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http://bejerano.stanford.edu/prism/public/html/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: PRISM (Stanford database) (RRID:SCR_005375) Copy   

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http://sourceforge.net/projects/molbiolib/

A compact, portable, and extensively tested C++11 software framework and set of applications tailored to the demands of next-generation sequencing data and applicable to many other applications. It is designed to work with common file formats and data types used both in genomic analysis and general data analysis. A central relational-database-like Table class is a flexible and powerful object to intuitively represent and work with a wide variety of tabular datasets, ranging from alignment data to annotations. MolBioLib includes programs to perform a wide variety of analysis tasks such as computing read coverage, annotating genomic intervals, and novel peak calling with a wavelet algorithm. This package assumes fluency in both UNIX and C++.

Proper citation: MolBioLib (RRID:SCR_005372) Copy   

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http://statgenpro.psychiatry.hku.hk/limx/kggseq/

A biological Knowledge-based mining platform for Genomic and Genetic studies using Sequence data. The software platform, constituted of bioinformatics and statistical genetics functions, makes use of valuable biologic resources and knowledge for sequencing-based genetic mapping of variants / genes responsible for human diseases / traits. It facilitates geneticists to fish for the genetic determinants of human diseases / traits in the big sea of DNA sequences. KGGSeq has paid attention to downstream analysis of genetic mapping. The framework was implemented to filter and prioritize genetic variants from whole exome sequencing data.

Proper citation: KGGSeq (RRID:SCR_005311) Copy   

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http://code.google.com/p/hydra-sv/

Software that detects structural variation (SV) breakpoints by clustering discordant paired-end alignments whose signatures corroborate the same putative breakpoint. Hydra can detect breakpoints caused by all classes of structural variation. Moreover, it was designed to detect variation in both unique and duplicated genomic regions; therefore, it will examine paired-end reads having multiple discordant alignments. Hydra does not attempt to classify SV breakpoints based on the mapping distances and orientations of each breakpoint cluster, it merely detects and reports breakpoints. This is an intentional decision, as it was observed that in loci affected by complex rearrangements, the type of variant suggested by the breakpoint signature is not always correct. Hydra does report the orientations, distances, number of supporting read-pairs, etc., for each breakpoint. It is suggested that downstream methods be used to classify variants based on the genomic features that they overlap and the co-occurrence of other breakpoints. For example, they developed BEDTools for exactly this purpose and the breakpoints reported by Hydra are in the BEDPE format used by BEDTools. Future releases of Hydra will include scripts that assist in the classification process.

Proper citation: Hydra (RRID:SCR_005260) Copy   

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http://202.97.205.78/CpG_MPs/

Tool for identification and analysis of CpG methylation patterns of genomic regions from high-throughput bisulfite sequencing data. It may identify the unmethylated and methylated regions for a single sample, the conserved and differential methylation regions with different methylation patterns for paired or multiple samples. It includes four main modules as follows: # Normalization of the sequencing reads of cytosines following guanines; # Identification of the unmethylated (methylated) regions using hotspot extension algorithm; # Identification of conservatively and differentially methylated regionsby combining the combinatorial algorithm for determination of potentially functional regions with the algorithm of analysis of variance (ANOVA) for assess the statistical significance of differentially methylated regions; # Extraction of sequence features and visualization of these potentially functional regions.

Proper citation: CpG MPs (RRID:SCR_005441) Copy   

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http://www.garban.org/garban/home.php

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 12, 2012. GARBAN is a tool for analysis and rapid functional annotation of data arising from cDNA microarrays and proteomics techniques. GARBAN has been implemented with bioinformatic tools to rapidly compare, classify, and graphically represent multiple sets of data (genes/ESTs, or proteins), with the specific aim of facilitating the identification of molecular markers in pathological and pharmacological studies. GARBAN has links to the major genomic and proteomic databases (Ensembl, GeneBank, UniProt Knowledgebase, InterPro, etc.), and follows the criteria of the Gene Ontology Consortium (GO) for ontological classifications. Source may be shared: e-mail garban (at) ceit.es. Platform: Online tool

