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http://www.jcvi.org/charprotdb/index.cgi/home

The Characterized Protein Database, CharProtDB, is designed and being developed as a resource of expertly curated, experimentally characterized proteins described in published literature. For each protein record in CharProtDB, storage of several data types is supported. It includes functional annotation (several instances of protein names and gene symbols) taxonomic classification, literature links, specific Gene Ontology (GO) terms and GO evidence codes, EC (Enzyme Commisssion) and TC (Transport Classification) numbers and protein sequence. Additionally, each protein record is associated with cross links to all public accessions in major protein databases as ��synonymous accessions��. Each of the above data types can be linked to as many literature references as possible. Every CharProtDB entry requires minimum data types to be furnished. They are protein name, GO terms and supporting reference(s) associated to GO evidence codes. Annotating using the GO system is of importance for several reasons; the GO system captures defined concepts (the GO terms) with unique ids, which can be attached to specific genes and the three controlled vocabularies of the GO allow for the capture of much more annotation information than is traditionally captured in protein common names, including, for example, not just the function of the protein, but its location as well. GO evidence codes implemented in CharProtDB directly correlate with the GO consortium definitions of experimental codes. CharProtDB tools link characterization data from multiple input streams through synonymous accessions or direct sequence identity. CharProtDB can represent multiple characterizations of the same protein, with proper attribution and links to database sources. Users can use a variety of search terms including protein name, gene symbol, EC number, organism name, accessions or any text to search the database. Following the search, a display page lists all the proteins that match the search term. Click on the protein name to view more detailed annotated information for each protein. Additionally, each protein record can be annotated.

Proper citation: CharProtDB: Characterized Protein Database (RRID:SCR_005872) Copy   

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http://crcview.hegroup.org/

Web-based microarray data analysis and visualization system powered by CRC, or Chinese Restaurant cluster, a Dirichlet process model-based clustering algorithm recently developed by Dr. Steve Qin. It also incorporates several gene expression analysis programs from Bioconductor, including GOStats, genefilter, and Heatplus. CRCView also installs from the Bioconductor system 78 annotation libraries of microarray chips for human (31), mouse (24), rat (14), zebrafish (1), chicken (1), Drosophila (3), Arabidopsis (2), Caenorhabditis elegans (1), and Xenopus Laevis (1). CRCView allows flexible input data format, automated model-based CRC clustering analysis, rich graphical illustration, and integrated Gene Ontology (GO)-based gene enrichment for efficient annotation and interpretation of clustering results. CRC has the following features comparing to other clustering tools: 1) able to infer number of clusters, 2) able to cluster genes displaying time-shifted and/or inverted correlations, 3) able to tolerate missing genotype data and 4) provide confidence measure for clusters generated. You need to register for an account in the system to store your data and analyses. The data and results can be visited again anytime you log in.

Proper citation: CRCView (RRID:SCR_007092) Copy   

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http://www.cpc.unc.edu/projects/addhealth

Longitudinal study of a nationally representative sample of adolescents in grades 7-12 in the United States during the 1994-95 school year. Public data on about 21,000 people first surveyed in 1994 are available on the first phases of the study, as well as study design specifications. It also includes some parent and biomarker data. The Add Health cohort has been followed into young adulthood with four in-home interviews, the most recent in 2008, when the sample was aged 24-32. Add Health combines longitudinal survey data on respondents social, economic, psychological and physical well-being with contextual data on the family, neighborhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. The fourth wave of interviews expanded the collection of biological data in Add Health to understand the social, behavioral, and biological linkages in health trajectories as the Add Health cohort ages through adulthood. The restricted-use contract includes four hours of free consultation with appropriate staff; after that, there''s a fee for help. Researchers can also share information through a listserv devoted to the database.

Proper citation: Add Health (National Longitudinal Study of Adolescent Health) (RRID:SCR_007434) Copy   

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http://mummer.sourceforge.net/

Software package as system for rapidly aligning entire genomes. Alignment tool for DNA and protein sequences. Can align incomplete genomes.

