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http://www.kcl.ac.uk/ioppn/depts/neuroimaging/research/imaginganalysis/Software/PIPR.aspx

Software toolbox designed to provide machine learning methods for pre-processed imaging data allowing for two (or more) class classification in the context of drug development. The Toolbox includes implementations of Gaussian Process Classification, Support Vector Machines, Ordinal Regression and Sparse Multinomial Logistic Regression for fMRI, Structural and ASL imaging data.

Proper citation: Pharmacological Imaging and Pattern Recognition toolbox (RRID:SCR_003874) Copy   

•   Source: SciCrunch Registry

http://www.blast2go.com/b2ghome

An ALL in ONE tool for functional annotation of (novel) sequences and the analysis of annotation data. Blast2GO (B2G) joins in one universal application similarity search based GO annotation and functional analysis. B2G offers the possibility of direct statistical analysis on gene function information and visualization of relevant functional features on a highlighted GO direct acyclic graph (DAG). Furthermore B2G includes various statistics charts summarizing the results obtained at BLASTing, GO-mapping, annotation and enrichment analysis (Fisher''''s Exact Test). All analysis process steps are configurable and data import and export are supported at any stage. The application also accepts pre-existing BLAST or annotation files and takes them to subsequent steps. The tool offers a very suitable platform for high throughput functional genomics research in non-model species. B2G is a species-independent, intuitive and interactive desktop application which allows monitoring and comprehending the whole annotation and analysis process supported by additional features like GO Slim integration, evidence code (EC) consideration, a Batch-Mode or GO-Multilevel-Pies. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Blast2GO (RRID:SCR_005828) Copy   

•   Source: SciCrunch Registry

http://www.bcgsc.ca/platform/bioinfo/software/alea

A computational software toolbox for allele-specific (AS) epigenomics analysis. It incorporates allelic variation data within existing resources, allowing for the identification of significant associations between epigenetic modifications and specific allelic variants in human and mouse cells. It provides a customizable pipeline of command line tools for AS analysis of next-generation sequencing data (ChIP-seq, RNA-seq, etc.) that takes the raw sequencing data and produces separate allelic tracks ready to be viewed on genome browsers. ALEA takes advantage of the available genomic resources for human (The 1000 Genomes Project Consortium) and mouse (The Mouse Genome Project) to reconstruct diploid in-silico genomes for human or hybrid mice under study. Then, for each accompanying ChIP-seq or RNA-seq dataset, it generates two Wiggle track format (WIG) files from short reads aligned differentially to each haplotype.

Proper citation: ALEA (RRID:SCR_006417) Copy   

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http://www.bioconductor.org/

Software repository for R packages related to analysis and comprehension of high throughput genomic data. Uses separate set of commands for installation of packages. Software project based on R programming language that provides tools for analysis and comprehension of high throughput genomic data.

Proper citation: Bioconductor (RRID:SCR_006442) Copy   

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http://www.cgat.org/~andreas/documentation/cgat/cgat.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 3, 2023. A collection of tools for the computational genomicist written in the python language to assist in the analysis of genome scale data from a range of standard file formats. The toolkit enables filtering, comparison, conversion, summarization and annotation of genomic intervals, gene sets and sequences. The tools can both be run from the Unix command line and installed into visual workflow builders, such as Galaxy. Please note that the tools are part of a larger code base also including genomics and NGS pipelines. Everyone who uses parts of the CGAT code collection is encouraged to contribute. Contributions can take many forms: bugreports, bugfixes, new scripts and pipelines, documentation, tests, etc. All contributions are welcome.

