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http://www.r-project.org/

Software environment and programming language for statistical computing and graphics. R is integrated suite of software facilities for data manipulation, calculation and graphical display. Can be extended via packages. Some packages are supplied with the R distribution and more are available through CRAN family.It compiles and runs on wide variety of UNIX platforms, Windows and MacOS.

Proper citation: R Project for Statistical Computing (RRID:SCR_001905) Copy   

•   Source: SciCrunch Registry

https://github.com/benedictpaten/pecan

A Java consistency based multiple sequence alignment software program.

Proper citation: Pecan (RRID:SCR_001909) Copy   

•   Source: SciCrunch Registry

http://qnl.bu.edu/obart

Tool that provides an interactive method to examine quantitative relationships between brain regions defined by different digital atlases or parcellation methods. Its current focus is for human brain imaging, though the techniques generalize to other domains. The method offers a quantitative answer to the nomenclature problem in neuroscience by comparing brain parts on the basis of their geometrical definitions rather than on the basis of name alone. Thus far these tools have been used to quantitatively compare eight distinct parcellations of the International Consortium for Brain Mapping (ICBM) single-subject template brain, each created using existing atlasing methods. This resources provides measures of global and regional similarity, and offers visualization techniques that allow users to quickly identify the correspondences (or lack of correspondences) between regions defined by different atlases.

Proper citation: OBART (RRID:SCR_001903) Copy   

•   Source: SciCrunch Registry

http://www.plantgdb.org/AtGDB/

Database providing a sequence-centered genome view for Arabidopsis thaliana, with a narrow focus on gene structure annotation. The current genome assembly displayed at AtGDB is version TAIR9. Annotated gene models are TAIR10. They have mapped the complete set of 176,915 publicly available Arabidopsis EST sequences onto the Arabidopsis genome using GeneSeqer, a spliced alignment program incorporating sequence similarity and splice site scoring. About 96% of the available ESTs could be properly aligned with a genomic locus, with the remaining ESTs deriving from organelle genomes and non-Arabidopsis sources or displaying insufficient sequence quality for alignment. The mapping provides verified sets of EST clusters for evaluation of EST clustering programs. Analysis of the spliced alignments suggests corrections to current gene structure annotation and provides examples of alternative and non-canonical pre-mRNA splicing.

Proper citation: Arabidopsis thaliana Genome Database (RRID:SCR_001901) Copy   

•   Source: SciCrunch Registry

http://www.wellcome.ac.uk/

The Wellcome Trust is the largest charity in the UK. We fund innovative biomedical research, in the UK and internationally, spending over 600 million each year to support the brightest scientists with the best ideas. The Wellcome Trust is an independent charity funding research to improve human and animal health. Established in 1936 and with an endowment of around 13 billion, it is the UK's largest non-governmental source of funds for biomedical research. What we do We spend over 600 million every year both in the UK and internationally achieving our mission. Funding We support many different kinds of research and activities with the ultimate aim of protecting and improving human and animal health. This support is not restricted to UK researchers - we devote significant funding to international research too. Biomedical science Our biomedical science funding enables the investigation of health and disease in humans and animals. This includes funding for scientists, clinicians and veterinarians at different career stages. Technology transfer Our technology transfer funding supports the development of innovative, early-stage projects with potential medical applications. Medical humanities Our medical humanities funding supports research into biomedical ethics and the history of medicine. Public engagement Our public engagement funding promotes interest, excitement and debate around science and society. Capital funding Our capital funding is for large-scale construction or refurbishment projects in the UK that support science, public engagement, medical history, or the activities of learned societies. Strategic awards Our Strategic Awards provide flexible funding that adds value to excellent research groups. Managing a grant This area contains information and resources to help you manage a grant once it has been awarded, from the grant-start certificate to the end-of-grant report and beyond. Education Resources Teaching and education Resources to help promote contemporary science in the curriculum and to enable young people to engage with biomedical science. Tree of Life Darwin200 Big Picture Science Learning Centres Scientific animations Creative Encounters Courses and conferences Trust-run conferences, courses and workshops for scientists, historians, ethicists, social scientists, teachers, healthcare professionals and policymakers, held in the UK and overseas. Advanced Courses Scientific conferences Conference centres Retreats History of medicine Biomedical ethics Biomedical resources Tools, databases and information to support different areas of biomedical research, including genomics, post-genomics and developmental biology. Animal research Genomics Model organisms Microorganisms Post-genomics Tissues Researcher support Support and advice for all kinds of engagement activities to help you communicate your work in the most effective and rewarding way possible. About researcher support National opportunities Regional opportunities Highlights Publications Browse a wealth of publications covering all aspects of the work we fund. Wellcome Trust websites Explore a range of sites covering key biomedical topics and our public engagement activities.