Proper citation: MUMmer (RRID:SCR_018171) Copy   

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http://www.cbs.dtu.dk/services/NetMHCpan/

Web server for quantitative prediction of peptide binding to any MHC molecule of known sequence using artificial neural networks. Characterizes binding specificity of given major histocompatibility complex molecule and predicts peptide length profile and peptide binding affinity. NetMHCpan 3.0 is improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length data sets. NetMHCpan 4.0 is trained on naturally eluted ligands and on peptide binding affinity data. NetMHCpan-4.1 server predicts binding of peptides to any MHC molecule of known sequence using artificial neural networks (ANNs).

Proper citation: NetMHCpan Server (RRID:SCR_018182) Copy   

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http://web.cbio.uct.ac.za/~darren/rdp.html

Software package to analyse nucleotide sequence data and identify evidence of genetic recombination. RDP3 is version of RDP program for characterizing recombination events in DNA-sequence alignments. RDP4 is version of RDP program for detection and analysis of recombination patterns in virus genomes.

Proper citation: Recombination Detection Program (RRID:SCR_018537) Copy   

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https://bioweb.pasteur.fr/packages/pack@[email protected]

Open source software tool for analysing trace files generated by Bayesian MCMC runs. Software package for visualising and analysing MCMC trace files generated through Bayesian phylogenetic inference. Provides kernel density estimation, multivariate visualisation, demographic trajectory reconstruction, conditional posterior distribution summary and more.

Proper citation: Tracer (RRID:SCR_019121) Copy   

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https://github.com/datatagsuite

Software suite to enable discoverability of datasets. Enables submission of metadata on datasets to DataMed. Has core set of elements, which are generic and applicable to any type of dataset, and extended set that can accommodate more specialized data types. Platform independent model developed by NIH BD2K bioCADDIE project for DataMed Data Discovery Index prototype being developed. Also available as annotated serialization in schema.org, which in turn is widely used by major search engines like Google, Microsoft, Yahoo and Yandex.

Proper citation: DatA Tag Suite (RRID:SCR_019236) Copy   

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http://www.sb.cs.cmu.edu/drem

The Dynamic Regulatory Events Miner (DREM) allows one to model, analyze, and visualize transcriptional gene regulation dynamics. The method of DREM takes as input time series gene expression data and static transcription factor-gene interaction data (e.g. ChIP-chip data), and produces as output a dynamic regulatory map. The dynamic regulatory map highlights major bifurcation events in the time series expression data and transcription factors potentially responsible for them. DREM 2.0 was released and supports a number of new features including: * new static binding data for mouse, human, D. melanogaster, A. thaliana * a new and more flexible implementation of the IOHMM supports dynamic binding data for each time point or as a mix of static/dynamic TF input * expression levels of TFs can be used to improve the models learned by DREM * the motif finder DECOD can be used in conjuction with DREM and help find DNA motifs for unannotated splits * new features for the visualization of expressed TFs, dragging boxes in the model view, and switching between representations

Proper citation: Dynamic Regulatory Events Miner (RRID:SCR_003080) Copy   

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http://www.cs.cmu.edu/~jernst/stem/

The Short Time-series Expression Miner (STEM) is a Java program for clustering, comparing, and visualizing short time series gene expression data from microarray experiments (~8 time points or fewer). STEM allows researchers to identify significant temporal expression profiles and the genes associated with these profiles and to compare the behavior of these genes across multiple conditions. STEM is fully integrated with the Gene Ontology (GO) database supporting GO category gene enrichment analyses for sets of genes having the same temporal expression pattern. STEM also supports the ability to easily determine and visualize the behavior of genes belonging to a given GO category or user defined gene set, identifying which temporal expression profiles were enriched for these genes. (Note: While STEM is designed primarily to analyze data from short time course experiments it can be used to analyze data from any small set of experiments which can naturally be ordered sequentially including dose response experiments.) Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Short Time-series Expression Miner (STEM) (RRID:SCR_005016) Copy   

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http://ontodog.hegroup.org/index.php

