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http://lincsportal.ccs.miami.edu/dcic-portal/

Portal which provides a unified interface for searching LINCS dataset packages and reagents. Users can use the portal to access datasets, small molecules, cells, genes, proteins and peptides, and antibodies.

Proper citation: LINCS Data Portal (RRID:SCR_014939) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://proteomics.ucsd.edu/Software/NeuroPedia/index.html

A neuropeptide encyclopedia of peptide sequences (including genomic and taxonomic information) and spectral libraries of identified MS/MS spectra of homolog neuropeptides from multiple species.

Proper citation: NeuroPedia (RRID:SCR_001551) Copy   

•   Source: SciCrunch Registry

http://www.uniprot.org/

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   

•   Source: SciCrunch Registry

http://www.mitomap.org/

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 It reports published and unpublished data on human mitochondrial DNA variation.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   

•   Source: SciCrunch Registry

https://lincsportal.ccs.miami.edu/signatures/home

Primary access point for compendium of LINCS data with substantial changes in data architecture and APIs, completely redesigned user interface, and enhanced curated metadata annotations to support more advanced, intuitive and deeper querying, exploration and analysis capabilities. LINCS datasets are accessible at data point level enabling users to directly access and download any subset of signatures across entire library independent from originating source, project or assay. Newly designed query interface enables global metadata search with autosuggest across all annotations associated with perturbations, model systems, and signatures.

Proper citation: LINCS Data Portal 2.0 (RRID:SCR_022566) Copy   

•   Source: SciCrunch Registry

http://evs.gs.washington.edu/EVS/

The goal of the project is to discover novel genes and mechanisms contributing to heart, lung and blood disorders by pioneering the application of next-generation sequencing of the protein coding regions of the human genome across diverse, richly-phenotyped populations and to share these datasets and findings with the scientific community to extend and enrich the diagnosis, management and treatment of heart, lung and blood disorders. The groups participating and collaborating in the NHLBI GO ESP include: Seattle GO - University of Washington, Seattle, WA Broad GO - Broad Institute of MIT and Harvard, Cambridge, MA WHISP GO - Ohio State University Medical Center, Columbus, OH Lung GO - University of Washington, Seattle, WA WashU GO - Washington University, St. Louis, MO Heart GO - University of Virginia Health System, Charlottesville, VA ChargeS GO - University of Texas Health Sciences Center at Houston

Proper citation: NHLBI Exome Sequencing Project (ESP) (RRID:SCR_012761) Copy   

•   Source: SciCrunch Registry

https://www.accordtrial.org/public

Study testing whether strict glucose control lowers the risk of heart disease and stroke in adults with type 2 diabetes. In addition the study is exploring: 1) Whether in the context of good glycemic control the use of different lowering lipid drugs will further improve these outcomes and 2) If strict control of blood pressure will also have additional beneficial effects on reducing cardiovascular disease. The design was a randomized, multicenter, double 2 X 2 factorial trial in 10,251 patients with type 2 diabetes mellitus. It was designed to test the effects on major CVD events of intensive glycemia control, of fibrate treatment to increase HDL-cholesterol and lower triglycerides (in the context of good LDL-C and glycemia control), and of intensive blood pressure control (in the context of good glycemia control), each compared to an appropriate control. All 10,251 participants were in an overarching glycemia trial. In addition, one 2 X 2 trial addressed the lipid question in 5,518 of the participants and the other 2 X 2 trial addressed the blood pressure question in 4,733 of the participants. The glycemia trial was terminated early due to higher mortality in the intensive compared with the standard glycemia treatment strategies. The results were published in June 2008 (N Eng J Med 2008;358:2545-59). Study-delivered treatment for all ACCORD participants was stopped on June 30, 2009, and the participants were assisted as needed in transferring their care to a personal physician. The lipid and blood pressure results (as well as the microvascular outcomes and eye substudy results) were published in 2010. All participants are continuing to be followed in a non-treatment observational study.

Proper citation: ACCORD (RRID:SCR_009015) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

https://github.com/BioDepot/nbdocker

Software tool as Jupyter Notebook extension for Docker. Each Docker container encapsulates its individual computing environment to allow different programming languages and computing environments to be included in one single notebook, provides user to document code as well as computing environment.

Proper citation: nbdocker (RRID:SCR_017159) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

https://github.com/aametwally/Metabolic_Subphenotype_Predictor

Software repository contains code for Inference of T2D metabolic subphenotypes (MuscleIR, Beta-cell Function, Incretin Effect, Hepatic IR), Identification of dominant metabolic subphenotype, Feature extraction from glucose tiemseries, Extraction of reduced representation of glucose tiemseries,Visualization of metabolic phenotypes based on various glucose-related metrics,Concordance between CGM and Venous glucose values from at home and at clinical setting, Classification of metabolic subphenotypes.