Proper citation: Welcome Trust (RRID:SCR_001852) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/flowClust.html

A Bioconductor software package for automated gating of flow cytometry data that implements a robust model-based clustering approach based on multivariate t mixture models with the Box-Cox transformation.

Proper citation: flowClust (RRID:SCR_001807) Copy   

•   Source: SciCrunch Registry

http://www.nesys.uio.no/Atlas3D/

A splice alignment software tool of RNA-Seq reads mapping.

Proper citation: HSA (RRID:SCR_001809) Copy   

•   Source: SciCrunch Registry

http://videocast.nih.gov/

VideoCasting of special NIH events, seminars, conferences, meetings and lectures available to viewers on the NIH network and the Internet from the VideoCast web site. VideoCasting is the method of electronically streaming digitally encoded video and audio data from a server to a client. VideoCast is often referred to as streaming video. Streaming files are not downloaded, but rather are broadcast in a manner similar to television broadcasts. The videos are processed by a compression program into a streaming format and delivered in a staggered fashion to minimize impact upon the network and maximize the experience of the content for the viewer. When users request a streaming file they will receive an initial burst of data after a short delay (file latency). While content is being viewed, the streaming server machine and software continues to stream data in such a manner that the viewer experiences no break in the content. CIT can broadcast your seminar, conference or meeting live to a world-wide audience over the Internet as a real-time streaming video. The event can be recorded and made available for viewers to watch at their convenience as an on-demand video or a downloadable podcast. CIT can also broadcast NIH-only or HHS-only content.

Proper citation: NIH VideoCasting (RRID:SCR_001885) Copy   

•   Source: SciCrunch Registry

http://astral.berkeley.edu/

It provides databases and tools useful for analyzing protein structures and their sequences. It is partially derived from, and augments the SCOP: Structural Classification of Proteins database, a database created by manual inspection and abetted by a battery of automated methods, aims to provide a detailed and comprehensive description of the structural and evolutionary relationships between all proteins whose structure is known. Most of the resources provided here depend upon the coordinate files maintained and distributed by the Protein Data Bank. Sponsors: This work is supported by grants from the NIH (1-P50-GM62412, 1-K22-HG00056) and the Searle Scholars Program (01-L-116), and by the US Department of Energy under contract DE-AC03-76SF00098.

Proper citation: ASTRAL Compendium for Sequence and Structure Analysis (RRID:SCR_001886) Copy   

•   Source: SciCrunch Registry

https://www.mdcalc.com/calc/715/nih-stroke-scale-score-nihss

The National Institutes of Health Stroke Scale (NIHSS) is a systematic assessment tool that provides a quantitative measure of stroke-related neurological deficit. The NIHSS was originally designed as a research tool to measure baseline data on patients in acute stroke clinical trials. Now, the scale is also widely used as a clinical assessment tool to evaluate acuity of stroke patients, determine appropriate treatment, and predict patient outcome. The NIHSS can be used as a clinical stroke assessment tool to evaluate and document neurological status in acute stroke patients. The stroke scale is valid for predicting lesion size and can serve as a measure of stroke severity. The NIHSS has been shown to be a predictor of both short and long term outcome of stroke patients. Additionally, the stroke scale serves as a data collection tool for planning patient care and provides a common language for information exchanges among healthcare providers. Performing the scale takes between 5-8 minutes. Emergency physicians and nurses, neurologists, neuroscience nurses and other stroke team members are typical examples of who should be certified to perform the NIHSS. The NINDS/NIH training and testing DVD can be obtained from the National Institute of Neurological Disorders and Stroke. Sponsors: NIHSS is supported by the National Institute of Neurological Disorders and Stroke (NINDS).