Proper citation: Computational Genomics Analysis Tools (RRID:SCR_006390) Copy   

•   Source: SciCrunch Registry

http://www.gaworkshop.org/

The Genetic Analysis Workshops (GAWs) are a collaborative effort among genetic epidemiologists to evaluate and compare statistical genetic methods. For each GAW, topics are chosen that are relevant to current analytical problems in genetic epidemiology, and sets of real or computer-simulated data are distributed to investigators worldwide. Results of analyses are discussed and compared at meetings held in even-numbered years. The GAWs began in 1982 were initially motivated by the development and publication of several new algorithms for statistical genetic analysis, as well as by reports in the literature in which different investigators, using different methods of analysis, had reached contradictory conclusions. The impetus was initially to determine the numerical accuracy of the algorithms, to examine the robustness of the methodologies to violations of assumptions, and finally, to compare the range of conclusions that could be drawn from a single set of data. The Workshops have evolved to include consideration of problems related to analyses of specific complex traits, but the focus has always been on analytical methods. The Workshops provide an opportunity for participants to interact in addressing methodological issues, to test novel methods on the same well-characterized data sets, to compare results and interpretations, and to discuss current problems in genetic analysis. The Workshop discussions are a forum for investigators who are evolving new methods of analysis as well as for those who wish to gain further experience with existing methods. The success of the Workshops is due at least in part to the focus on specific problems and data sets, the informality of sessions, and the requirement that everyone who attends must have made a contribution. Topics are chosen and a small group of organizers is selected by the GAW Advisory Committee. Data sets are assembled, and six or seven months before each GAW, a memo is sent to individuals on the GAW mailing list announcing the availability of the GAW data. Included with the memo is a short description of the data sets and a form for requesting data. The form contains a statement to be signed by any investigator requesting the data, acknowledging that the data are confidential and agreeing not to use them for any purpose other than the Genetic Analysis Workshop without written permission from the data provider(s). Data are distributed by the ftp or CD-ROM or, most recently, on the web, together with a more complete written description of the data sets. Investigators who wish to participate in GAW submit written contributions approximately 6-8 weeks before the Workshop. The GAW Advisory Committee reviews contributions for relevance to the GAW topics. Contributions are assembled and distributed to all participants approximately two weeks before the Workshop. Participation in the GAWs is limited to investigators who (1) submit results of their analyses for presentation at the Workshop, or (2) are data providers, invited speakers or discussants, or Workshop organizers. GAWs are held just before the meetings of the American Society of Human Genetics or the International Genetic Epidemiology Society, at a meeting site nearby. We choose a location that will encourage interaction among participants and permit an intense period of concentrated work. The proceedings of each GAW are published. Proceedings from GAW16 were published in part by Genetic Epidemiology 33(Suppl 1), S1-S110 (2009) and in part by Biomed Central (BMC Proceedings, Volume 3, Supplement 7, 2009). Sponsors: GAW is funded by the Southwest Foundation for Biomedical Research.

Proper citation: Genetic Analysis Workshop (RRID:SCR_008350) Copy   

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https://www.biotech.cornell.edu/core-facilities-brc/facilities/bioinformatics-facility

Facility provides access to high performance computing environment, BioHPC, which includes both hosted hardware and shared machines. Provides consulting and collaborations for Bioinformatics analysis and workflows.

Proper citation: Cornell University BRC Bioinformatics Core Facility (RRID:SCR_021757) Copy   

•   Source: SciCrunch Registry

https://www.flimfit.org/

Software package for quantitative analysis of large Fluorescence Lifetime Imaging Microscopy (FLIM) data, including global analysis. It is able to routinely analyse multi-well plate FLIM datasets on conventional PC workstations in a reasonable time., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: FLIMfit (RRID:SCR_016298) Copy   

•   Source: SciCrunch Registry

https://w3.psychology.su.se/sleipner/

Software package as collection of modules implementing methods of analysis that form self-contained and empirically grounded toolbox for handling longitudinal data within person oriented paradigm.

Proper citation: sleipner (RRID:SCR_018143) Copy   

•   Source: SciCrunch Registry

https://CRAN.R-project.org/package=meta

Software general R package providing standard methods for meta analysis.

Proper citation: meta (RRID:SCR_019055) Copy   

•   Source: SciCrunch Registry

https://github.com/sqjin/CellChat

Software R toolkit for inference, visualization and analysis of cell-cell communication from single cell data.Quantitatively infers and analyzes intercellular communication networks from single-cell RNA-sequencing data. Predicts major signaling inputs and outputs for cells and how those cells and signals coordinate for functions using network analysis and pattern recognition approaches. Classifies signaling pathways and delineates conserved and context specific pathways across different datasets.