Proper citation: Metabolic Subphenotype Predictor (RRID:SCR_027192) Copy   

•   Source: SciCrunch Registry

https://github.com/EpistasisLab/ReBATE

Open source software Python package to compare relief based feature selection algorithms used in data mining. Used for feature selection in any bioinformatics problem with potentially predictive features and target outcome variable, to detect feature interactions without examination of all feature combinations, to detect features involved in heterogeneous patterns of association such as genetic heterogeneity .

Proper citation: ReBATE (RRID:SCR_017139) Copy   

•   Source: SciCrunch Registry

https://github.com/xu-lab/SINCERA

Software tool implemented in R S4 as an analytic pipeline for processing single-cell RNA-seq data from a whole organ or sorted cells. Used for Single Cell RNA-Seq profiling analysis.

Proper citation: SINCERA Pipeline (RRID:SCR_016563) Copy   

•   Source: SciCrunch Registry

https://bioconductor.org/packages/variancePartition/

Software R package to quantify and interpret divers of variation in multilevel gene expression experiments.Provides statistical and visualization framework for studying drivers of variation in RNA-seq datasets in many types of high throughput genomic assays including RNA-seq gene-, exon- and isoform-level quantification, splicing efficiency, protein quantification, metabolite quantification, metagenomic assays, methylation arrays and epigenomic sequencing assays.

Proper citation: variancePartition (RRID:SCR_019204) Copy   

•   Source: SciCrunch Registry

http://nsr.bioeng.washington.edu/

Database of physiological, pharmacological, and pathological information on humans and other organisms and integration through computational modeling. Models include everything from diagrammatic schema, suggesting relationships among elements composing a system, to fully quantitative, computational models describing the behavior of physiological systems and an organism''s response to environmental change. Each mathematical model is an internally self-consistent summary of available information, and thereby defines a working hypothesis about how a system operates. Predictions from such models are subject to test, with new results leading to new models.BR /> A Tool developed for the NSR Physiome project is JSim, an open source, free software. JSim is a Java-based simulation system for building quantitative numeric models and analyzing them with respect to experimental reference data. JSim''s primary focus is in physiology and biomedicine, however its computational engine is quite general and applicable to a wide range of scientific domains. JSim models may intermix ODEs, PDEs, implicit equations, integrals, summations, discrete events and procedural code as appropriate. JSim''s model compiler can automatically insert conversion factors for compatible physical units as well as detect and reject unit unbalanced equations. JSim also imports the SBML and CellML model archival formats. All JSim models are open source. Goals of the Physiome Project: - To develop and database observations of physiological phenomenon and interpret these in terms of mechanism (a fundamentally reductionist goal). - To integrate experimental information into quantitative descriptions of the functioning of humans and other organisms (modern integrative biology glued together via modeling). - To disseminate experimental data and integrative models for teaching and research. - To foster collaboration amongst investigators worldwide, to speed up the discovery of how biological systems work. - To determine the most effective targets (molecules or systems) for therapy, either pharmaceutic or genomic. - To provide information for the design of tissue-engineered, biocompatible implants.

Proper citation: NSR Physiome Project (RRID:SCR_007379) Copy   

•   Source: SciCrunch Registry

http://enhancer.lbl.gov/

Resource for experimentally validated human and mouse noncoding fragments with gene enhancer activity as assessed in transgenic mice. Most of these noncoding elements were selected for testing based on their extreme conservation in other vertebrates or epigenomic evidence (ChIP-Seq) of putative enhancer marks. Central public database of experimentally validated human and mouse noncoding fragments with gene enhancer activity as assessed in transgenic mice. Users can retrieve elements near single genes of interest, search for enhancers that target reporter gene expression to particular tissue, or download entire collections of enhancers with defined tissue specificity or conservation depth.

Proper citation: VISTA Enhancer Browser (RRID:SCR_007973) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

https://pmc.ncbi.nlm.nih.gov/articles/PMC4525701/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on July 25,2025. Web tool to parse Sanger sequencing chromatograms with double peaks into wildtype and alternative allele sequences. Used to separate chromatogram data containing ambiguous base calls into wildtype and mutant allele sequences.Used for identification of unknown indels using sanger sequencing of polymerase chain reaction products.

Proper citation: Poly Peak Parser (RRID:SCR_023776) Copy   

•   Source: SciCrunch Registry