Proper citation: National Institutes of Health Stroke Scale (RRID:SCR_001804) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/COMPASS.html

Software for combinatorial polyfunctionality analysis of single cells. It is a statistical framework that enables unbiased analysis of antigen-specific T-cell subsets. It uses a Bayesian hierarchical framework to model all observed cell-subsets and select the most likely to be antigen-specific while regularizing the small cell counts that often arise in multi-parameter space. The model provides a posterior probability of specificity for each cell subset and each sample, which can be used to profile a subject's immune response to external stimuli such as infection or vaccination.

Proper citation: COMPASS (RRID:SCR_001801) Copy   

•   Source: SciCrunch Registry

https://david.ncifcrf.gov/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025. Bioinformatics resource system including web server and web service for functional annotation and enrichment analyses of gene lists. Consists of comprehensive knowledgebase and set of functional analysis tools. Includes gene centered database integrating heterogeneous gene annotation resources to facilitate high throughput gene functional analysis.

Proper citation: DAVID (RRID:SCR_001881) Copy   

•   Source: SciCrunch Registry

https://software.broadinstitute.org/gatk/

A software package to analyze next-generation resequencing data. The toolkit offers a wide variety of tools, with a primary focus on variant discovery and genotyping as well as strong emphasis on data quality assurance. Its robust architecture, powerful processing engine and high-performance computing features make it capable of taking on projects of any size. This software library makes writing efficient analysis tools using next-generation sequencing data very easy, and second it's a suite of tools for working with human medical resequencing projects such as 1000 Genomes and The Cancer Genome Atlas. These tools include things like a depth of coverage analyzers, a quality score recalibrator, a SNP/indel caller and a local realigner. (entry from Genetic Analysis Software)

Proper citation: GATK (RRID:SCR_001876) Copy   

•   Source: SciCrunch Registry

http://flybrain.stanford.edu/

Project content including raw image data, neuronal tracings, image registration tools and analysis scripts covering three manuscripts: Comprehensive Maps of DrosophilaHigher Olfactory Centres : Spatially Segregated Fruit and Pheromone Representation which uses single cell labeling and image registration to describe the organization of the higher olfactory centers of Drosophila; Diversity and wiring variability of olfactory local interneurons in the Drosophila antennal lobe which uses single cell labeling to describe the organization of the antennal lobe local interneurons; and Sexual Dimorphism in the Fly Brain which uses clonal analysis and image registration to identify a large number of sex differences in the brain and VNC of Drosophila. Data * Raw Data of Reference Brain (pic, amira) (both seed and average) * Label field of LH and MB calyx and surfaces for these structures * Label field of neuropil of Reference Brain * Traces (before and after registration). Neurolucida, SWC and AmiraMesh lineset. * MB and LH Density Data for different classes of neuron. In R format and as separate amira files. * Registration files for all brains used in the study * MBLH confocal images for all brains actually used in the study (Biorad pic format) * Sample confocal images for antennal lobe of every PN class * Confocal stacks of GABA stained ventral PNs Programs * ImageJ plugins (Biorad reader /writer/Amira reader/writer/IGS raw Reader) * Binary of registration, warp and gregxform (macosx only, others on request) * Simple GUI for registration tools (macosx only at present) * R analysis/visualization functions * Amira Script to show examples of neuronal classes The website is a collaboration between the labs of Greg Jefferis and Liqun Luo and has been built by Chris Potter and Greg Jefferis. The core Image Registration tools were created by Torsten Rohlfing and Calvin Maurer.