Proper citation: GARBAN (RRID:SCR_005778) Copy   

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http://corneliu.henegar.info/FunCluster.htm

FunCluster is a genomic data analysis algorithm which performs functional analysis of gene expression data obtained from cDNA microarray experiments. Besides automated functional annotation of gene expression data, FunCluster functional analysis aims to detect co-regulated biological processes through a specially designed clustering procedure involving biological annotations and gene expression data. FunCluster''''s functional analysis relies on Gene Ontology and KEGG annotations and is currently available for three organisms: Homo Sapiens, Mus Musculus and Saccharomyces Cerevisiae. FunCluster is provided as a standalone R package, which can be run on any operating system for which an R environment implementation is available (Windows, Mac OS, various flavors of Linux and Unix). Download it from the FunCluster website, or from the worldwide mirrors of CRAN. FunCluster is provided freely under the GNU General Public License 2.0. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: FunCluster (RRID:SCR_005774) Copy   

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http://great.stanford.edu/public/html/splash.php

Data analysis service that predicts functions of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. The utility of GREAT extends to data generated for transcription-associated factors, open chromatin, localized epigenomic markers and similar functional data sets, and comparative genomics sets. Platform: Online tool

Proper citation: GREAT: Genomic Regions Enrichment of Annotations Tool (RRID:SCR_005807) Copy   

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Ratings or validation data are available for this resource

http://genome.ucsc.edu/

Portal to interactively visualize genomic data. Provides reference sequences and working draft assemblies for collection of genomes and access to ENCODE and Neanderthal projects. Includes collection of vertebrate and model organism assemblies and annotations, along with suite of tools for viewing, analyzing and downloading data.

Proper citation: UCSC Genome Browser (RRID:SCR_005780) Copy   

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http://athina.biol.uoa.gr/bioinformatics/GENEVITO/

A JAVA-based computer application that serves as a workbench for genome-wide analysis through visual interaction. GeneViTo offers an inspectional view of genomic functional elements, concerning data stemming both from database annotation and analysis tools for an overall analysis of existing genomes. The application deals with various experimental information concerning both DNA and protein sequences (derived from public sequence databases or proprietary data sources) and meta-data obtained by various prediction algorithms, classification schemes or user-defined features. Interaction with a Graphical User Interface (GUI) allows easy extraction of genomic and proteomic data referring to the sequence itself, sequence features, or general structural and functional features. Emphasis is laid on the potential comparison between annotation and prediction data in order to offer a supplement to the provided information, especially in cases of poor annotation, or an evaluation of available predictions. Moreover, desired information can be output in high quality JPEG image files for further elaboration and scientific use. GeneViTo has already been applied to visualize the genomes of two microbial organisms: the bacterion Chlamydia trachomatis and the archaeon Methanococcus jannaschii. The application is compatible with Linux or Windows ME-2000-XP operating systems, provided that the appropriate Java Runtime Environment (Java 1.4.1) is already installed in the system.

Proper citation: GeneVito (RRID:SCR_006211) Copy   

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https://www.med.unc.edu/pgc/

Consortium conducting meta-analyses of genome-wide genetic data for psychiatric disease. Focused on autism, attention-deficit hyperactivity disorder, bipolar disorder, major depressive disorder, schizophrenia, anorexia nervosa (AN), Tourette syndrome (TS), and obsessive-compulsive disorder (OCD). Used to investigate common single nucleotide polymorphisms (SNPs) genotyped on commercial arrays, structural variation (copy number variation) and uncommon or rare genetic variation. To participate you are asked to upload data from your study to central computer used by this consortium. Genetic Cluster Computer serves as data warehouse and analytical platform for this study . When data from your study have been incorporated, account will be provided on central server and access to all GWAS genotypes, phenotypes, and meta-analytic results relevant to deposited data and participation aims. NHGRI GWAS Catalog contains updated information about all GWAS in biomedicine, and is usually excellent starting point to find comprehensive list of studies. Files can be obtained by any PGC member for any disease to which they contributed data. These files can also be obtained by application to NIMH Genetics Repository. Individual-level genotype and phenotype data requires application, material transfer agreement, and informed consent consideration. Some datasets are also in controlled-access dbGaP and Wellcome Trust Case-Control Consortium repositories. PGC members can also receive back cleaned and imputed data and results for samples they contributed to PGC analyses.

