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https://openmm.org/

Software toolkit to run modern molecular simulations. It can be used either as a standalone application for running simulations, or as a library that enables accelerated calculations for molecular dynamics on high-performance computer architectures.

Proper citation: OpenMM (RRID:SCR_000436) Copy   

•   Source: SciCrunch Registry

http://ccr.coriell.org/Sections/Collections/NIGMS/?SsId=8

Highly characterized cell lines and high quality DNA for cell and genetic research representing a variety of disease states, chromosomal abnormalities, apparently healthy individuals and many distinct human populations. The NIGMS Repository contains more than 10,600 cell lines, primarily fibroblasts and transformed lymphoblasts, and over 5,500 DNA samples. The NIGMS Repository has a major emphasis on heritable diseases and chromosomally aberrant cell lines. In addition, it contains a large collection dedicated to understanding human variation that includes samples from populations around the world, the CEPH collection, the Polymorphism Discovery Resource, and many apparently healthy controls. Human induced pluripotent stem cell lines, many of which were derived from NIGMS Repository fibroblasts, have recently become available through the NIGMS Repository. Sample donation facilitates all areas of research by making available well-characterized materials to any qualified researcher who might have otherwise been unable to invest the time and resources to collect needed samples independently. Donations to the Repository have created a resource of unparalleled scope. Samples from the collection have been used in more than 5,500 publications and are distributed to scientists in more than 50 countries. This resource is continuously expanding to support new directions in human genetics.

Proper citation: NIGMS Human Genetic Cell Repository (RRID:SCR_004517) Copy   

•   Source: SciCrunch Registry

http://www.omicsdi.org/

Portal for dataset discovery across a heterogeneous, distributed group of transcriptomics, genomics, proteomics and metabolomics data resources. These resources span eight repositories in three continents and six organisations, including both open and controlled access data resources.

Proper citation: Omics Discovery Index (RRID:SCR_010494) Copy   

•   Source: SciCrunch Registry

https://www2.mrc-lmb.cam.ac.uk/groups/murshudov/content/balbes/balbes_layout.html

Software system for solving protein structures using x-ray crystallographic data. Automatic molecular replacement pipeline for molecular replacement in CCP4. Integrates into one system all components necessary for solving crystal structure by Molecular Replacement. System is automated so that it needs no user intervention when running combination of jobs such as model searching, molecular replacement and refinement.

Proper citation: BALBES (RRID:SCR_018763) Copy   

•   Source: SciCrunch Registry

https://github.com/RabadanLab/arcasHLA

Software tool for high resolution HLA typing from RNAseq. Fast and accurate in silico inference of HLA genotypes from RNA-seq.

Proper citation: arcasHLA (RRID:SCR_022286) Copy   

•   Source: SciCrunch Registry

https://www.utsouthwestern.edu/labs/danuser/software/

Software package as quantitative image analysis software for measurement of microtubule dynamics. MATLAB software for tracking full dynamics of microtubules based on plusTIP marker live cell image sequences.

Proper citation: plusTipTracker (RRID:SCR_021890) Copy   

•   Source: SciCrunch Registry

https://github.com/vpc-ccg/svict

Software tool for detecting structural variations from cell free DNA containing low dilutions of circulating tumor DNA.

Proper citation: SViCT (RRID:SCR_023656) Copy   

•   Source: SciCrunch Registry

https://www.crisprscan.org/

Web tool for predictive sgRNA-scoring that captures sequence features affecting Cas9/sgRNA activity in vivo. Scoring algorithm to help select the best gRNAs for CRISPR.

Proper citation: CRISPRscan (RRID:SCR_023777) Copy   

•   Source: SciCrunch Registry

https://pathvisio.org/

Software visualization tool for biological pathways. Pathway analysis and drawing software which allows drawing, editing, and analyzing biological pathways. Developed in Java and can be extended with plugins.

Proper citation: PathVisio (RRID:SCR_023789) Copy   

•   Source: SciCrunch Registry

https://github.com/katiasmirn/PERFect#perfect-permutation-filtering-package-in-r

Software R package as filtering test for microbiome data. Permutation filtering approach to address two unsolved problems in microbiome data processing: (i) define and quantify loss due to filtering by implementing thresholds and (ii) introduce and evaluate a permutation test for filtering loss to provide a measure of excessive filtering.

