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http://ikmbio.csie.ncku.edu.tw/coin/home.php

A web-based system that assess articles according to their term correlations among sentences. It employs the co-occurrence relations and their network centralities to evaluate the influence of biomedical terms from Comparative Toxicogenomics Database (CTD)., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: CoIN (RRID:SCR_005332) Copy   

•   Source: SciCrunch Registry

http://en.wikipedia.org/wiki/Gene_Wiki

The Gene Wiki is a project that facilitates transferring information on human genes to Wikipedia article stubs with the goal of promoting collaboration and expansion of the articles. Number of gene articles The human genome contains an estimated 20,00025,000 protein-coding genes. The goal of the Gene Wiki project is to create seed articles for every notable human gene, that is, every gene whose function has been assigned in the peer-reviewed scientific literature. Approximately half of human genes have assigned function, therefore the total number of articles seeded by the Gene Wiki project would be expected to be in the range of 10,000 - 15,000. To date, approximately 10,271 articles have been created or augmented to include Gene Wiki project content. Expansion Once seed articles have been established, the hope and expectation is that these will be annotated and expanded by editors ranging in experience from the lay audience to students to professionals and academics. Proteins encoded by genes The majority of genes encode proteins hence understanding the function of a gene generally requires understanding of the function of the corresponding protein. In addition to including basic information about the gene, the project therefore also includes information about the protein encoded by the gene. Stubs for the Gene Wiki project are created by a bot and contain links to the following primary gene/protein databases * HUGO Gene Nomenclature Committee official gene name * Entrez Gene database * OMIM (Mendelian Inheritance in Man) database that catalogues all the known diseases with a genetic component * Amigo Gene Ontology * HomoloGene gene homologs in other species * SymAtlasRNA gene expression pattern in tissues * Protein Data Bank 3D structure of protein encoded by the gene * Uniprot (universal protein resource) a central repository of protein data

Proper citation: Gene Wiki (RRID:SCR_005317) Copy   

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http://enigma.ini.usc.edu/

Network that brings together researchers in imaging genomics, to understand brain structure and function, based on MRI, DTI, fMRI and genomewide association scan (GWAS) data. The ENIGMA Network has several goals: * to create a network of like-minded individuals, interested in pushing forward the field of imaging genetics * to ensure promising findings are replicated via member collaborations, in order to satisfy the mandates of most journals * to share ideas, algorithms, data, and information on promising findings or methods * to facilitate training, including workshops and conferences on key methods and emerging directions in imaging genetics. Data sharing with other members of the ENIGMA Network is optional and by no means a requirement of joining the network. Genetics and Imaging Protocols are available.

Proper citation: ENIGMA: Enhancing Neuro Imaging Genetics Through Meta-Analysis (RRID:SCR_005515) Copy   

•   Source: SciCrunch Registry

http://nlmfoundation.org/

The primary mission of the Nancy Lurie Marks (NLM) Family Foundation is to help people with autism lead fulfilling and rewarding lives. The Foundation is committed to understanding autism from a scientific perspective, increasing opportunities and services available to the autism community and educating the public about autism. In pursuit of its mission, the Foundation develops and provides grants to programs in research, clinical care, policy, advocacy and education. Founded by Nancy Lurie Marks over 25 years ago, the NLM Family Foundation is one of the largest supporters of initiatives in these areas. The principal goal of the scientific program is to achieve a deeper understanding of the biological basis of autism, focusing on genetics, synaptic chemistry, the neurobiology of communication, systems biology and the physiology of movement. The Foundation funds peer-reviewed research, the development of collaborative investigator projects, and research fellowship programs. Through sponsorship of scientific conferences, symposia and workshops, the Foundation seeks to encourage innovation and provide a springboard to generate new avenues of shared inquiry. The NLM Family Foundation supports programs which focus on novel ways to improve the communication and social abilities of those with autism. Other programs are designed to increase advocacy for legal rights and access to support services for persons with autism, and to increase community understanding and openness to inclusion through education and documentary films.

Proper citation: Nancy Lurie Marks Family Foundation (RRID:SCR_005455) Copy   

•   Source: SciCrunch Registry

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

With the CGAP GO browser, you can browse through the GO vocabularies, and find human and mouse genes assigned to each term. GO data updated every few months. Platform: Online tool

Proper citation: CGAP GO Browser (RRID:SCR_005676) Copy   

•   Source: SciCrunch Registry

http://vortex.cs.wayne.edu/projects.htm#Onto-Express

The typical result of a microarray experiment is a list of tens or hundreds of genes found to be differentially regulated in the condition under study. Independently of the methods used to select these genes, the common task faced by any researcher is to translate these lists of genes into a better understanding of the biological phenomena involved. Currently, this is done through a tedious combination of searches through the literature and a number of public databases. We developed Onto-Express (OE) as a novel tool able to automatically translate such lists of differentially regulated genes into functional profiles characterizing the impact of the condition studied. OE constructs functional profiles (using Gene Ontology terms) for the following categories: biochemical function, biological process, cellular role, cellular component, molecular function and chromosome location. Statistical significance values are calculated for each category. We demonstrated the validity and the utility of this comprehensive global analysis of gene function by analyzing two breast cancer data sets from two separate laboratories. OE was able to identify correctly all biological processes postulated by the original authors, as well as discover novel relevant mechanisms (Draghici et.al, Genomics, 81(2), 2003). Other results obtained with Onto-Express can be found in Khatri et.al., Genomics. 79(2), 2002. Custom level of abstraction of the Gene Ontology. User account required. Platform: Online tool

Proper citation: Onto-Express (RRID:SCR_005670) Copy   

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http://www.genetools.us

Web-service providing access to database that brings together information from broad range of resources. Web application for functional annotation and statistical hypothesis testing. Provides tools for analysis of genomic and microarray data. Collection of tools include Bibliographic Information,Databases,Gene Annotation,Gene Regulation, Microarray,Proteins,Sequence Manipulation - Nucleic Acids,Sequence Manipulation - Protein, Systems Biology.

Proper citation: GeneTools (RRID:SCR_005663) Copy   

•   Source: SciCrunch Registry

http://manatee.sourceforge.net/

Manatee is a web-based gene evaluation and genome annotation tool; Manatee can store and view annotation for prokaryotic and eukaryotic genomes. The Manatee interface allows biologists to quickly identify genes and make high quality functional assignments, such as GO classifications, using search data, paralogous families, and annotation suggestions generated from automated analysis. Manatee can be downloaded and installed to run under the CGI area of a web server, such as Apache. Platform: Online tool, Linux compatible, Solaris

Proper citation: Manatee (RRID:SCR_005685) Copy   

•   Source: SciCrunch Registry

http://genenet2.uthsc.edu/geneinfoviz/search.php

GeneInfoViz is a web based tool for batch retrieval of gene function information, visualization of GO structure and construction of gene relation networks. It takes a input list of genes in the form of LocusLink ID, UniGeneID, gene symbol, or accession number and returns their functional genomic information. Based on the GO annotations of the given genes, GeneInfoViz allows users to visualize these genes in the DAG structure of GO, and construct a gene relation network at a selected level of the DAG. Platform: Online tool

Proper citation: GeneInfoViz (RRID:SCR_005680) Copy   

•   Source: SciCrunch Registry

http://sybil.sourceforge.net/

A web-based software package for comparative genomics.

Proper citation: Sybil (RRID:SCR_005593) Copy   

•   Source: SciCrunch Registry

https://github.com/transmart

tranSMART is a knowledge management platform that enables scientists to develop and refine research hypotheses by investigating correlations between genetic and phenotypic data, and assessing their analytical results in the context of published literature and other work. tranSMART is licensed through GPL 3. The integration, normalization, and alignment of data in tranSMART permits users to explore data very efficiently to formulate new research strategies. Some of tranSMART''s specific applications include: * Revalidating previous hypotheses * Testing and refining novel hypotheses * Conducting cross-study meta-analysis * Searching across multiple data sources to find associations of concepts, such as a gene''s involvement in biological processes or experimental results * Comparing biological processes and pathways among multiple data sets from related diseases or even across multiple therapeutic areas Data Repository The tranSMART Data Repository combines a data warehouse with access to federated sources of open and commercial databases. tranSMART accommodates: * Phenotypic data, such as demographics, clinical observations, clinical trial outcomes, and adverse events * High content biomarker data, such as gene expression, genotyping, pharmacokinetic and pharmaco-dynamics markers, metabolomics data, and proteomics data * Unstructured text-data, such as published journal articles, conference abstracts and proceedings, and internal studies and white papers * Reference data from sources such as MeSH, UMLS, Entrez, GeneGo, Ingenuity, etc. * Metadata providing context about datasets, allowing users to assess the relevance of results delivered by tranSMART Data in tranSMART is aligned to allow identification and analysis of associations between phenotypic and biomarker data, and it is normalized to conform with CDISC and other standards to facilitate search and analysis across different data sources. tranSMART also enables investigators to search published literature and other text sources to evaluate their analysis in the context of the broader universe of reported research. External data can also be integrated into the tranSMART data repository, either from open data projects like GEO, EBI Array Express, GCOD, or GO, or from commercially available data sources. Making data accessible in tranSMART enables organizations to leverage investments in manual curation, development costs of automated ETL tools, or commercial subscription fees across multiple research groups. Dataset Explorer tranSMART''s Dataset Explorer provides flexible, powerful search and analysis capabilities. The core of the Dataset Explorer integrates and extends the open source i2b2 application, Lucene text indexing, and GenePattern analytical tools. Connections to other open source and commercial analytical tools such as Galaxy, Integrative Genomics Viewer, Plink, Pathway Studio, GeneGo, Spotfire, R, and SAS can be established to expand tranSMART''s capabilities. tranSMART''s design allows organizations flexibility in selecting analytical tools accessible through the Dataset Explorer, and provides file export capabilities to enable researchers to use tools not accessible in the tranSMART portal.

Proper citation: tranSMART (RRID:SCR_005586) Copy   

•   Source: SciCrunch Registry

http://mor.nlm.nih.gov/perl/gennav.pl

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 31,2025. GenNav searches GO terms and annotated gene products, and provides a graphical display of a term's position in the GO DAG.

Proper citation: GenNav (RRID:SCR_000147) Copy   

•   Source: SciCrunch Registry

http://cistrome.org

Web based integrative platform for transcriptional regulation studies.

Proper citation: Cistrome (RRID:SCR_000242) Copy   

•   Source: SciCrunch Registry

http://www.mmnt.net/db/0/0/ftp-genome.wi.mit.edu/distribution/GISTIC2.0

Software to identify genes targeted by somatic copy-number alterations (SCNAs) that drive cancer growth. By separating SCNA profiles into underlying arm-level and focal alterations, they improve the estimation of background rates for each category.

Proper citation: GISTIC (RRID:SCR_000151) Copy   

•   Source: SciCrunch Registry

http://beetlebase.org/

A centralized sequence database and community resource for Tribolium genetics, genomics and developmental biology containing genomic sequence scaffolds mapped to 10 linkage groups, genetic linkage maps, the official gene set, Reference Sequences from NCBI (RefSeq), predicted gene models, ESTs and whole-genome tiling array data representing several developmental stages. The current version of Beetlebase is built on the Tribolium castaneum 3.0 Assembly (Tcas 3.0) released by the Human Genome Sequencing Center at the Baylor College of Medicine. The database is constructed using the upgraded Generic Model Organism Database (GMOD) modules. The genomic data is stored in a PostgreSQL relational database using the Chado schema and visualized as tracks in GBrowse. The genetic map is visualized using the comparative genetic map viewer CMAP. To enhance search capabilities, the BLAST search tool has been integrated with the GMOD tools. Tribolium castaneum is a very sophisticated genetic model organism among higher eukaryotes. As the member of a primitive order of holometabolous insects, Coleoptera, Tribolium is in a key phylogenetic position to understand the genetic innovations that accompanied the evolution of higher forms with more complex development. Coleoptera is also the largest and most species diverse of all eukaryotic orders and Tribolium offers the only genetic model for the profusion of medically and economically important species therein. The genome sequences may be downloaded.

Proper citation: BeetleBase (RRID:SCR_001955) Copy   

•   Source: SciCrunch Registry

http://www.tipharma.com/pharmaceutical-research-projects/predictive-drug-disposition-and-toxicology-research/adr-safety-biomarkers.html

Consortium to develop novel in vitro predictive screening tools and in vivo translational models and biomarkers to improve adverse drug reaction (ADR) hazard identification. This project studies the metabolic effects of eight drugs (among which are paracetamol and diclofenac ) with known side effects in the liver. By looking into the mechanics on a level ranging from the molecule to the patient, the researchers in this project aim to find biomarkers and develop tools for the early prediction of side effects of drugs. One of the breakthroughs in the project is the discovery that a person''''s genetic profile appears to be one of the mechanics that have an influence on the resistance to adverse drug reactions. The ability to identify adverse effects in an early stage will prevent much discomfort in patients and economic loss. Several PhD theses have been written from this project.

Proper citation: Towards novel translational safety biomarkers for adverse drug toxicity (RRID:SCR_004006) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.aecom.yu.edu/index.htm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 6, 2023. Primary informatics resource for joint research efforts of the Albert Einstein College of Medicine and Montefiore Medical Center to facilitate the study and understanding of biological processes, clinical disorders, pathologic abnormalities, and the relationships among them, using a wide variety of informatics techniques, applications, and user training. Their services include: * Collaboration on research design to enable effective data management throughout all phases of a project * Provision of management capability for large volumes of data generated by microarrays and related technologies * Provision and supports a software toolchest for data capture, retrieval, and analysis * Design and implementation of custom interfaces to incorporate existing or separately designed databases into the central data management architecture * Support for data management for the Biorepository, to enhance specimen storage, identification, and linkage with clinical data * Ensuring conformity of data elements and structures to national standards via participation in standards organizations, facilitating intramural and extramural collaboration * Providing individualized support to end-users with bioinformatics training needs * Serving as a bioinformatics liaison to other research institutes and organizations * Providing data management support for clinical research * Providing a common, secure repository for clinical, experimental, and biosample storage data

Proper citation: Einstein-Montefiore ICTR Research Informatics Core (RRID:SCR_003451) Copy   

•   Source: SciCrunch Registry

http://www.sanger.ac.uk/resources/databases/exomiser/query/exomiser2

A Java program that functionally annotates variants from whole-exome sequencing data starting from a VCF (Variant Call Format) file (version 4). The functional annotation code is based on Annovar and uses UCSCKnownGene transcript definitions and hg19 genomic coordinates. Variants are prioritized according to user-defined criteria on variant frequency, pathogenicity, quality, inheritance pattern, phenotype data from human and model organisms, and proximity in the interactome to phenotypically similar genes.

Proper citation: Exomiser (RRID:SCR_002192) Copy   

•   Source: SciCrunch Registry

https://www.proteinspire.org/MOPED/

An expanding multi-omics resource that enables rapid browsing of gene and protein expression information from publicly available studies on humans and model organisms. MOPED also serves the greater research community by enabling users to visualize their own expression data, compare it with existing studies, and share it with others via private accounts. MOPED uniquely provides gene and protein level expression data, meta-analysis capabilities and quantitative data from standardized analysis utilizing SPIRE (Systematic Protein Investigative Research Environment). Data can be queried for specific genes and proteins; browsed based on organism, tissue, localization and condition; and sorted by false discovery rate and expression. MOPED links to various gene, protein, and pathway databases, including GeneCards, Entrez, UniProt, KEGG and Reactome. The current version of MOPED (MOPED 2.5) The current version of MOPED (MOPED 2.5, 2014) contains approximately 5 million total records including ~260 experiments and ~390 conditions.

Proper citation: MOPED - Model Organism Protein Expression Database (RRID:SCR_006065) Copy   

•   Source: SciCrunch Registry

http://genome.jgi.doe.gov/programs/fungi/index.jsf

Fungal genomics database and interactive analytical tools that integrates all fungal genomes for diverse fungi that are important for energy and environment, the focus of the JGI Fungal program. It integrates genomics data from the DOE JGI and its users and promotes user community participation in data submission, annotation and analysis. Over 100 newly sequenced and annotated fungal genomes from JGI and elsewhere are available to the public through MycoCosm, and new annotated genomes are being added to this resource upon completion of annotation. MycoCosm offers web-based genome analysis tools for fungal biologists to ''navigate'' through sequenced genomes and explore them in the context of ''genome-centric'' and ''comparative views''.

Proper citation: MycoCosm (RRID:SCR_005312) Copy   

•   Source: SciCrunch Registry