Proper citation: Flybrain at Stanford (RRID:SCR_001877) Copy   

•   Source: SciCrunch Registry

http://learn.genetics.utah.edu/

Educational resources that provide accurate and unbiased information about topics in genetics, bioscience and health for global and local audiences. They are jargon-free, target multiple learning styles, and often convey concepts through animation and interactivity. The Genetic Science Learning Center is a science and health education program located in the midst of the bioscience research being carried out at the University of Utah. Our mission is making science easy for everyone to understand. * Two websites, available free of charge to Internet users worldwide: ** Learn.Genetics delivers educational materials on genetics, bioscience and health topics. They are designed to be used by students, teachers and members of the public. The materials meet selected US education standards for science and health. ** Teach.Genetics provides resources for K-12 teachers, higher education faculty, and public educators. These include PDF-based Print-and-Go™ activities, unit plans and other supporting resources. The materials are designed to support and extend the materials on Learn.Genetics. *Professional development programs that update K-16 teachers' expertise in bioscience and health topics as well as prepare them to implement the materials on our websites. * Community programs that engage with diverse communities in discussions about genetics and health, and in developing culturally and linguistically-appropriate educational materials. Some topics in genetics and bioscience research are controversial. The Center does not take sides in political or ethical controversies. Rather, our goal is to provide comprehensive information that promotes a lively discussion of these topics, so that individuals can arrive at their own informed decisions.

Proper citation: University of Utah Genetic Science Learning Center - Learn Genetics (RRID:SCR_001910) Copy   

•   Source: SciCrunch Registry

http://www.agcol.arizona.edu/software/tcw/

Software package for assembling, annotating, querying, and comparing transcript and expression level data that consists of two parts: * singleTCW (sTCW): Single transcript sets or assemblies; annotation; differential expression (EdgeR, DEGSeq, DESeq, GoSeq) * multiTCW (mTCW): Comparison of multiple transcript sets; ortholog grouping (e.g., OrthoMCL) It has been tested on Linux and uses Java, mySQL and optionally R.

Proper citation: TCW (RRID:SCR_001875) Copy   

•   Source: SciCrunch Registry

http://victorian-bioinformatics-consortium.github.io/degust/

An interactive web tool for visualizing differential gene expression data.

Proper citation: Degust (RRID:SCR_001878) Copy   

•   Source: SciCrunch Registry

http://english.msip.go.kr/english/main/main.do

Division of the South Korea government responsible for formulating national science and technology policies and plans.

Proper citation: Korean Ministry of Science ICT and Future Planning (RRID:SCR_001911) Copy   

•   Source: SciCrunch Registry

http://www.bioconductor.org/packages/release/bioc/html/flowUtils.html

Software that provides utilities for flow cytometry data.

Proper citation: flowUtils (RRID:SCR_001879) Copy   

•   Source: SciCrunch Registry

http://mousecyc.jax.org/

A manually curated database of both known and predicted metabolic pathways for the laboratory mouse. It has been integrated with genetic and genomic data for the laboratory mouse available from the Mouse Genome Informatics database and with pathway data from other organisms, including human. The database records for 1,060 genes in Mouse Genome Informatics (MGI) are linked directly to 294 pathways with 1,790 compounds and 1,122 enzymatic reactions in MouseCyc. (Aug. 2013) BLAST and other tools are available. The initial focus for the development of MouseCyc is on metabolism and includes such cell level processes as biosynthesis, degradation, energy production, and detoxification. MouseCyc differs from existing pathway databases and software tools because of the extent to which the pathway information in MouseCyc is integrated with the wealth of biological knowledge for the laboratory mouse that is available from the Mouse Genome Informatics (MGI) database.

Proper citation: MouseCyc (RRID:SCR_001791) Copy   

•   Source: SciCrunch Registry