Proper citation: PERFect (RRID:SCR_024682) 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

https://gitlab.com/gernerlab/cytomap/-/wikis/home

Software tool as spatial analysis software for whole tissue sections.Utilizes information on cell type and position to phenotype local neighborhoods and reveal how their spatial distribution leads to generation of global tissue architecture.Used to make advanced data analytic techniques accessible for single cell data with position information.

Proper citation: CytoMAP (RRID:SCR_021227) Copy   

•   Source: SciCrunch Registry

https://github.com/CEGRcode/stencil

Web engine for visualizing and sharing life science datasets.Designed to organize, visualize, and enable sharing of interactive genomic data visualizations. Provides ability to inspect and interpret sequencing data, without requiring programming expertise.

Proper citation: STENCIL (RRID:SCR_021878) Copy   

•   Source: SciCrunch Registry

https://github.com/zdk123/SpiecEasi

Software R package for microbiome network analysis. Used for inference of microbial ecological networks from amplicon sequencing datasets. Combines data transformations developed for compositional data analysis with graphical model inference framework that assumes underlying ecological association network is sparse.

Proper citation: SpiecEasi (RRID:SCR_022712) Copy   

•   Source: SciCrunch Registry

https://zdock.umassmed.edu/

Web tool as protein docking server, based on rigid body docking programs ZDOCK and M-ZDOCK, to predict structures of protein-protein complexes and symmetric multimers.

Proper citation: ZDOCK Server (RRID:SCR_022518) Copy   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

http://virtualplant.bio.nyu.edu/cgi-bin/vpweb/

Software platform to support systems biology research. Integrates genomic data and provides visualization and analysis tools for exploration of genomic data. Provides tools to generate biological hypotheses.

Proper citation: VirtualPlant (RRID:SCR_022576) Copy   

•   Source: SciCrunch Registry

https://github.com/ChristopherWilks/megadepth

Software tool for quantifying alignments and coverage for BigWig and BAM/CRAM input files.Quantifies number of RNA-seq reads assigned to gene in BAM file, successor of bamcounts.

Proper citation: Megadepth (RRID:SCR_022779) Copy   

•   Source: SciCrunch Registry

http://www.evidenceontology.org

A controlled vocabulary that describes types of scientific evidence within the realm of biological research that can arise from laboratory experiments, computational methods, manual literature curation, and other means. Researchers can use these types of evidence to support assertions about research subjects that result from scientific research, such as scientific conclusions, gene annotations, or other statements of fact. ECO comprises two high-level classes, evidence and assertion method, where evidence is defined as a type of information that is used to support an assertion, and assertion method is defined as a means by which a statement is made about an entity. Together evidence and assertion method can be combined to describe both the support for an assertion and whether that assertion was made by a human being or a computer. However, ECO can not be used to make the assertion itself; for that, one would use another ontology, free text description, or other means. ECO was originally created around the year 2000 to support gene product annotation by the Gene Ontology. Today ECO is used by many groups concerned with provenance in scientific research. ECO is used in AmiGO 2

Proper citation: ECO (RRID:SCR_002477) Copy   

•   Source: SciCrunch Registry

http://publications.nigms.nih.gov/computinglife/

An NIGMS magazine that showcases the exciting ways that scientists are using the power of computers to expand our knowledge of biology and medicine. From text messaging friends to navigating city streets with GPS technology, we''re all living the computing life. But as we''ve upgraded from snail mail and compasses, so too have scientists. Computer advances now let researchers quickly search through DNA sequences to find gene variations that could lead to disease, simulate how flu might spread through your school and design three-dimensional animations of molecules that rival any video game. By teaming computers and biology, scientists can answer new and old questions that could offer insights into the fundamental processes that keep us alive and make us sick. This booklet introduces you to just some of the ways that physicists, biologists and even artists are computing life. Each section focuses on a different research problem, offers examples of current scientific projects and acquaints you with the people conducting the work. You can follow the links for online extras and other opportunities to learn aboutand get involved inthis exciting new interdisciplinary field.

Proper citation: NIGMS Computing Life (RRID:SCR_005850) Copy   

•   Source: SciCrunch Registry