Proper citation: CellChat (RRID:SCR_021946) Copy   

•   Source: SciCrunch Registry

http://visit.llnl.gov/

Open source software interactive, scalable, visualization, animation and analysis tool. Used to generate visualizations, animate them through time, manipulate them with variety of operators and mathematical expressions, and save resulting images and animations for presentations.

Proper citation: VisIt (RRID:SCR_024370) Copy   

•   Source: SciCrunch Registry

http://www.hunter.cuny.edu/chemistry/facilities/nmr/home

A service facility with four main spectrometers. The facility consists of four NMR instruments: a JEOL GX-400, a Varian Inova 500, a Bruker Avance 500 equipped with a 13C-1H cryoprobe, and a Bruker Avance III 600 MHz spectrometer equipped with a cryoprobe. These spectrometers are utilized by scientists from Hunter College, as well as from the entire CUNY community. The large variety of available probes allows detection of virtually any MR-active nuclide. Data analysis is performed either at the spectrometer workstation with vendor software or off-line with third party software packages.

Proper citation: Hunter NMR Spectroscopy Facility (RRID:SCR_000883) Copy   

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http://wyss.harvard.edu/viewpage/594/

A core facility with access to imaging equipment and analysis software such as wide-field light microscopy, Total Internal Reflection Fluorescence microscopy (TIRF), confocal microscopy, Atomic Force Microscopy (AFM), Transmission Electron Microscopy (TEM), small animal imaging, spectroscopy, and flow cytometry.

Proper citation: Wyss Institute Imaging Core (RRID:SCR_000898) Copy   

•   Source: SciCrunch Registry

http://www.sanger.ac.uk/science/tools/ssaha2-0

A program designed for the efficient mapping of sequence reads onto genomic references. The software is capable of reading most sequencing platforms and giving a range of outputs are supported.

Proper citation: Sequence Search and Alignment by Hashing Algorithm (RRID:SCR_000544) Copy   

•   Source: SciCrunch Registry

http://www.informatics.jax.org/phenotypes.shtml

Enables comparative phenotype analysis, searches for human disease models, and hypothesis generation by providing access to spontaneous, induced, and genetically engineered mutations and their strain-specific phenotypes.

Proper citation: Phenotypes and Mutant Alleles (RRID:SCR_017523) Copy   

•   Source: SciCrunch Registry

https://www.sourcebioscience.com/products/life-sciences-research/clones/rnai-resources/c-elegans-rnai-collection-ahringer/

C. elegans RNAi feeding library distributed by Source BioScience Ltd. Designed for genome wide study of gene function in C. elegans through loss of function studies.

Proper citation: C. elegans RNAi Collection (Ahringer) (RRID:SCR_017064) Copy   

•   Source: SciCrunch Registry

https://ualr.edu/bioinformatics/midsouth-bioinformatics-center/

Core provides bioinformatics consulting, training, technical assistance, and access to computational infrastructure for faculty, students, and researchers in region with their bioscience computational needs. Offers private sessions, workshops and training on specialty topics. Computing resources including software, computing cluster, technical advice.

Proper citation: University of Arkansas at Little Rock MidSouth Bioinformatics Center Core Facility (RRID:SCR_017168) Copy   

•   Source: SciCrunch Registry

https://git.io/vAeKZ

It is non-graphical user interface in MATLAB which relies on keyboard callback functions. Used for analyzing big data sets.

Proper citation: ROIs selection with a non-graphical user interface (RRID:SCR_016352) Copy   

•   Source: SciCrunch Registry

http://structuralbiology.cau.edu.cn/PlantGSEA/

Websever for gene set enrichment analysis of plants. Used for interpreting biological meaning of a list of genes by computing the overlaps with various previously defined gene sets.

Proper citation: PlantGSEA (RRID:SCR_016866) Copy   

•   Source: SciCrunch Registry