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http://bio2rdf.blogspot.com/

This is a blog about post genomic knowledge. The website''s goal is to make public datasets from the bioinformatics community available in RDF format via standard SPARQL endpoints.

Proper citation: Bio2RDF atlas of post genomic knowledge (RRID:SCR_007991) Copy   

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http://www.animalgenome.org/pigs/nagrp.html

Database and resources on the pig genome.

Proper citation: U.S. Pig Genome Project (RRID:SCR_008151) Copy   

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http://genewindow.nci.nih.gov/

Software tool for pre- and post-genetic bioinformatics and analytical work, developed and used at the Core Genotyping Facility (CGF) at the National Cancer Institute. While Genewindow is implemented for the human genome and integrated with the CGF laboratory data, it stands as a useful tool to assist investigators in the selection of variants for study in vitro, or in novel genetic association studies. The Genewindow application and source code is publicly available for use in other genomes, and can be integrated with the analysis, storage, and archiving of data generated in any laboratory setting. This can assist laboratories in the choice and tracking of information related to genetic annotations, including variations and genomic positions. Features of GeneWindow include: -Intuitive representation of genomic variation using advanced web-based graphics (SVG) -Search by HUGO gene symbol, dbSNP ID, internal CGF polymorphism ID, or chromosome coordinates -Gene-centric display (only when a gene of interest is in view) oriented 5 to 3 regardless of the reference strand and adjacent genes -Two views, a Locus Overview, which varies in size depending on the gene or genomic region being viewed and, below it, a Sequence View displaying 2000 base pairs within the overview -Navigate the genome by clicking along the gene in the Locus Overview to change the Sequence View, expand or contract the genomic interval, or shift the view in the 5 or 3 direction (relative to the current gene) -Lists of available genomic features -Search for sequence matches in the Locus Overview -Genomic features are represented by shape, color and opacity with contextual information visible when the user moves over or clicks on a feature -Administrators can insert newly-discovered polymorphisms into the Genewindow database by entering annotations directly through the GUI -Integration with a Laboratory Information Management System (LIMS) or other databases is possible

Proper citation: GeneWindow (RRID:SCR_008183) 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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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   

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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   

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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   

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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   

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http://www.dpvweb.net/

DPVweb provides a central source of information about viruses, viroids and satellites of plants, fungi and protozoa. Comprehensive taxonomic information, including brief descriptions of each family and genus, and classified lists of virus sequences are provided. The database also holds detailed, curated, information for all sequences of viruses, viroids and satellites of plants, fungi and protozoa that are complete or that contain at least one complete gene. For comparative purposes, it also contains a single representative sequence of all other fully sequenced virus species with an RNA or single-stranded DNA genome. The start and end positions of each feature (gene, non-translated region and the like) have been recorded and checked for accuracy. As far as possible, nomenclature for genes and proteins are standardized within genera and families. Sequences of features (either as DNA or amino acid sequences) can be directly downloaded from the website in FASTA format. The sequence information can also be accessed via client software for PC computers (freely downloadable from the website) that enable users to make an easy selection of sequences and features of a chosen virus for further analyses. The public sequence databases contain vast amounts of data on virus genomes but accessing and comparing the data, except for relatively small sets of related viruses can be very time consuming. The procedure is made difficult because some of the sequences on these databases are incorrectly named, poorly annotated or redundant. The NCBI Reference Sequence project (1) provides a comprehensive, integrated, non-redundant set of sequences, including genomic DNA, transcript (RNA) and protein products, for major research organisms. This now includes curated information for a single sequence of each fully sequenced virus species. While this is a welcome development, it can only deal with complete sequences. An important feature of DPV is the opportunity to access genes (and other features) of multiple sequences quickly and accurately. Thus, for example, it is easy to obtain the nucleotide or amino acid sequences of all the available accessions of the coat protein gene of a given virus species or for a group of viruses. To increase its usefulness further, DPVweb also contains a single representative sequence of all other fully sequenced virus species with an RNA or single-stranded DNA (ssDNA) genome. Sponsors: This site is supported by the Association of Applied Biologists and the Zhejiang Academy of Agricultural Sciences, Hangzhou, People''s Republic of China.

Proper citation: Descriptions of Plant Viruses (RRID:SCR_006656) 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://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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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.molecularevolution.org/software/genomics/velvet

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Software package as de novo genomic assembler for short read sequencing technologies using de Bruijn graphs. Takes in short read sequences, removes errors, then produces high quality unique contigs, retrieves repeated areas between contigs. Can leverage very short reads in combination with read pairs to produce useful assemblies. Operating system Unix/Linux., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Velvet (RRID:SCR_010755) Copy   

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http://homer.ucsd.edu/

Software tools for Motif Discovery and next-gen sequencing analysis. Used for analyzing ChIP-Seq, GRO-Seq, RNA-Seq, DNase-Seq, Hi-C and numerous other types of functional genomics sequencing data sets. Collection of command line programs for unix style operating systems written in Perl and C++.

Proper citation: HOMER (RRID:SCR_010881) Copy   

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http://tagcleaner.sourceforge.net/

A software tool which can automatically detect and efficiently remove tag sequences from genomic and metagenomic datasets.

Proper citation: TagCleaner (RRID:SCR_011846) Copy   

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https://www.q2labsolutions.com/genomics-laboratories

Core provides whole genome to focused set gene expression and genotyping assays along with DNA sequencings services, sequence enrichment technologies and bioinformatics support. Platforms utilized include Affymetrix GeneChip, Agilent Sure Select, Fluidigm Access Arrays, Illumina BeadChip, iScan, Genome Analyzer and Hi-Seq, RainDance Technologies RDT 1000 and, the Pacific Biosciences PacBio RS. Expression Analysis offers solutions for challenging specimens such as whole blood and FFPE tissues, as well as nucleic acid isolation and data analysis services.

Proper citation: Q Squared Solutions Expression Analysis (RRID:SCR_012497) Copy   

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https://nemoarchive.org/

Data repository specifically focused on storage and dissemination of omic data generated from BRAIN Initiative and related brain research projects. Data repository and archive for BCDC and BICCN project, among others. NeMO data include genomic regions associated with brain abnormalities and disease, transcription factor binding sites and other regulatory elements, transcription activity, levels of cytosine modification, histone modification profiles and chromatin accessibility.

Proper citation: NeMOarchive (RRID:SCR_016152) Copy   

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http://disulfind.dsi.unifi.it/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023, Software for predicting the disulfide bonding state of cysteines and their disulfide connectivity, starting from a protein sequence alone and may be useful in other genomic annotation tasks.

Proper citation: DISULFIND (RRID:SCR_016072) Copy   

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http://genome.imim.es/software/gfftools/GFF2APLOT.html

Software application to visualize the alignment of two genomic sequences together with their annotations. Used to generate print-quality images for comparative genome sequence analysis.

Proper citation: Gff2aplot (RRID:SCR_016128) Copy   

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