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

Database containing descriptions of biological source materials used in experimental assays. Sources include: GenBank, Sequence Read Archive (SRA), Coriell, ATCC. Submissions are supported by a web-based Submission Portal that guides users through a series of forms for input of rich metadata describing their samples. As the capacity and complexity of biological data sets expands, databases face new challenges in ensuring that the information is adequately organized and described. The NCBI BioSample database is being developed to help address the challenges by providing the means by which data generators can organize and describe a broad range of sample types, and link to corresponding sets of experimental data in archival databases.

Proper citation: NCBI BioSample (RRID:SCR_004854) Copy   

•   Source: SciCrunch Registry

http://www.benoslab.pitt.edu/comir/

Data analysis service that predicts whether a given mRNA is targeted by a set of miRNAs. ComiR uses miRNA expression to improve and combine multiple miRNA targets for each of the four prediction algorithms: miRanda, PITA, TargetScan and mirSVR. The composite scores of the four algorithms are then combined using a support vector machine trained on Drosophila Ago1 IP data.

Proper citation: ComiR (RRID:SCR_013023) Copy   

•   Source: SciCrunch Registry

https://www.robotreviewer.net/

Software tool as machine learning system that automatically assesses bias in clinical trials. From PDF formatted trial report determines risks of bias for domains defined by Cochrane Risk of Bias (RoB) tool, and extracts supporting text for these judgments.

Proper citation: Robot Reviewer (RRID:SCR_018961) Copy   

•   Source: SciCrunch Registry

https://simpleitk.org/

Open source software library for multi dimensional image analysis in Python, R, Java, C#, Lua, Ruby, TCL and C++. New interface to Insight Segmentation and Registration Toolkit (ITK) designed to facilitate rapid prototyping, education and scientific activities via high level programming languages. Provides easy to use and simplified interface to ITK's algorithms.

Proper citation: SimpleITK (RRID:SCR_024693) Copy   

•   Source: SciCrunch Registry

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

A database of organized information related to human medical genetics, such as attributes of conditions with a genetic contribution.

Proper citation: MedGen (RRID:SCR_000111) Copy   

•   Source: SciCrunch Registry

http://compbio.dfci.harvard.edu/amp/

THIS RESOURCE IS NO LONGER IN SERVICE, documented November 4, 2015. Web application based on the TM4 Microarray Software Suite to provide a means of normalization and analysis of microarray data. Users can upload data in the form of Affymetrix CEL files, and define an analysis pipeline by selecting several intuitive options. It performs data normalization (eg RMA), basic statistical analysis (eg t-test, ANOVA), and analysis of annotation using gene classification (eg Gene Ontology term assignment). The analysis are performed without user intervention and the results are presented in a web-based summary that allows data to be downloaded in a variety of formats compatible with further directed analysis.

Proper citation: Automated Microarray Pipeline (RRID:SCR_001219) Copy   

•   Source: SciCrunch Registry

http://atgc.lbl.gov/atgc/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 23,2022. ATGC stands for Alignable Tight Genomic Cluster, which is cluster of closely related prokaryotic genomes. ATGC is the principal notion of this web resource. The purpose of this web resource is to prepare ATGC-derived data sets for a variety of research projects in functional and evolutionary genomics. Unique features of ATGC include: * Reliable identification of orthologs (high degree of similarity between the genomes in the set allow an extensive use of synteny in ortholog identification); * Fine granularity of protein classification (in comparisons of more distant genomes, proteins belonging to families of paralogs are often lumped into a singlegroup; under the ATGC approach, comparison of genomic sequences from highly similar genomes allows one to track each set of orthologs separately); * Relative rarity of changes of any kind (in sequence, genome organization and gene content) allows the use of parsimony-related methods of analysis.

Proper citation: Alignable Tight Genomic Cluster (RRID:SCR_001894) Copy   

•   Source: SciCrunch Registry

https://glimmpse.samplesizeshop.org/#/

Web based software tool that calculates power and sample size for study designs with normally distributed outcomes. Permits power calculations for clinical trials, randomized experiments, and observational studies with clustering, repeated measures, and both, and almost any testable hypothesis. GLIMMPSE Version 3 release back end has been refactored in Python, interface has been simplified, requiring user decisions about only one topic per screen, new menu improves specification of both between-participant and within-participant hypothese, recursive algorithm permits computing covariances for up to ten levels of clustering., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: GLIMMPSE (RRID:SCR_016297) Copy   

•   Source: SciCrunch Registry

http://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/AnneOTate.cgi

Web search tool to gain overview of set of articles retrieved by PubMed query. Used to support user driven summarization, drill down and browsing of PubMed search results. Value-added PubMed search engine for analysis and text mining.

