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http://sig.biostr.washington.edu/projects/fm/
A domain ontology that represents a coherent body of explicit declarative knowledge about human anatomy. It is concerned with the representation of classes or types and relationships necessary for the symbolic representation of the phenotypic structure of the human body in a form that is understandable to humans and is also navigable, parseable and interpretable by machine-based systems. Its ontological framework can be applied and extended to all other species. The description of how the OWL version was generated is in Pushing the Envelope: Challenges in a Frame-Based Representation of Human Anatomy by N. F. Noy, J. L. Mejino, C. Rosse, M. A. Musen: http://bmir.stanford.edu/publications/view.php/pushing_the_envelope_challenges_in_a_frame_based_representation_of_human_anatomy The Foundational Model of Anatomy ontology has four interrelated components: # Anatomy taxonomy (At), # Anatomical Structural Abstraction (ASA), # Anatomical Transformation Abstraction (ATA), # Metaknowledge (Mk), The ontology contains approximately 75,000 classes and over 120,000 terms; over 2.1 million relationship instances from over 168 relationship types link the FMA's classes into a coherent symbolic model.
Proper citation: FMA (RRID:SCR_003379) Copy
https://medicine.yale.edu/keck/nida/yped/
Open source system for storage, retrieval, and integrated analysis of large amounts of data from high throughput proteomic technologies. YPED currently handles LCMS, MudPIT, ICAT, iTRAQ, SILAC, 2D Gel and DIGE. The repository contains data sets which have been released for public viewing and downloading by the responsible Primary Investigators. It includes proteomic data generated by the Yale NIDA Neuroproteomics Center (http://medicine.yale.edu/keck/nida/index.aspx). Sample descriptions are compatible with the evolving MIAPE standards.
Proper citation: YPED (RRID:SCR_001436) Copy
Trans-NIH program encouraging and facilitating the study of the underlying mechanisms controlling blood vessel growth and development. Other aims include: to identify specific targets and to develop therapeutics against pathologic angiogenesis in order to reduce the morbidity due to abnormal blood vessel proliferation in a variety of disease states; to better understand the process of angiogenesis and vascularization to improve states of decreased vascularization; to encourage and facilitate the study of the processes of lymphangiogenesis; and to achieve these goals through a multidisciplinary approach, bringing together investigators with varied backgrounds and varied interests.
Proper citation: Trans-Institute Angiogenesis Research Program (RRID:SCR_000384) Copy
http://www.bumc.bu.edu/cardiovascularproteomics/
The Cardiovascular Proteomics Center is a research center funded by the NIH/NHLBI to analyze and identify proteins that may be modified or created by oxidative stress. The CPC is developing and applying new proteomics methodology and instrumentation to the analysis of known proteins and those yet to be discovered.
Proper citation: Cardiovascular Proteomics Center (RRID:SCR_000603) Copy
http://interactome.baderlab.org/
Project portal for the Human Reference Protein Interactome Project, which aims generate a first reference map of the human protein-protein interactome network by identifying binary protein-protein interactions (PPIs). It achieves this by systematically interrogating all pairwise combinations of predicted human protein-coding genes using proteome-scale technologies.
Proper citation: Human Reference Protein Interactome Project (RRID:SCR_015670) Copy
https://software.broadinstitute.org/cancer/cga/polysolver
Software tool for HLA typing based on whole exome sequencing data and infers alleles for three major MHC class I genes. Enables accurate inference of germline alleles of class I HLA-A, B and C genes and subsequent detection of mutations in these genes using inferred alleles as reference.
Proper citation: Polysolver (RRID:SCR_022278) Copy
https://maayanlab.cloud/sigcom-lincs
Web server that serves over million gene expression signatures processed, analyzed, and visualized from LINCS, GTEx, and GEO. Data and metadata search engine for gene expression signatures.
Proper citation: SigCom LINCS (RRID:SCR_022275) Copy
https://github.com/FunctionLab/sei-framework
Web server for systematically predicting sequence regulatory activities and applying sequence information to human genetics data. Provides global map from any sequence to regulatory activities, as represented by sequence classes, and each sequence class integrates predictions for chromatin profiles like transcription factor, histone marks, and chromatin accessibility profiles across wide range of cell types.
Proper citation: sei (RRID:SCR_022571) Copy
https://appyters.maayanlab.cloud
Collection of web-based software applications that enable users to execute bioinformatics workflows without coding. Turns Jupyter notebooks into fully functional standalone web-based bioinformatics applications. Each Appyter application introduces data entry form for uploading or fetching data, as well as for selecting options for various settings. Once user presses Submit, Appyter is executed in cloud and user is presented with Jupyter Notebook report that contain results. Report includes markdown text, interactive and static figures, and source code. Appyter users can share the link to the output report, as well as download the fully executable notebook for execution on other platforms.