Proper citation: Psychiatric Genomics Consortium (RRID:SCR_004495) Copy   

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http://www.tbidx.net

Network evaluating consensus-based common data elements (CDE) for traumatic brain injury (TBI) and psychological health (TBI-CDE, www.commondataelements.ninds.nih.gov/TBI.aspx) while extensively phenotyping a cohort of TBI patients across the injury spectrum from concussion to coma. Institutions that participate in the TBI Network will be able to track the outcomes of patients through a 3, 6 and 12-month followup program and compare outcomes with other participating institutions. For the three acute care centers, patients were enrolled that presented to the emergency department within 24 hours of head injury and required computed tomography (CT). For the rehabilitation center, referrals from acute hospitals were enrolled. Patients were consented to participate in components: clinical profile; blood draws for measurement of proteomic and genomic markers; 3T MRI within 2 weeks; three-month Glasgow Outcome Scale-Extended (GOS-E); and six-month TBI-CDE Core outcome assessments. A web-enabled database, imaging repository, and biospecimen bank was developed using the TBI-CDE recommendations. A total of 605 patients were enrolled. Of these subjects, 88% had a GCS 13-15, 5% had a GCS 9-12, and 7% had a GCS of 8 or less. Three-month GOS-E''s were obtained for 78% of the patients. Comprehensive 6-month outcome measures, including PTSD assessment, are ongoing until September 2011. Blood specimens were collected from 450 patients. Initial CTs for 605 patients and 235 patients with 3T MRI studies were transferred to an imaging repository. The TRACK TBI Network will provide qualified institutions access to a web-based version of key forms in tracking TBI outcomes for Quality Improvement and institutional benchmarking.

Proper citation: TRACK TBI Network (RRID:SCR_004723) Copy   

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http://www.jax.org/index.html

An independent, nonprofit organization focused on mammalian genetics research to advance human health. Their mission is to discover the genetic basis for preventing, treating, and curing human disease, and to enable research for the global biomedical community. Jackson Laboratory breeds and manages colonies of mice as resources for other research institutions and laboratories, along with providing software and techniques. Jackson Lab also conducts genetic research and provides educational material for various educational levels.

Proper citation: Jackson Laboratory (RRID:SCR_004633) Copy   

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

Web service for permanent archiving and sharing of all types of personally identifiable genetic and phenotypic data resulting from biomedical research projects. The repository allows you to explore datasets from numerous genotype experiments, supplied by a range of data providers. The EGA''s role is to provide secure access to the data that otherwise could not be distributed to the research community. The EGA contains exclusive data collected from individuals whose consent agreements authorize data release only for specific research use or to bona fide researchers. Strict protocols govern how information is managed, stored and distributed by the EGA project. As an example, only members of the EGA team are allowed to process data in a secure computing facility. Once processed, all data are encrypted for dissemination and the encryption keys are delivered offline. The EGA also supports data access only for the consortium members prior to publication.

Proper citation: European Genome phenome Archive (RRID:SCR_004944) Copy   

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http://glioblastoma.alleninstitute.org/

Platform for exploring the anatomic and genetic basis of glioblastoma at the cellular and molecular levels that includes two interactive databases linked together by de-identified tumor specimen numbers to facilitate comparisons across data modalities: * The open public image database, here, providing in situ hybridization data mapping gene expression across the anatomic structures inherent in glioblastoma, as well as associated histological data suitable for neuropathological examination * A companion database (Ivy GAP Clinical and Genomic Database) offering detailed clinical, genomic, and expression array data sets that are designed to elucidate the pathways involved in glioblastoma development and progression. This database requires registration for access. The hope is that researchers all over the world will mine these data and identify trends, correlations, and interesting leads for further studies with significant translational and clinical outcomes. The Ivy Glioblastoma Atlas Project is a collaborative partnership between the Ben and Catherine Ivy Foundation, the Allen Institute for Brain Science and the Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment.

