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http://www.imexconsortium.org/

Interaction database from international collaboration between major public interaction data providers who share curation effort and develop set of curation rules when capturing data from both directly deposited interaction data or from publications in peer reviewed journals. Performs complete curation of all protein-protein interactions experimentally demonstrated within publication and makes them available in single search interface on common website. Provides data in standards compliant download formats. IMEx partners produce their own separate resources, which range from all encompassing molecular interaction databases, such as are maintained by IntAct, MINT and DIP, organism-centric resources such as BioGrid or MPIDB or biological domain centric, such as MatrixDB. They have committed to making records available, via PSICQUIC webservice, which have been curated to IMEx rules and are available to users as single, non-redundant set of curated publications which can be searched at the IMEx website. Data is made available in standards-compliant tab-deliminated and XML formats, enabling to visualize data using wide range of tools. Consortium is open to participation of additional partners and encourages deposition of data, prior to publication, and will supply unique accession numbers which may be referenced within final article. Submitters may send their data directly to any of member databases using variety of formats, but should conform to guidelines as to minimum information required to describe data.

Proper citation: IMEx - The International Molecular Exchange Consortium (RRID:SCR_002805) Copy   

•   Source: SciCrunch Registry

http://www.greenphyl.org/

A database designed for plant comparative and functional genomics based on complete genomes. It comprises complete proteome sequences from the major phylum of plant evolution. The clustering of these proteomes was performed to define a consistent and extensive set of homeomorphic plant families. Based on this, lists of gene families such as plant or species specific families and several tools are provided to facilitate comparative genomics within plant genomes. The analyses follow two main steps: gene family clustering and phylogenomic analysis of the generated families. Once a group of sequences (cluster) is validated, phylogenetic analyses are performed to predict homolog relationships such as orthologs and ultraparalogs.

Proper citation: GreenPhylDB (RRID:SCR_002834) Copy   

•   Source: SciCrunch Registry

http://andestools.sourceforge.net/

Software library and a suite of applications, written in Perl and R, for deep sequencing statistical analyses.

Proper citation: ANDES (RRID:SCR_002791) Copy   

•   Source: SciCrunch Registry

http://cgap.nci.nih.gov/

Project to determine the gene expression profiles of normal, precancer, and cancer cells, whose generated resources are available to the cancer community. Interconnected modules provide access to all CGAP data, bioinformatic analysis tools, and biological resources allowing the user to find in silico answers to biological questions in a fraction of the time it once took in the laboratory. * Genes * Tissues * Pathways * RNAi * Chromosomes * SAGE Genie * Tools

Proper citation: Cancer Genome Anatomy Project (RRID:SCR_003072) Copy   

•   Source: SciCrunch Registry

http://www.ichip.de/software/SplicingCompass.html

Software for detection of differential splicing between two different conditions using RNA-Seq data.

Proper citation: SplicingCompass (RRID:SCR_003249) Copy   

•   Source: SciCrunch Registry

http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi

A web interface to the Primer3 primer design program as an enhanced alternative for the CGI- scripts that come with Primer3.

Proper citation: Primer3Plus (RRID:SCR_003081) Copy   

•   Source: SciCrunch Registry

http://phenome.jax.org/

Database enables integration of genomic and phenomic data by providing access to primary experimental data, data collection protocols and analysis tools. Data represent behavioral, morphological and physiological disease-related characteristics in naive mice and those exposed to drugs, environmental agents or other treatments. Collaborative standardized collection of measured data on laboratory mouse strains to characterize them in order to facilitate translational discoveries and to assist in selection of strains for experimental studies. Includes baseline phenotype data sets as well as studies of drug, diet, disease and aging effect., protocols, projects and publications, and SNP, variation and gene expression studies. Provides tools for online analysis. Data sets are voluntarily contributed by researchers from variety of institutions and settings, or retrieved by MPD staff from open public sources. MPD has three major types of strain-centric data sets: phenotype strain surveys, SNP and variation data, and gene expression strain surveys. MPD collects data on classical inbred strains as well as any fixed-genotype strains and derivatives that are openly acquirable by the research community. New panels include Collaborative Cross (CC) lines and Diversity Outbred (DO) populations. Phenotype data include measurements of behavior, hematology, bone mineral density, cholesterol levels, endocrine function, aging processes, addiction, neurosensory functions, and other biomedically relevant areas. Genotype data are primarily in the form of single-nucleotide polymorphisms (SNPs). MPD curates data into a common framework by standardizing mouse strain nomenclature, standardizing units (SI where feasible), evaluating data (completeness, statistical power, quality), categorizing phenotype data and linking to ontologies, conforming to internal style guides for titles, tags, and descriptions, and creating comprehensive protocol documentation including environmental parameters of the test animals. These elements are critical for experimental reproducibility.

