the appearance of eukaryotic life. Few similarly conserved genes that are important for multicellular organization were identified, suggesting that the transition from prokaryotes to eukaryotes required more new genes than did the transition from unicellular to multicellular organization.

Proper citation: GeneDB Spombe (RRID:SCR_010639) Copy   

•   Source: SciCrunch Registry

http://www.sherpa.ac.uk/romeo/index.php?la=en&fIDnum=/&mode=simple

A database which houses publisher policies regarding the self- archiving of journal articles on the web and in Open Access repositories. RoMEO contains publishers' general policies on self-archiving of journal articles and certain conference series. Each entry provides a summary of the publisher's policy, including what version of an article can be deposited, where it can be deposited, and any conditions that are attached to that deposit.

Proper citation: SHERPA RoMEO (RRID:SCR_013815) Copy   

•   Source: SciCrunch Registry

https://vertebrate.genenames.org/

Software resource for vertebrate gene nomenclature. Database of gene symbols. Coordinates with vertebrate nomenclature committees, MGNC (mouse), RGNC (rat), CGNC (chicken), AGNC (Anole green lizard), XNC (Xenopus frog) and ZNC (zebrafish), to ensure genes are named in line with their human homologs.

Proper citation: VGNC (RRID:SCR_017514) Copy   

•   Source: SciCrunch Registry

http://www.alspac.bris.ac.uk

A long-term health research project which follows pregnant women and their offspring in a continuous health and developmental study. More than 14,000 mothers enrolled during pregnancy in 1991 and 1992, and the health and development of their children has been followed in great detail. The ALSPAC families have provided a vast amount of genetic and environmental information over the years which can be made available to researchers globally.

Proper citation: ALSPAC (RRID:SCR_007260) Copy   

•   Source: SciCrunch Registry

http://www.ucl.ac.uk/ploras#annotations:QXuC2C7REeaxtw-aEPo07Q

A research project investigating the difficulties of recovering language after stroke (aphasia). The overall aim of the study is to give future stroke survivors accurate predictions of their aphasia recovery by creating clinical tools and discerning why some patients recover from aphasia better than others.

Proper citation: Predicting Language Outcome and Recovery After Stroke (PLORAS) (RRID:SCR_014498) Copy   

•   Source: SciCrunch Registry

https://www.sanger.ac.uk/science/tools/reapr

Software tool to identify errors in genome assemblies without need for reference sequence. Can be used in any stage of assembly pipeline to automatically break incorrect scaffolds and flag other errors in assembly for manual inspection. Reports mis-assemblies and other warnings, and produces new broken assembly based on error calls.

Proper citation: Recognition of Errors in Assemblies using Paired Reads (RRID:SCR_017625) Copy   

•   Source: SciCrunch Registry

http://hmri.info

Software toolbox for quantitative MRI in neuroscience and clinical research. Open source and flexible tool for qMRI data handling and processing. Allows estimation of high quality multi parameter qMRI maps followed by spatial registration in common space for statistical analysis.

Proper citation: hMRI-toolbox (RRID:SCR_017682) Copy   

•   Source: SciCrunch Registry

http://www.opensourcebrain.org

A resource for sharing and collaboratively developing computational models of neural systems. While models can be submitted and developed in any format, the use of open standards such as NeuroML and PyNN is encouraged, to ensure transparency, modularity, accessibility and cross simulator portability. OSB will provide advanced facilities to analyze, visualize and transform models in these formats, and to connect researchers interested in models of specific neurons, brain regions and disease states. Research themes include: Basal ganglia modelling, Cerebellar Granule cell modelling, Cerebellar modelling, Hippocampal modelling, Neocortical modelling, Whole brain models. Additional themes are welcome.

Proper citation: Open Source Brain (RRID:SCR_001393) Copy   

•   Source: SciCrunch Registry