Proper citation: Ivy Glioblastoma Atlas Project (RRID:SCR_005044) Copy   

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https://github.com/cwhelan/cloudbreak

Software providing a Hadoop-based genomic structural variation (SV) caller for Illumina paired-end DNA sequencing data. It contains a full pipeline for aligning data in the form of FASTQ files using alignment pipelines that generate many possible mappings for every read, in the Hadoop framework. It then contains Hadoop jobs for computing genomic features from the alignments, and for calling insertion and deletion variants from those features.

Proper citation: Cloudbreak (RRID:SCR_005097) Copy   

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http://www.genome.jp/kegg/expression/

Database for mapping gene expression profiles to pathways and genomes. Repository of microarray gene expression profile data for Synechocystis PCC6803 (syn), Bacillus subtilis (bsu), Escherichia coli W3110 (ecj), Anabaena PCC7120 (ana), and other species contributed by the Japanese research community.

Proper citation: Kyoto Encyclopedia of Genes and Genomes Expression Database (RRID:SCR_001120) Copy   

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http://www.well.ox.ac.uk/happy/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Software package for Multipoint QTL Mapping in Genetically Heterogeneous Animals (entry from Genetic Analysis Software) The method is implemented in a C-program and there is now an R version of HAPPY. You can run HAPPY remotely from their web server using your own data (or try it out on the data provided for download).

Proper citation: Happy (RRID:SCR_001395) Copy   

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http://neuronalarchitects.com/ibiofind.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 17, 2016. C#.NET 4.0 WPF / OWL / REST / JSON / SPARQL multi-threaded, parallel desktop application enables the construction of biomedical knowledge through PubMed, ScienceDirect, EndNote and NIH Grant repositories for tracking the work of medical researchers for ranking and recommendations. Users can crawl web sites, build latent semantic indices to generate literature searches for both Clinical Translation Science Award and non-CTSA institutions, examine publications, build Bayesian networks for neural correlates, gene to gene interactions, protein to protein interactions and as well drug treatment hypotheses. Furthermore, one can easily access potential researcher information, monitor and evolve their networks and search for possible collaborators and software tools for creating biomedical informatics products. The application is designed to work with the ModelMaker, R, Neural Maestro, Lucene, EndNote and MindGenius applications to improve the quality and quantity of medical research. iBIOFind interfaces with both eNeoTutor and ModelMaker 2013 Web Services Implementation in .NET for eNeoTutor to aid instructors to build neuroscience courses as well as rare diseases. Added: Rare Disease Explorer: The Visualization of Rare Disease, Gene and Protein Networks application module. Cinematics for the Image Finder from Yale. The ability to automatically generate and update websites for rare diseases. Cytoscape integration for the construction and visualization of pathways for Molecular targets of Model Organisms. Productivity metrics for medical researchers in rare diseases. iBIOFind 2013 database now includes over 150 medical schools in the US along with Clinical Translational Science Award Institutions for the generation of biomedical knowledge, biomedical informatics and Researcher Profiles.

Proper citation: iBIOFind (RRID:SCR_001587) Copy   

•   Source: SciCrunch Registry

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http://www.nitrc.org/projects/plink

Open source whole genome association analysis toolset, designed to perform range of basic, large scale analyses in computationally efficient manner. Used for analysis of genotype/phenotype data. Through integration with gPLINK and Haploview, there is some support for subsequent visualization, annotation and storage of results. PLINK 1.9 is improved and second generation of the software.

Proper citation: PLINK (RRID:SCR_001757) Copy   

•   Source: SciCrunch Registry