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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://bioconductor.org/packages/release/bioc/html/scater.html

Software toolkit for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control. This package facilitates pre-processing, quality control, normalization and visualization of scRNA-seq data.

Proper citation: scater (RRID:SCR_015954) Copy   

•   Source: SciCrunch Registry

https://github.com/maypoleflyn/BSATOS

Software tools for next generation sequencing based bulked segregation analysis for outbreeding species including fruit trees such as apple or cirtus. Used to improve gene mapping efficiency of next generation sequencing based segregant analysis in outbreeding species and realize rapid candidate gene mining based on multi-omics data.

Proper citation: Bulked segregation analysis tools for outbreeding species (RRID:SCR_017009) Copy   

•   Source: SciCrunch Registry

http://portal.brain-map.org/

Portal provides access to data and web based applications created for benefit of global research community by Allen Institute for Brain Science. Projects to ombine genomics with neuroanatomy by creating gene expression maps for mouse and human brain. Mouse Brain Atlas, Human Brain Atlas, Developing Mouse Brain Atlas, Developing Human Brain Atlas, Mouse Connectivity Atlas, Non-Human Primate Atlas, and Mouse Spinal Cord Atlas and three related projects Glioblastoma, Mouse Diversity, and Sleep data banks, are used to advance various fields of science especially in neurobiological diseases.

Proper citation: Allen Brain Atlas (RRID:SCR_017001) Copy   

•   Source: SciCrunch Registry

https://github.com/KhiabanianLab/TuBA

Software tool as graph based unsupervised biclustering algorithm to identify alterations in tumors based on hypothesis that gene pairs relevant to clinical process share statistically significant number of samples with extreme expression.

Proper citation: Tunable Biclustering Algorithm (RRID:SCR_017121) Copy   

•   Source: SciCrunch Registry

http://pklab.med.harvard.edu/scde/pagoda.links.html

Software tool for analyzing transcriptional heterogeneity to detect statistically significant ways in which measured cells can be classified. Used to resolve multiple, potentially overlapping aspects of transcriptional heterogeneity by testing gene sets for coordinated variability among measured cells.

Proper citation: PAGODA (RRID:SCR_017099) Copy   

•   Source: SciCrunch Registry

https://github.com/PGB-LIV/VAPPER

Software tool for analysis of variant antigens in African trypanosomes. Used for quantitative analysis of antigenic diversity in systems data of genomes, transcriptomes, and proteomes, called Variant Antigen Profiling to understand how antigenic diversity relates to clinical outcome, how antigen genes may be used as epidemiological markers of virulence, and in measuring gene expression during experimental infections.

Proper citation: VAPPER (RRID:SCR_016993) Copy   

•   Source: SciCrunch Registry

https://www.sciencescott.com/pyminer

Software tool to automate cell type identification, cell type-specific pathway analyses, graph theory-based analysis of gene regulation, and detection of autocrine-paracrine signaling networks. Finds Gene and Autocrine-Paracrine Networks from Human Islet scRNA-Seq.

Proper citation: PyMINEr (RRID:SCR_016990) Copy   

•   Source: SciCrunch Registry

https://github.com/dgrun/RaceID

Algorithm for identification of rare and abundant cell types from single cell transcriptome data. Based on transcript counts obtained with unique molecular identifies. Used for discovering rare cell types and corresponding marker genes in healthy and diseased organs. Operating system Unix/Linux, Mac OS, Windows.

Proper citation: RaceID (RRID:SCR_017045) Copy   

•   Source: SciCrunch Registry

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

Software application for performing Gene Ontology analysis on RNAseq data and other length biased data. Used to reduce complexity and highlight biological processes in genome wide expression studies.

Proper citation: Goseq (RRID:SCR_017052) Copy   

•   Source: SciCrunch Registry

https://pcago.bioinf.uni-jena.de/

Interactive web service for analysis of RNA-Seq read count data with principal component analysis (PCA) and agglomerative clustering. Includes features like read count normalization, filtering read counts by gene annotation and visualization options.

Proper citation: PCAGO (RRID:SCR_017033) Copy   

•   Source: SciCrunch Registry

https://github.com/powellgenomicslab/ascend

Software R package for analysis of single cell RNA-seq expression, normalization and differential expression data. Provides framework to perform cell and gene filtering, quality control, normalization, dimension reduction, clustering, differential expression, and visualization functions.

Proper citation: ascend (RRID:SCR_017257) Copy   

•   Source: SciCrunch Registry

https://github.com/SionBayliss/PIRATE

Software pangenomics toolbox for clustering diverged orthologues in bacteria. Used to identify and classify orthologous gene families in bacterial pangenomes over wide range of sequence similarity thresholds.

Proper citation: PIRATE (RRID:SCR_017265) Copy   

•   Source: SciCrunch Registry

https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger

Software tool as set of analysis pipelines that process chromium single cell RNA-seq output to align reads, generate feature-barcode matrices and perform clustering and gene expression analysis by 10xGenomics.

Proper citation: Cell Ranger (RRID:SCR_017344) Copy   

•   Source: SciCrunch Registry

http://pathwaynet.princeton.edu/

Web user interface for interaction predictions of human gene networks and integrative analysis of user data types that takes advantage of data from diverse tissue and cell-lineage origins. Predicts presence of functional association and interaction type among human genes or its protein products on whole genome scale. Used to analyze experimetnal gene in context of interaction networks.

Proper citation: PathwayNet (RRID:SCR_017353) Copy   

•   Source: SciCrunch Registry

https://github.com/epurdom/clusterExperiment

Software open source R package for executing, evaluating and visualizing different clusterings of experimental data, including data from single cell RNA-Seq studies. Software for running and comparing different clusterings of single cell sequencing data.

Proper citation: clusterExperiment (RRID:SCR_017439) Copy   

•   Source: SciCrunch Registry

https://www.hmtphenome.uniba.it

Collection of data about variants, genes, phenotypes and diseases involved in mitochondrial functionality. Users can search for variant position, gene, phenotype or disease and retrieve all related information through integrated network of biological entities.

Proper citation: HmtPhenome (RRID:SCR_017289) Copy   

•   Source: SciCrunch Registry