Ontodog is a web-based ontology view generator. It can generate inSubset annotation ontology, user preferred label annotation ontology and subset of source ontology. Simply provide Ontodog input term file (Microsoft Excel file or tab-delimited text file), select one source ontology or enter your own source ontology and SPARQL endpoint, then set the settings for Ontodog output files and get the OWL (RDF/XML) Output files. Ontodog performs the basic ontology modularization-like function, i.e.,it automatically extracts all axioms and related terms associated with user-specified signature term(s). In addition, Ontodog includes extra features: (1) extracting all instance data associated with the retrieved class terms and annotations; and (2) recursively extracting all axioms and related terms indirectly associated with signature terms. More features are being added to Ontodog, such as relabeling preferred names for various ontology terms to fit in with the needs from a specific community. The Ontodog input data requires a source ontology and a list of user-specified signature terms in tab-delimited format. Ontodog provides the template files for generating the signature terms as the input terms file to download. There are several output options that the users can choose based on their needs. With more and more ontologies being developed, Ontodog offers a timely web-based package of solutions for ontology view generation. Ontodog provides an efficient approach to promote ontology sharing and interoperability. It is easy to use and does not require knowledge of SPARQL, script programming, and command line operation. Ontodog is developed to serve the ontology community for ontology reuse. It is freely available under the Apache License 2.0. The source code is made available under Apache License 2.0.

Proper citation: Ontodog: A Web-based Ontology View Generator (RRID:SCR_005061) Copy   

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https://www.immunespace.org/

A consortium of university groups to characterize human immune populations. The Human Immunology Project Consortium (HIPC) program, established in 2010 by the NIAID Division of Allergy, Immunology, and Transplantation, is a major collaborative effort that is generating large amounts of cross-center and cross-assay data including high-dimensional data to characterize the status of the immune system in diverse populations under both normal conditions and in response to stimuli. This large data problem has given birth to ImmuneSpace, a powerful data management and analysis engine where datasets can be easily explored and analyzed using state-of-the-art computational tools.

Proper citation: ImmuneSpace (RRID:SCR_010508) Copy   

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http://david.abcc.ncifcrf.gov/content.jsp?file=/ease/ease1.htm&type=1

Windows(c) desktop software application, customizable and standalone, that facilitates the biological interpretation of gene lists derived from the results of microarray, proteomic, and SAGE experiments. Provides statistical methods for discovering enriched biological themes within gene lists, generates gene annotation tables, and enables automated linking to online analysis tools. Offers statistical models to deal with multi-test comparison problem. Platform: Windows compatible

Proper citation: EASE: the Expression Analysis Systematic Explorer (RRID:SCR_013361) Copy   

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https://github.com/JCVenterInstitute/NSForest/releases

Software tool as method that takes cluster results from single cell nuclei RNAseq experiments and generates lists of minimal markers needed to define each cell type cluster. Utilizes random forest of decision trees machine learning approach. Used to determine minimum set of marker genes whose combined expression identified cells of given type with maximum classification accuracy.

Proper citation: NS-Forest (RRID:SCR_018348) Copy   

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https://github.com/marbl/salsa

Software tool for scaffold long read assemblies with Hi-C data.

Proper citation: SALSA (RRID:SCR_022013) Copy   

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https://github.com/wyp1125/MCScanx

Software toolkit for detection and evolutionary analysis of gene synteny and collinearity.

Proper citation: MCScanX (RRID:SCR_022067) Copy   

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https://github.com/JamieHeather/stitchr

Software Python tool for stitching coding T cell receptors nucleotide sequences from V,J,CDR3 information. Produces complete coding sequences representing fully spliced TCR cDNA given minimal V,J,CDR3 information.

Proper citation: Stitchr (RRID:SCR_022139) Copy   

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https://github.com/immunogenomics/harmony

Software R package to project cells into shared embedding in which cells group by cell type rather than dataset specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. Used for integration of single cell data.

Proper citation: Harmony (RRID:SCR_022206) Copy   

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http://www.nsrrc.missouri.edu/

Provides access to critically needed swine models of human health and disease as well as a central resource for reagents, creation of new genetically modified swine, and information and training related to use of swine models in biomedical research.

Proper citation: National Swine Resource and Research Center (RRID:SCR_006855) Copy   

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https://rdrr.io/cran/DrInsight/src/R/drug.identification.R

Software connectivity mapping based drug repurposing tool that identifies drugs that can potentially reverse query disease phenotype or have similar functions with query drugs.

Proper citation: DrInsight (RRID:SCR_023871) Copy   

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