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http://www.biomedbridges.eu/

Consortium of 12 Biomedical sciences research infrastructure (BMS RI) partners to develop a shared e-infrastructure to allow interoperability between data and services in the biological, medical, translational and clinical domains (providing a complex knowledge environment comprising standards, ontologies, data and services) and thus strengthen biomedical resources in Europe. The BMS RIs are on the roadmap of the European Strategy Forum on Research Infrastructures (ESFRI). Connecting several European research infrastructures brings a diversity of ethical, legal and security concerns including data security requirements for participating e-Infrastructures that are storing or processing patient-related data (or biosamples): EATRIS, ECRIN, BBMRI, EuroBioImaging and EMBL-EBI. In addition, INSTRUCT is interested in secure sample transport and in intellectual property rights; Infrafrontier stores high-throughput data from mice. BBMRI with its focus on the availability of biomaterials is currently emphasizing aspects like k-anonymity and metadata management for its data. Sharing of imaging data by Euro-BioImaging poses challenges with respect to anonymisation and intellectual property. Therefore, an ethical, regulatory and security framework for international data sharing that covers these diverse areas and different types of data (e.g. clinical trials data, mouse data, and human genotype and DNA sequence data) is of crucial importance. The outcomes will lead to real and sustained improvement in the services the biomedical sciences research infrastructures offer to the research community. Data curation and sample description will be improved by the adoption of best practices and agreed standards. Many improvements will emerge from new interactions between RIs created by data linkage and networking. Ensuring access to relevant information for all life science researchers across all BMS RIs will enable scientists to conduct and share cutting-edge research.

Proper citation: BioMedBridges (RRID:SCR_006179) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/devel/bioc/html/deepSNV.html

Software package that provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs (single nucleotide variants).

Proper citation: deepSNV (RRID:SCR_006214) Copy   

•   Source: SciCrunch Registry

http://mango.adult-neurogenesis.de

Database of genes concerning adult neurogenesis mapped to cell types and processes that have been curated from the literature. In its present state, the database is restricted to neurogenesis in the hippocampus.

Proper citation: Mammalian Adult Neurogenesis Gene Ontology (RRID:SCR_006176) Copy   

•   Source: SciCrunch Registry

http://service.iris.edu/fdsnws/station/1/

Web service that returns International Federation of Digital Seismograph Networks (FDSN) station metadata in FDSN StationXML format or as delimited text. Results are available at multiple levels of granularity: network, station, channel and response. Metadata may be selected based on channel descriptors, time ranges, geographic regions, and more. This service is an implementation of the FDSN web service specification version 1.

Proper citation: IRIS DMC FDSNWS station Web Service (RRID:SCR_006177) Copy   

•   Source: SciCrunch Registry

http://www.ddduk.org/

The Deciphering Developmental Disorders (DDD) study aims to find out if using new genetic technologies can help doctors understand why patients get developmental disorders. To do this we have brought together doctors in the 23 NHS Regional Genetics Services throughout the UK and scientists at the Wellcome Trust Sanger Institute, a charitably funded research institute which played a world-leading role in sequencing (reading) the human genome. The DDD study involves experts in clinical, molecular and statistical genetics, as well as ethics and social science. It has a Scientific Advisory Board consisting of scientists, doctors, a lawyer and patient representative, and has received National ethical approval in the UK. Over the next few years, we are aiming to collect DNA and clinical information from 12,000 undiagnosed children in the UK with developmental disorders and their parents. The results of the DDD study will provide a unique, online catalogue of genetic changes linked to clinical features that will enable clinicians to diagnose developmental disorders. Furthermore, the study will enable the design of more efficient and cheaper diagnostic assays for relevant genetic testing to be offered to all such patients in the UK and so transform clinical practice for children with developmental disorders. Over time, the work will also improve understanding of how genetic changes cause developmental disorders and why the severity of the disease varies in individuals. The Sanger Institute will contribute to the DDD study by performing genetic analysis of DNA samples from patients with developmental disorders, and their parents, recruited into the study through the Regional Genetics Services. Using microarray technology and the latest DNA sequencing methods, research teams will probe genetic information to identify mutations (DNA errors or rearrangements) and establish if these mutations play a role in the developmental disorders observed in patients. The DDD initiative grew out of the groundbreaking DECIPHER database, a global partnership of clinical genetics centres set up in 2004, which allows researchers and clinicians to share clinical and genomic data from patients worldwide. The DDD study aims to transform the power of DECIPHER as a diagnostic tool for use by clinicians. As well as improving patient care, the DDD team will empower researchers in the field by making the data generated securely available to other research teams around the world. By assembling a solid resource of high-quality, high-resolution and consistent genomic data, the leaders of the DDD study hope to extend the reach of DECIPHER across a broader spectrum of disorders than is currently possible.