Proper citation: Mouse Phenome Database (MPD) (RRID:SCR_003212) Copy   

•   Source: SciCrunch Registry

http://dip.doe-mbi.ucla.edu/

Database to catalog experimentally determined interactions between proteins combining information from a variety of sources to create a single, consistent set of protein-protein interactions that can be downloaded in a variety of formats. The data were curated, both, manually and also automatically using computational approaches that utilize the the knowledge about the protein-protein interaction networks extracted from the most reliable, core subset of the DIP data. Because the reliability of experimental evidence varies widely, methods of quality assessment have been developed and utilized to identify the most reliable subset of the interactions. This CORE set can be used as a reference when evaluating the reliability of high-throughput protein-protein interaction data sets, for development of prediction methods, as well as in the studies of the properties of protein interaction networks. Tools are available to analyze, visualize and integrate user's own experimental data with the information about protein-protein interactions available in the DIP database. The DIP database lists protein pairs that are known to interact with each other. By interact they mean that two amino acid chains were experimentally identified to bind to each other. The database lists such pairs to aid those studying a particular protein-protein interaction but also those investigating entire regulatory and signaling pathways as well as those studying the organization and complexity of the protein interaction network at the cellular level. Registration is required to gain access to most of the DIP features. Registration is free to the members of the academic community. Trial accounts for the commercial users are also available.

Proper citation: Database of Interacting Proteins (DIP) (RRID:SCR_003167) Copy   

•   Source: SciCrunch Registry

http://www.sysbio.se/piano/

Software R-package for running gene set analysis using various statistical methods, from different gene level statistics and a wide range of gene-set collections. The Piano package contains functions for combining the results of multiple runs of gene set analyses.

Proper citation: Piano (RRID:SCR_003200) Copy   

•   Source: SciCrunch Registry

http://cmb.molgen.mpg.de/2ndGenerationSequencing/Solas/

Software package for the statistical language R, devoted to the analysis of next generation short read data of RNA-seq transcripts. It provides predictions of alternative exons in a single condition/cell sample, predictions of differential alternative exons between two conditions/cell samples, and quantification of alternative splice forms in a single condition/cell sample.

Proper citation: Solas (RRID:SCR_003168) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/cancer/software/genepattern

A powerful genomic analysis platform that provides access to hundreds of tools for gene expression analysis, proteomics, SNP analysis, flow cytometry, RNA-seq analysis, and common data processing tasks. A web-based interface provides easy access to these tools and allows the creation of multi-step analysis pipelines that enable reproducible in silico research.

Proper citation: GenePattern (RRID:SCR_003201) Copy   

•   Source: SciCrunch Registry

http://bibiserv.techfak.uni-bielefeld.de/dialign/

Tool for multiple sequence alignment using various sources of external information that is particularly useful to detect local homologies in sequences with low overall similarity. While standard alignment methods rely on comparing single residues and imposing gap penalties, DIALIGN constructs pairwise and multiple alignments by comparing entire segments of the sequences. No gap penalty is used. This approach can be used for both global and local alignment, but it is particularly successful in situations where sequences share only local homologies. Several versions of DIALIGN are available online at GOBICS, http://dialign.gobics.de/

Proper citation: DIALIGN (RRID:SCR_003041) Copy   

•   Source: SciCrunch Registry

http://pir.georgetown.edu/pirwww/dbinfo/pirsf.shtml

A SuperFamily classification system, with rules for functional site and protein name, to facilitate the sensible propagation and standardization of protein annotation and the systematic detection of annotation errors. The PIRSF concept is being used as a guiding principle to provide comprehensive and non-overlapping clustering of UniProtKB sequences into a hierarchical order to reflect their evolutionary relationships. The PIRSF classification system is based on whole proteins rather than on the component domains; therefore, it allows annotation of generic biochemical and specific biological functions, as well as classification of proteins without well-defined domains. There are different PIRSF classification levels. The primary level is the homeomorphic family, whose members are both homologous (evolved from a common ancestor) and homeomorphic (sharing full-length sequence similarity and a common domain architecture). At a lower level are the subfamilies which are clusters representing functional specialization and/or domain architecture variation within the family. Above the homeomorphic level there may be parent superfamilies that connect distantly related families and orphan proteins based on common domains. Because proteins can belong to more than one domain superfamily, the PIRSF structure is formally a network. The FTP site provides free download for PIRSF.

