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http://human.brain-map.org/static/brainexplorer

Multi modal atlas of human brain that integrates anatomic and genomic information, coupled with suite of visualization and mining tools to create open public resource for brain researchers and other scientists. Data include magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), histology and gene expression data derived from both microarray and in situ hybridization (ISH) approaches. Brain Explorer 2 is desktop software application for viewing human brain anatomy and gene expression data in 3D.

Proper citation: Allen Human Brain Atlas (RRID:SCR_007416) Copy   

•   Source: SciCrunch Registry

http://www.cbioc.com/en/

Commercial organization that discovers, validates & analyzes genomic biomarkers with a focus on body fluid samples. Take advantage of their proven expertise in biomarker signature development and speed up your biomarker studies.

Proper citation: Comprehensive Biomarker Center (RRID:SCR_003901) Copy   

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http://www.europeanlung.org/en/projects-and-research/projects/airprom/

Consortium focused on developing computer and physical models of the airway system for patients with asthma and chronic obstructive pulmonary disease (COPD). Developing accurate models will better predict how asthma and COPD develop, since current methods can only assess the severity of disease. They aim to bridge the gaps in clinical management of airways-based disease by providing reliable models that predict disease progression and the response to treatment for each person with asthma or COPD. A data management platform provides a secure and sustainable infrastructure that semantically integrates the clinical, physiological, genetic, and experimental data produced with existing biomedical knowledge from allied consortia and public databases. This resource will be available for analysis and modeling, and will facilitate sharing, collaboration and publication within AirPROM and with the broader community. Currently the AirPROM knowledge portal is only accessible by AirPROM partners.

Proper citation: AirPROM (RRID:SCR_003827) Copy   

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http://www.chargeconsortium.com/

Consortium formed to facilitate genome-wide association study meta-analyses and replication opportunities among multiple large and well-phenotyped longitudinal cohort studies. A bibliography of CHARGE publications is available. Its founding member cohorts include: * Age, Gene, Environment, Susceptibility Study -- Reykjavik * Atherosclerosis Risk in Communities Study * Cardiovascular Health Study * Framingham Heart Study * Rotterdam Study Additional core cohorts include: * Coronary Artery Risk Development in Young Adults * Family Heart Study * Health, Aging, and Body Composition Study * Jackson Heart Study * Multi-Ethnic Study of Atherosclerosis

Proper citation: Cohorts for Heart and Aging Research in Genomic Epidemiology (RRID:SCR_004034) Copy   

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http://sourceforge.net/projects/gmato/files/?source=navbar

A software tool used for simple sequence repeats (SSR) or microsatellite characterization. It also facilitates SSR marker design on a genomic scale, microsatellite mining at any length, and comprehensive statistical analysis for DNA sequences in any genome at any size. Analysis parameters are customizable.

Proper citation: GMATo (RRID:SCR_000165) Copy   

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http://icbi.at/software/gpviz/gpviz.shtml

A versatile Java-based software used for dynamic gene-centered visualization of genomic regions and/or variants.

Proper citation: GPViz (RRID:SCR_000346) Copy   

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http://genboree.org

A software application and database viewing system for genomic research, more specifically formulti-genome comparison and pattern discovery via genome self-comparison. Data are available for a range of species including Human Chr3, Human Chr12, Sea Urchin, Tribolium, and cow. The Genboree Discovery System is the largest software system developed at the bioinformatics laboratory at Baylor in close collaboration with the Human Genome Sequencing Center. Genboree is a turnkey software system for genomic research. Genboree is hosted on the Internet and, as of early 2007, the number of registered users exceeds 600. While it can be configured to support almost any genome-centric discovery process, a number of configurations already exist for specific applications. Current focus is on enabling studies of genome variation, including array CGH studies, PCR-based resequencing, genome resequencing using comparative sequence assembly, genome remapping using paired-end tags and sequences, genome analysis and annotation, multi-genome comparison and pattern discovery via genome self-comparison. Genboree database and visualization settings, tools, and user roles are configurable to fit the needs of specific discovery processes. Private permanent project-specific databases can be accessed in a controlled way by collaborators via the Internet. Project-specific data is integrated with relevant data from public sources such as genome browsers and genomic databases. Data processing tools are integrated using a plug-in model. Genboree is extensible via flexible data-exchange formats to accommodate project specific tools and processing steps. Our Positional Hashing method, implemented in the Pash program, enables extremely fast and accurate sequence comparison and pattern discovery by employing low-level parallelism. Pash enables fast and sensitive detection of orthologous regions across mammalian genomes, and fast anchoring of hundreds of millions of short sequences produced by next-generation sequencing technologies. We are further developing the Pash program and employing it in the context of various discovery pipelines. Our laboratory participates in the pilot stage of the TCGA (The Cancer Genome Atlas) project. We aim to develop comprehensive, rapid, and economical methods for detecting recurrent chromosomal aberrations in cancer using next-generation sequencing technologies. The methods will allow detection of recurrent chromosomal aberrations in hundreds of small (

Proper citation: Genboree Discovery System (RRID:SCR_000747) Copy   

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http://franklin.imgen.bcm.tmc.edu/

The mission of the Baylor College of Medicine - Shaw Laboratory is to apply methods of statistics and bioinformatics to the analysis of large scale genomic data. Our vision is data integration to reveal the underlying connections between genes and processes in order to cure disease and improve healthcare.