Proper citation: Deciphering Developmental Disorders (RRID:SCR_006171) Copy   

•   Source: SciCrunch Registry

http://www.crowdlabs.org/

A social visualization repository for the scientific workflow management system VisTrails providing a platform for sharing and executing computational tasks. It adopts the model used by social Web sites and that integrates a set of usable tools and a scalable infrastructure to provide an environment for scientists to collaboratively analyze and visualize data. crowdLabs aims to foster collaboration but was specifically designed to support the needs of computational scientists, including the ability to access high-performance computers and manipulate large volumes of data. By providing mechanisms that simplify the publishing and use of analysis pipelines, it allows IT personnel and end users to collaboratively construct and refine portals. This lowers the barriers for the use of scientific analyses and enables broader audiences to contribute insights to the scientific exploration process, without the high costs incurred by traditional portals. In addition, it supports a more dynamic environment where new exploratory analyses can be added on-the-fly.

Proper citation: crowdLabs (RRID:SCR_006294) Copy   

•   Source: SciCrunch Registry

http://vizhub.wustl.edu

A visualization hub displaying sequencing data from the Roadmap Epigenomics project. It hosts high volume of tracks from ENCODE and Roadmap Epigenomics projects, supports multiple organisms, visualizes chromatin-interaction data (e.g. Hi-C), performs gene set view, gene plot, and many others. All delivered on the web at high performance.

Proper citation: VizHub (RRID:SCR_006209) Copy   

•   Source: SciCrunch Registry

http://evolution.genetics.washington.edu/phylip.html

A free package of software programs for inferring phylogenies (evolutionary trees). The source code is distributed (in C), and executables are also distributed. In particular, already-compiled executables are available for Windows (95/98/NT/2000/me/xp/Vista), Mac OS X, and Linux systems. Older executables are also available for Mac OS 8 or 9 systems.

Proper citation: PHYLIP (RRID:SCR_006244) Copy   

•   Source: SciCrunch Registry

http://www.semantic-mediawiki.org/wiki/Semantic_MediaWiki

A free, open-source extension to MediaWiki - the wiki software that powers Wikipedia - that helps to search, organize, tag, browse, evaluate, and share the wiki''s content. While traditional wikis contain only text which computers can neither understand nor evaluate, SMW adds semantic annotations that allow a wiki to function as a collaborative database. Semantic MediaWiki introduces some additional markup into the wiki-text which allows users to add semantic annotations to the wiki. While this first appears to make things more complex, it can also greatly simplify the structure of the wiki, help users to find more information in less time, and improve the overall quality and consistency of the wiki. A large number of related extensions have been created that extend the ability to edit, display and browse through the data stored by SMW: the term Semantic MediaWiki is sometimes used to refer to this entire family of extensions.

Proper citation: Semantic MediaWiki (RRID:SCR_006246) Copy   

•   Source: SciCrunch Registry

http://www.birncommunity.org/tools-catalog/human-imaging-database-hid/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented October 5, 2017.