Proper citation: Appyters (RRID:SCR_021245) Copy
https://sites.cscc.unc.edu/aric/
Platform for prospective epidemiologic study conducted in four U.S. communities. One of most significant and longest running heart health studies and is the largest study of heart health in African Americans. ARIC investigates risk factors for heart disease and stroke, and connections between cardiovascular and cognitive health. ARIC includes two parts: Cohort Component and Community Surveillance Component. Cohort Component began in 1987, and each ARIC field center randomly selected and recruited cohort sample of individuals aged 45-64 from defined population in their community, to receive extensive examinations, including medical, social, and demographic data. In Community Surveillance Component, four communities are investigated to determine long term trends in hospitalized myocardial infarction and coronary heart disease deaths in men and women aged 35-84 years.
Proper citation: Atherosclerosis Risk in Communities (RRID:SCR_021769) Copy
https://github.com/vlink/marge
Software package that integrates genome wide genetic variation with epigenetic data to identify collaborative transcription factor pairs. Optimized to work with chromatin accessibility assays such as ATAC-seq or DNase I hypersensitivity, as well as transcription factor binding data collected by ChIP-seq. Used to identify combinations of cell type specific transcription factors while simultaneously interpreting functional effects of non-coding genetic variation.
Proper citation: Motif Mutation Analysis for Regulatory Genomic Elements (RRID:SCR_021902) Copy
Collection of data of protein sequence and functional information. Resource for protein sequence and annotation data. Consortium for preservation of the UniProt databases: UniProt Knowledgebase (UniProtKB), UniProt Reference Clusters (UniRef), and UniProt Archive (UniParc), UniProt Proteomes. Collaboration between European Bioinformatics Institute (EMBL-EBI), SIB Swiss Institute of Bioinformatics and Protein Information Resource. Swiss-Prot is a curated subset of UniProtKB.
Proper citation: UniProt (RRID:SCR_002380) Copy
Database of polymorphisms and mutations of the human mitochondrial DNA. It reports published and unpublished data on human mitochondrial DNA variation. All data is curated by hand. If you would like to submit published articles to be included in mitomap, please send them the citation and a pdf.
Proper citation: MITOMAP - A human mitochondrial genome database (RRID:SCR_002996) Copy
http://life.ccs.miami.edu/life/
LIFE search engine contains data generated from LINCS Pilot Phase, to integrate LINCS content leveraging semantic knowledge model and common LINCS metadata standards. LIFE makes LINCS content discoverable and includes aggregate results linked to Harvard Medical School and Broad Institute and other LINCS centers, who provide more information including experimental conditions and raw data. Please visit LINCS Data Portal.
Proper citation: LINCS Information Framework (RRID:SCR_003937) Copy
https://maayanlab.cloud/drugmonizome/#/
Database with search engine for querying annotated sets of drugs and small molecules for performing drug set enrichment analysis.
Proper citation: Drugmonizome (RRID:SCR_024821) Copy
Web interactive browser to visualize data and perform gene set enrichment analysis along with gene and SNP lookup. Web interface used to query STARNET datasets and downstream analysis which includes RNAseq from 7 tissues: blood, free internal mammary artery (MAM), atherosclerotic aortic root (AOR), subcutaneous fat (SF), visceral abdominal fat (VAF), skeletal muscle (SKLM), and liver (LIV). Paired SNP genotyping data is included and utilized for tissue expression quantitative trait loci (eQTL), CAD heritability (H2), co-expression networks and gene regulatory networks.
Proper citation: STARNET (RRID:SCR_025238) Copy
Software quality assurance and checking tool for quantitative assessment of magnetic resonance imaging and computed tomography data. Used for quality control of MR imaging data.
Proper citation: MRQy (RRID:SCR_025779) Copy
https://pypi.org/project/SpaGCN/
Software graph convolutional network to integrate gene expression and histology to identify spatial domains and spatially variable genes. SpaGCN integrates information from gene.
Proper citation: SpaGCN (RRID:SCR_025978) Copy
https://pypi.org/project/pmlb/
Python wrapper for Penn Machine Learning Benchmark data repository. Large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms. Part of PyPI https://pypi.org/
Proper citation: Penn machine learning benchmark repository (RRID:SCR_017138) Copy
A tool for finding dbGaP studies containing phenotype variables of interest. Lack of standardization makes locating and categorizing previously measured variables difficult. This query tool for biomedical researchers is to identify studies and phenotype variables of interest.
Proper citation: PhenoExplorer (RRID:SCR_008980) Copy
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