Proper citation: Baylor College of Medicine - Shaw Laboratory (RRID:SCR_000604) 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://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   

•   Source: SciCrunch Registry

http://www.cdc.gov/genomics/default.htm

The Office of Public Health Genomics (OPHG) aims to integrate genomics into public health research, policy, and programs. Doing so could improve interventions designed to prevent and control the country''s leading chronic, infectious, environmental, and occupational diseases. OPHG''s efforts focus on conducting population-based genomic research, assessing the role of family health history in disease risk and prevention, supporting a systematic process for evaluating genetic tests, translating genomics into public health research and programs, and strengthening capacity for public health genomics in disease prevention programs. Goals: To improve public health interventions of diseases of major public health importance, including chronic, infectious, environmental, and occupational diseases, through six major initiatives: * Evaluation of Genomic Applications in Practice and Prevention (EGAPP), * Human Genome Epidemiology Network (HuGENet), * NHANES Collaborative Genomics Project, * Family History Public Health Initiative, * Genomics Translation Research and Programs, and, * Genomic Applications in Practice and Prevention Network (GAPPNet).

Proper citation: Public Health Genomics (RRID:SCR_006462) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov

A portal to biomedical and genomic information. NCBI creates public databases, conducts research in computational biology, develops software tools for analyzing genome data, and disseminates biomedical information for the better understanding of molecular processes affecting human health and disease.

Proper citation: NCBI (RRID:SCR_006472) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/bioc/html/GeneAnswers.html

GeneAnswers provide an integrated tool for given genes biological or medical interpretation. It includes statistical test of given genes and specified categories. Microarray techniques have been widely employed in genomic scale studies for more than one decade. The standard analysis of microarray data is to filter out a group of genes from thousands of probes by certain statistical criteria. These genes are usually called significantly differentially expressed genes. Recently, next generation sequencing (NGS) is gradually adopted to explore gene transcription, methylation, etc. Also a gene list can be obtained by NGS preliminary data analysis. However, this type of information is not enough to understand the potential linkage between identified genes and interested functions. The integrated functional and pathway analysis with gene expression data would be very helpful for researchers to interpret the relationship between the identified genes and proposed biological or medical functions and pathways. The GeneAnswers package provides an integrated solution for a group of genes and specified categories (biological or medical functions, such as Gene Ontology, Disease Ontology, KEGG, etc) to reveal the potential relationship between them by means of statistical methods, and make user-friendly network visualization to interpret the results. Besides the package has a function to combine gene expression profile and category analysis together by outputting concept-gene cross tables, keywords query on NCBI Entrez Gene and application of human based Disease ontology analysis of given genes from other species can help people to understand or discover potential connection between genes and functions. Sponsors: This project was supported in part by Award Number UL1RR025741 from the National Center for Research Resources.

Proper citation: GeneAnswers (RRID:SCR_006498) Copy   

•   Source: SciCrunch Registry

http://gmd.mpimp-golm.mpg.de/

It facilitates the search for and dissemination of mass spectra from biologically active metabolites quantified using Gas chromatography (GC) coupled to mass spectrometry (MS). Use the Search Page to search for a compound of your interest, using the name, mass, formula, InChI etc. as query input. Additionally, a Library Search service enables the search of user submitted mass spectra within the GMD. In parallel to the library search, a prediction of chemical sub-groups is performed. This approach has reached beta level and a publication is currently under review. Using several sub-group specific Decision Trees (DTs), mass spectra are classified with respect to the presence of the chemical moieties within the linked (unknown) compound. Prediction of functional groups (ms analysis) facilitates the search of metabolites within the GMD by means of user submitted GC-MS spectra consisting of retention index (n-alkanes, if vailable) and mass intensities ratios. In addition, a functional group prediction will help to characterize those metabolites without available reference mass spectra included in the GMD so far. Instead, the unknown metabolite is characterized by predicted presence or absence of functional groups. For power users this functionality presented here is exposed as soap based web services. Functional group prediction of compounds by means of GC-EI-MS spectra using Microsoft analysis service decision trees All currently available trained decision trees and sub-structure predictions provided by the GMD interface. Table describes the functional group, optional use of an RI system, record date of the trained decision tree, number of MSTs with proportion of MSTs linked to metabolites with the functional group present for each tree. Average and standard deviation of the 50-fold CV error, namely the ratio false over correctly sorted MSTs in the trained DT, are listed. The GMD website offers a range of mass spectral reference libraries to academic users which can be downloaded free of charge in various electronic formats. The libraries are constituted by base peak normalized consensus spectra of single analytes and contain masses in the range 70 to 600 amu, while the ubiquitous mass fragments typically generated from compounds carrying a trimethylsilyl-moiety, namely the fragments at m/z 73, 74, 75, 147, 148, and 149, were excluded.

Proper citation: GMD (RRID:SCR_006625) Copy   

•   Source: SciCrunch Registry

https://www.fludb.org/brc/home.spg?decorator=influenza

The Influenza Research Database (IRD) serves as a public repository and analysis platform for flu sequence, experiment, surveillance and related data.

Proper citation: Influenza Research Database (IRD) (RRID:SCR_006641) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry