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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   

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http://www.emouseatlas.org/emage

A database of in situ gene expression data in the developing mouse embryo and an accompanying suite of tools to search and analyze the data. mRNA in situ hybridization, protein immunohistochemistry and transgenic reporter data is included. The data held is spatially annotated to a framework of 3D mouse embryo models produced by EMAP (e-Mouse Atlas Project). These spatial annotations allow users to query EMAGE by spatial pattern as well as by gene name, anatomy term or Gene Ontology (GO) term. The conceptual framework which houses the descriptions of the gene expression patterns in EMAGE is the EMAP Mouse Embryo Anatomy Atlas. This consists of a set of 3D virtual embryos at different stages of development, as well as an accompanying ontology of anatomical terms found at each stage. The raw data images can be conventional 2D photographs (of sections or wholemount specimens) or 3D images of wholemount specimens derived from Optical Projection Tomography (OPT) or confocal microscopy. Users may submit data using a Data submission tool or without.

Proper citation: EMAGE Gene Expression Database (RRID:SCR_005391) Copy   

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http://genecodis.cnb.csic.es/

Web-based tool for the ontological analysis of large lists of genes. It can be used to determine biological annotations or combinations of annotations that are significantly associated to a list of genes under study with respect to a reference list. As well as single annotations, this tool allows users to simultaneously evaluate annotations from different sources, for example Biological Process and Cellular Component categories of Gene Ontology., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GeneCodis (RRID:SCR_006943) Copy   

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

Archive of aggregated information about sequence variation and its relationship to human health. Provides reports of relationships among human variations and phenotypes along with supporting evidence. Submissions from clinical testing labs, research labs, locus-specific databases, expert panels and professional societies are welcome. Collects reports of variants found in patient samples, assertions made regarding their clinical significance, information about submitter, and other supporting data. Alleles described in submissions are mapped to reference sequences, and reported according to HGVS standard.

Proper citation: ClinVar (RRID:SCR_006169) Copy   

•   Source: SciCrunch Registry

http://www.oeb.harvard.edu/faculty/hartl/old_site/lab/publications/GeneMerge.html

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. Web-based and standalone application that returns a wide range of functional genomic data for a given set of study genes and provides rank scores for over-representation of particular functions or categories in the data. It uses the hypergeometric test statistic which returns statistically correct results for samples of all sizes and is the #2 fastest GO tool available (Khatri and Draghici, 2005). GeneMerge can be used with any discrete, locus-based annotation data, including, literature references, genetic interactions, mutant phenotypes as well as traditional Gene Ontology queries. GeneMerge is particularly useful for the analysis of microarray data and other large biological datasets. The big advantage of GeneMerge over other similar programs is that you are not limited to analyzing your data from the perspective of a pre-packaged set of gene-association data. You can download or create gene-association files to analyze your data from an unlimited number of perspectives. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GeneMerge (RRID:SCR_005744) Copy   

•   Source: SciCrunch Registry

http://coot.embl.de/g2d/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 22, 2016. A database of candidate genes for mapped inherited human diseases. Candidate priorities are automatically established by a data mining algorithm that extracts putative genes in the chromosomal region where the disease is mapped, and evaluates their possible relation to the disease based on the phenotype of the disorder. Data analysis uses a scoring system developed for the possible functional relations of human genes to genetically inherited diseases that have been mapped onto chromosomal regions without assignment of a particular gene. Methodology can be divided in two parts: the association of genes to phenotypic features, and the identification of candidate genes on a chromosonal region by homology. This is an analysis of relations between phenotypic features and chemical objects, and from chemical objects to protein function terms, based on the whole MEDLINE and RefSeq databases.

Proper citation: Candidate Genes to Inherited Diseases (RRID:SCR_008190) Copy   

•   Source: SciCrunch Registry

http://neibank.nei.nih.gov

An integrated resource for genomics and bioinformatics in vision research including expressed sequence tag (EST) data and sequence-verified cDNA clones for multiple eye tissues of several species, web-based access to human eye-specific SAGE data through EyeSAGE, and comprehensive, annotated databases of known human eye disease genes and candidate disease gene loci. All expression- and disease-related data are integrated in EyeBrowse, an eye-centric genome browser. NEIBank provides a comprehensive overview of current knowledge of the transcriptional repertoires of eye tissues and their relation to pathology. The data can be interrogated in several ways. Specific gene names can be entered into the search window. Alternatively, regions of the genome can be displayed. For example, entering two STS markers separated by a semicolon (e.g. RH18061;RH80175) allows the display of the entire chromosomal region associated with the mapping of a specific disease locus. ESTs for each tissue can then be displayed to help in the selection of candidate genes. In addition, sequences can be entered into a BLAST search and rapidly aligned on the genome, again showing eye derived ESTs for the same region. To see the same region at the full UCSC site, cut and paste the location from the position window of the genome browser. EyeBrowse includes a custom track display SAGE data for human eye tissues derived from the EyeSAGE project. The track shows the normalized sum of SAGE tag counts from all published eye-related SAGE datasets centered on the position of each identifiable Unigene cluster. This indicates relative activity of each gene locus in eye. Clicking on the vertical count bar for a particular location will bring up a display listing gene details and linking to specific SAGE counts for each eye SAGE library and comparisons with normalized sums for neural and non-neural tissues. To view or alter settings for the EyeSAGE track on EyeBrowse, click on the vertical gray bar at the left of the display. Other custom tracks display known eye disease genes and mapped intervals for candidate loci for retinal disease, cataract, myopia and cornea disease. These link back to further information at NEIBank.

Proper citation: NEIBank (RRID:SCR_007294) Copy   

•   Source: SciCrunch Registry

https://sourceforge.net/projects/fraggenescan/

A software application for finding fragmented genes in short reads and may be applied to predict prokaryotic genes in incomplete assemblies or complete genomes.

Proper citation: FragGeneScan (RRID:SCR_011929) Copy   

•   Source: SciCrunch Registry

http://ecoliwiki.net/colipedia/index.php/Welcome_to_EcoliWiki

A component of EcoliHub, EcoliWiki is a wiki-based system for finding, editing, and adding information about E. coli K-12 and other model organism strains of E. coli. EcoliWiki is being constructed to include information about bacteriophage, plasmids, and mobile genetic elements. Information should be easily accessible and correct, and users have the right to edit any information they feel is incorrect. Most of the E. coli information was initially seeded with a subset of information from parsing EcoCyc data dumps. For phage gamma and the F plasmid, Genbank accessions were converted to GFF, which was parsed into the appropriate tables. Other sources of content include: * user additions * monthly addition of annotations from EcoCyc * structural data from the PDB * domains and motif information from InterPro * various databases including EcoGene, RegulonDB, Genbank, GenoBase, ASAP * many many scientific papers EcoliWiki participates in the RefGenome project. EcoliWiki provides REST web services as part of the EcoliHub Web Services infrastructure project.

Proper citation: EcoliWiki (RRID:SCR_010656) Copy   

•   Source: SciCrunch Registry

http://www.anim.med.kyoto-u.ac.jp/nbr/default.aspx

NBRP-Rat was established to overcome limitations associated with properly utilizing existing rat resources. The collection of existing strains and genetic sub strains, phenotypic and genotypic characterization, cryopreservation of embryos, distribution of the collected rat strains, and a publicly accessible database of all assembled data are the major goals of this project. Once achieved, this unique database including the unique rat strains will become a powerful tool for biomedical research. A catalog of comparable, standardized and well characterized rat strains will lead to new and more precise research topics as well as it will facilitate biomedical sciences, drug discovery, advanced chemical research, and contributes to life sciences worldwide. As mentioned before, the major goals of NBRP-Rat are the collection, preservation and supply of rat strains. The repository includes strains from Japan and abroad, spontaneous mutants, congenic and recombinant strains as well as transgenic and mutagenized rats. Deposited rat strains are not only conserved as cryopreserved embryos and sperm. Many reference and frequently used rat strains are also maintained as living animals under SPF conditions. Furthermore, NBRP-rat provides a unique database on various rat strain phenotypes accompanied with basic genetic information. This allows scientists the selection of standardized and research specific strains. The animals themselves are provided free of charge to the research community (except for shipping costs). Sponsors: This project is one part of the National BioResource Projects (NBRP) in Japan for more than 20 species including animals, plants, microbes, tissues and DNAs. It is founded by the Japanese Ministry of Education, Culture, Sports, Science and Technology (Monkasho) and started in 2002.

Proper citation: National Bio Resource Project for the Rat. (RRID:SCR_012774) Copy   

•   Source: SciCrunch Registry

http://genes.toronto.edu/

The Frey Lab develops techniques that use large scale datasets to derive predictive models of how genes and many other genomic features act in combination to produce genetic messages that control cellular activities. We have most recently focused on how organisms use alternative splicing to generate a tremendous level of biological complexity that cannot be explained by gene expression alone (Nature, 2010). Some of the tools, software and databases provided by the Frey Lab are affinity propagation, splicing prediction, PTMClust - A Post-translational Modification Refinement Algorithm, the ''epitome'': A new model of patterns, transformation invariant clustering and subspaces, learning flexible sprites from images and videos, phase unwrapping by loopy belief propagation, useful Matlab scripts, bioinformatics links, and SeedSearcher: A motif finder.

Proper citation: Frey Lab (RRID:SCR_008859) Copy   

•   Source: SciCrunch Registry

http://www.ggact.org

Project portal for a collaboration between Boston Children’s Hospital, Cincinnati Children’s Hospital Medical Center, and the University of California Los Angeles with funding from NIH NCATS. It aims to support investigators to rapidly translate complex gene and cell therapies to early phase, investigator-initiated clinical trials.

Proper citation: Growing Gene and Cell Therapy Cooperative (RRID:SCR_015861) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/release/bioc/html/DESeq2.html

Software package for differential gene expression analysis based on the negative binomial distribution. Used for analyzing RNA-seq data for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates.

Proper citation: DESeq2 (RRID:SCR_015687) Copy   

•   Source: SciCrunch Registry

http://hms-dbmi.github.io/scde/index.html

Software package that implements a set of statistical methods for analyzing single-cell RNA-seq data, including differential expression analysis (Kharchenko et al.) and pathway and geneset overdispersion analysis (Fan et al.)

Proper citation: SCDE (RRID:SCR_015952) Copy   

•   Source: SciCrunch Registry

http://caprica.genetics.kcl.ac.uk/BRAINEAC/

Database for the UK Brain Expression Consortium (UKBEC) dataset that comprises of brains from individuals free of neurodegenerative disorders. The aim of Braineac is to release to the scientific community a valid instrument to investigate the genes and SNPs associated with neurological disorders.

Proper citation: Braineac (RRID:SCR_015888) Copy   

•   Source: SciCrunch Registry

https://ccb.jhu.edu/software/stringtie/

Software application for assembling of RNA-Seq alignments into potential transcripts. It enables improved reconstruction of a transcriptome from RNA-seq reads. This transcript assembling and quantification program is implemented in C++ .

Proper citation: StringTie (RRID:SCR_016323) Copy   

•   Source: SciCrunch Registry