Database management system developed to handle the increasingly large and diverse datasets collected as part of the MBIRN and FBIRN collaboratories and throughout clinical imaging communities at large. The HID can be extended to contain relevant information concerning experimental subjects, assessments of subjects, the experimental data collected, the experimental protocols, and other metadata normally included with experiments.

Proper citation: Human Imaging Database (RRID:SCR_006126) Copy   

•   Source: SciCrunch Registry

https://github.com/RGLab/pepStat

Software for statistical analysis of peptide microarrays. It uses an integrated analytical method for analyzing peptide microarray antibody binding data, from normalization through subject-specific positivity calls and data integration and visualization.

Proper citation: pepStat (RRID:SCR_006240) Copy   

•   Source: SciCrunch Registry

http://stormo.wustl.edu/ScerTF

Catalog of over 1,200 position weight matrices (PWMs) for 196 different yeast transcription factors (TFs). They've curated 11 literature sources, benchmarked the published position-specific scoring matrices against in-vivo TF occupancy data and TF deletion experiments, and combined the most accurate models to produce a single collection of the best performing weight matrices for Saccharomyces cerevisiae. ScerTF is useful for a wide range of problems, such as linking regulatory sites with transcription factors, identifying a transcription factor based on a user-input matrix, finding the genes bound/regulated by a particular TF, and finding regulatory interactions between transcription factors. Enter a TF name to find the recommended matrix for a particular TF, or enter a nucleotide sequence to identify all TFs that could bind a particular region.

Proper citation: ScerTF (RRID:SCR_006121) Copy   

•   Source: SciCrunch Registry

http://www-bionet.sscc.ru/sitex/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 19,2019. Analyzing protein structure projection on exon-intron structure of corresponding gene through years led to several fundamental conclusions about structural and functional organization of the protein. According to these results we decided to map the protein functional sites. So we created the database SitEx that keep the information about this mapping and included the BLAST search and 3D similar structure search using PDB3DScan for the polypeptide encoded by one exon, participating in organizing the functional site. This will help: # to study the positions of the functional sites in exon structure; # to make the complex analysis of the protein function; # to exposure the exons that took part in exon shuffling and came from bacterial genomes; # to study the peculiarities of coding the polypeptide structures. Currently, SitEx contains information about 9994 functional sites presented in 2021 proteins described in proteomes of 17 organisms.

Proper citation: SitEx (RRID:SCR_006122) Copy   

•   Source: SciCrunch Registry

http://idp1.force.cs.is.nagoya-u.ac.jp/pscdb/

Database for protein structural change upon ligand binding that are classified into 7 classes in terms of the ligand binding sites and the location where the dominant motion occurs. # Coupled Domain motions are the domain motions induced upon ligand binding. # Independent Domain motions are the observable domain motions regardless of ligand binding. # Coupled Local motions are the local motions induced upon ligand binding. # Independent Local motions are the observable local motions regardless of ligand binding. # Burying ligand motions are imaginable motions required to hold ligand protein-inside. # No significant motions mean just nothing happen. # Other motions are motions unclassified into domain and local motions. Proteins are flexible molecules that undergo structural changes to function. The Protein Data Bank contains multiple entries for identical proteins determined under different conditions, e.g. with and without a ligand molecule, which provides important information for understanding the structural changes related to protein functions. We gathered 839 protein structural pairs of ligand-free and ligand-bound states from monomeric or homo-dimeric proteins, and constructed the Protein Structural Change DataBase (PSCDB). In the database, we focused on whether the motions were coupled with ligand binding. As a result, the protein structural changes were classified into seven classes, i.e. coupled domain motion (59 structural changes), independent domain motion (70), coupled local motion (125), independent local motion (135), burying ligand motion (104), no significant motion (311) and other type motion (35). PSCDB provides lists of each class. On each entry page, users can view detailed information about the motion, accompanied by a morphing animation of the structural changes.