Proper citation: PIRSF (RRID:SCR_003352) Copy   

•   Source: SciCrunch Registry

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

Centralized, standards compliant, public data repository for proteomics data, including protein and peptide identifications, post-translational modifications and supporting spectral evidence. Originally it was developed to provide a common data exchange format and repository to support proteomics literature publications. This remit has grown with PRIDE, with the hope that PRIDE will provide a reference set of tissue-based identifications for use by the community. The future development of PRIDE has become closely linked to HUPO PSI. PRIDE encourages and welcomes direct user submissions of protein and peptide identification data to be published in peer-reviewed publications. Users may Browse public datasets, use PRIDE BioMart for custom queries, or download the data directly from the FTP site. PRIDE has been developed through a collaboration of the EMBL-EBI, Ghent University in Belgium, and the University of Manchester.

Proper citation: Proteomics Identifications (PRIDE) (RRID:SCR_003411) Copy   

•   Source: SciCrunch Registry

https://bitbucket.org/dranew/defuse

Software package for gene fusion discovery using RNA-Seq data. It uses clusters of discordant paired end alignments to inform a split read alignment analysis for finding fusion boundaries.

Proper citation: deFuse (RRID:SCR_003279) Copy   

•   Source: SciCrunch Registry

http://www.cellimagelibrary.org/

Freely accessible, public repository of vetted and annotated microscopic images, videos, and animations of cells from a variety of organisms, showcasing cell architecture, intracellular functionalities, and both normal and abnormal processes. Explore by Cell Process, Cell Component, Cell Type or Organism. The Cell includes images acquired from historical and modern collections, publications, and by recruitment.

Proper citation: Cell Image Library (CIL) (RRID:SCR_003510) Copy   

•   Source: SciCrunch Registry

http://www.reactome.org

Collection of pathways and pathway annotations. The core unit of the Reactome data model is the reaction. Entities (nucleic acids, proteins, complexes and small molecules) participating in reactions form a network of biological interactions and are grouped into pathways (signaling, innate and acquired immune function, transcriptional regulation, translation, apoptosis and classical intermediary metabolism) . Provides website to navigate pathway knowledge and a suite of data analysis tools to support the pathway-based analysis of complex experimental and computational data sets.

Proper citation: Reactome (RRID:SCR_003485) Copy   

•   Source: SciCrunch Registry

http://www.etriks.org/

Research informatics and analytics platform for the IMI OncoTrack consortium.

Proper citation: eTRIKS (RRID:SCR_003765) Copy   

•   Source: SciCrunch Registry

http://cng.gmu.edu:8080/Lm

A freely available software tool available for the Windows and Linux platform, as well as the Online version Applet, for the analysis, comparison and search of digital reconstructions of neuronal morphologies. For the quantitative characterization of neuronal morphology, LM computes a large number of neuroanatomical parameters from 3D digital reconstruction files starting from and combining a set of core metrics. After more than six years of development and use in the neuroscience community, LM enables the execution of commonly adopted analyses as well as of more advanced functions, including: (i) extraction of basic morphological parameters, (ii) computation of frequency distributions, (iii) measurements from user-specified subregions of the neuronal arbors, (iv) statistical comparison between two groups of cells and (v) filtered selections and searches from collections of neurons based on any Boolean combination of the available morphometric measures. These functionalities are easily accessed and deployed through a user-friendly graphical interface and typically execute within few minutes on a set of 20 neurons. The tool is available for either online use on any Java-enabled browser and platform or may be downloaded for local execution under Windows and Linux.

Proper citation: L-Measure (RRID:SCR_003487) Copy   

•   Source: SciCrunch Registry

http://microbesonline.org/

MicrobesOnline is designed specifically to facilitate comparative studies on prokaryotic genomes. It is an entry point for operon, regulons, cis-regulatory and network predictions based on comparative analysis of genomes. The portal includes over 1000 complete genomes of bacteria, archaea and fungi and thousands of expression microarrays from diverse organisms ranging from model organisms such as Escherichia coli and Saccharomyces cerevisiae to environmental microbes such as Desulfovibrio vulgaris and Shewanella oneidensis. To assist in annotating genes and in reconstructing their evolutionary history, MicrobesOnline includes a comparative genome browser based on phylogenetic trees for every gene family as well as a species tree. To identify co-regulated genes, MicrobesOnline can search for genes based on their expression profile, and provides tools for identifying regulatory motifs and seeing if they are conserved. MicrobesOnline also includes fast phylogenetic profile searches, comparative views of metabolic pathways, operon predictions, a workbench for sequence analysis and integration with RegTransBase and other microbial genome resources. The next update of MicrobesOnline will contain significant new functionality, including comparative analysis of metagenomic sequence data. Programmatic access to the database, along with source code and documentation, is available at http://microbesonline.org/programmers.html.

Proper citation: MicrobesOnline (RRID:SCR_005507) Copy   

•   Source: SciCrunch Registry