Proper citation: Anne O'Tate (RRID:SCR_023086) Copy   

•   Source: SciCrunch Registry

https://gillisweb.cshl.edu/Primate_MTG_coexp/

We aligned single-nucleus atlases of middle temporal gyrus (MTG) of 5 primates (human, chimp, gorilla, macaque and marmoset) and identified 57 consensus cell types common to all species. We provide this resource for users to: 1) explore conservation of gene expression across primates at single cell resolution; 2) compare with conservation of gene coexpression across metazoa, and 3) identify genes with changes in expression or connectivity that drive rapid evolution of human brain.

Proper citation: Gene functional conservation across cell types and species (RRID:SCR_023292) 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

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

http://snyderome.stanford.edu/

Data set generated by personal omics profiling of Dr. Michael Snyder at Stanford University. It combines genomic, transcriptomic, proteomic, metabolomic, and autoantibody profiles from a single individual over a 14 month period. The analysis revealed various medical risks, including type II diabetes. It also uncovered extensive, dynamic changes in diverse molecular components and biological pathways across healthy and diseased conditions.

Proper citation: iPOP (RRID:SCR_008991) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://www.nlm.nih.gov/research/umls/

Database of key terminology, classification and coding standards, and associated resources to promote creation of more effective and interoperable biomedical information systems and services, including electronic health records. This set of files and software brings together many health and biomedical vocabularies and standards to enable interoperability between computer systems. Users can use the UMLS to enhance or develop applications, such as electronic health records, classification tools, dictionaries and language translators. The UMLS has three tools, which we call the Knowledge Sources: * Metathesaurus: Terms and codes from many vocabularies, including CPT, ICD-10-CM, LOINC, MeSH, RxNorm, and SNOMED CT * Semantic Network: Broad categories (semantic types) and their relationships (semantic relations) * SPECIALIST Lexicon and Lexical Tools: Natural language processing tools We use the Semantic Network and Lexical Tools to produce the Metathesaurus. Metathesaurus production involves: * Processing the terms and codes using the Lexical Tools * Grouping synonymous terms into concepts * Categorizing concepts by semantic types from the Semantic Network * Incorporating relationships and attributes provided by vocabularies * Releasing the data in a common format Although we integrate these tools for Metathesaurus production, you can access them separately or in any combination according to your needs. The UMLS Terminology Services (UTS) provides three ways to access the UMLS: Web Browsers, Local Installation, and Web Services APIs.

Proper citation: Unified Medical Language System (RRID:SCR_006363) Copy   

•   Source: SciCrunch Registry

https://evidencemodeler.github.io/

Software tool for automated eukaryotic gene structure annotation that reports eukaryotic gene structures as weighted consensus of all available evidence. Used to combine ab intio gene predictions and protein and transcript alignments into weighted consensus gene structures. Inputs include genome sequence, gene predictions, and alignment data (in GFF3 format).

Proper citation: EVidenceModeler (RRID:SCR_014659) Copy   

•   Source: SciCrunch Registry

https://www.robotreviewer.net/about

Open source web based system that uses machine learning and NLP to semi automate biomedical evidence synthesis, to aid practice of Evidence Based Medicine. Processes full text journal articles describing randomized controlled trials. Designed to automatically extract key data items from reports of clinical trials.

Proper citation: RobotReviewer (RRID:SCR_021064) Copy   

•   Source: SciCrunch Registry

https://www.ccpn.ac.uk/v2-software/software/extras/datamodelfolder

Model to cover data for macromolecular NMR spectroscopy from the initial experimental data to the final validation. Used for the large scale data deposition, data mining and program interoperability. Enables movement from one software package to another without difficulties with data conversion or loss of information. Works with CcpNmr Analysis software for analysis and interactive display, CcpNmr FormatConverter for allowing transfer of data from programs used in NMR to and from the Data Model, and the CLOUDS software for automated structure calculation and assignment. Used within the CCPN software suite for NMR spectroscopy and at the BioMagResBank for converting existing deposited restraint lists to a standard IUPAC nomenclature.

Proper citation: CCPN Data Model (RRID:SCR_016982) Copy   

•   Source: SciCrunch Registry

http://www.iu.edu/~beca/

Visualization and analysis software for interactive visual exploration and mining of fiber-tracts and brain networks with their genetic determinants and functional outcomes. BECA includes an fMRI and Diseases Analysis version as well as a Genome Explorer version.

Proper citation: BECA (RRID:SCR_015846) Copy   

•   Source: SciCrunch Registry

https://github.com/nlm-irp-jianglab/SpikeHunter

Software deep learning tool for identifying phage tailspike proteins. Used to identify phage tailspike proteins.

Proper citation: SpikeHunter (RRID:SCR_024831) Copy   

•   Source: SciCrunch Registry