Proper citation: PSCDB - Protein Structural Change DataBase (RRID:SCR_006116) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/RETO

An application ontology for the domain of gene transcription regulation. The ontology integrates fragments of GO and MI with data from GOA, IntAct, UniProt, NCBI, KEGG and orthology relations.

Proper citation: Regulation of Transcription Ontology (RRID:SCR_006238) Copy   

•   Source: SciCrunch Registry

https://proteomecommons.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A public resource for sharing general proteomics information including data (Tranche repository), tools, and news. Joining or creating a group/project provides tools and standards for collaboration, project management, data annotation, permissions, permanent storage, and publication.

Proper citation: Proteome Commons (RRID:SCR_006234) Copy   

•   Source: SciCrunch Registry

http://central.xnat.org

Online repository of open access images including MR Sessions, MRI, Freesurfer APARC, Freesurfer ASEGs, Clinical Assessments, Atlas Scaling Factors, and Fast Segmentations data. CENTRAL currently contains 374 Projects, 3808 Subjects, and 5174 Imaging Sessions (June 2014). Central is powered by XNAT (The Extensible Neuroimaging Archive Toolkit), an open source software platform designed to facilitate management and exploration of neuroimaging and related data. XNAT includes a secure database backend and a rich web-based user interface.

Proper citation: XNAT Central (RRID:SCR_006235) Copy   

•   Source: SciCrunch Registry

https://compbio.dfci.harvard.edu/predictivenetworks//

A flexible, open-source, web-based application and data services framework that enables the integration, navigation, visualization and analysis of gene interaction networks. The primary goal of PN is to allow biomedical researchers to evaluate experimentally derived gene lists in the context of large-scale gene interaction networks. The PN analytical pipeline involves two key steps. The first is the collection of a comprehensive set of known gene interactions derived from a variety of publicly available sources. The second is to use these ''known'' interactions together with gene expression data to infer robust gene networks. The regression-based network inference algorithm creates a graph of gene interactions in which cycles may be present (but no self-loops). Based on information-theoretic techniques, a causal gene interaction network is inferred from both prior knowledge (interactions extracted from biomedical literature and structured biological databases) and gene expression data. A prediction model is fitted for each gene, given its parents, enabling assessment of the predictive ability of the network model.

Proper citation: Predictive Networks (RRID:SCR_006110) Copy   

•   Source: SciCrunch Registry

http://pathogenseq.lshtm.ac.uk/estmoi

A per-based software to estimate multiplicity of infection (MOI) in parasite genomic sequence data. It is primarily developed to address the limitations of current laboratory (PCR) based estimates of multiplicity using high throughput sequence data. It requires a BAM (alignment output of short reads to the reference genome), VCF (a file with information on variant calls) and FASTA (reference genome) files. # Short reads are aligned to a reference genome using BWA, BOWTIE, SMALT or other short read aligners to generate a BAM file. # Single Nucleotide Polymorphisms (SNPs) are then identified using SAMTools/BCFtools and stored in the VCF format. # The reference FASTA file is expected to be indexed using ''samtools faidx'' to generate a *.fai file. estMOI generates files containing MOI estimates for each SNP combinations (file with name *.log) and a summary for all chromosomes (file with name *.txt).

Proper citation: estMOI (RRID:SCR_006192) Copy   

•   Source: SciCrunch Registry

https://pb.apf.edu.au/phenbank/homePage.html

The NHMRC Australian PhenomeBank (APB) is a non-profit repository of mouse strains used in Medical Research. The database allows you to search for murine strains, housed or archived in Australia, carrying mutations in particular genes, strains with transgenic alterations and for mice with particular phenotypes. 1876 publicly available strains, 922 genes, 439 transgenes The APB has two roles: Provide and maintain a central database of genetically modified mice held in Australia either live or as cryopreserved material; Establish and maintain a mouse strain archive. Strains are archived as cryopreserved sperm or embryos.

Proper citation: NHMRC Australian PhenomeBank (RRID:SCR_006149) Copy   

•   Source: SciCrunch Registry