Tools   Select Another Resource Report Type

http://www.cyflogic.com/

Software tool for a flow cytometry data analysis for Microsoft Windows enviroment developed by CyFlo Ltd. Has analysis capabilities, such as dot plot, histogram and statistics.

Proper citation: Cyflogic (RRID:SCR_016635) Copy   

•   Source: SciCrunch Registry

https://www.ncbi.nlm.nih.gov/sutils/pasc/viridty.cgi

Web tool for analysis of pairwise identity distribution within viral families. Used for virus sequence-based classification. Data in the system are updated every day to reflect changes in virus taxonomy and additions of new virus sequences to the public database.

Proper citation: PASC (RRID:SCR_016642) Copy   

•   Source: SciCrunch Registry

https://github.com/zburkett/VoICE

Software that groups vocal elements of birdsong by creating a high dimensionality dataset through scoring spectral similarity between vocalizations.

Proper citation: Vocal Inventory Clustering Engine (VoICE) (RRID:SCR_016004) Copy   

•   Source: SciCrunch Registry

https://omicc.niaid.nih.gov

Community based, biologist friendly web platform for creating and meta analyzing annotated gene expression data compendia., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: OMiCC (RRID:SCR_016604) Copy   

•   Source: SciCrunch Registry

https://cran.r-project.org/web/packages/factoextra/index.html

R package from CRAN to extract and visualize the results of multivariate data analysis.

Proper citation: factoextra (RRID:SCR_016692) Copy   

•   Source: SciCrunch Registry

https://github.com/csb-toolbox/CSB

Software package as an application framework and a Python class library. It is designed for reading, storing and analyzing biomolecular structures in a variety of formats with rich support for statistical analyses.

Proper citation: Computational Structural Biology Toolbox (RRID:SCR_016065) Copy   

•   Source: SciCrunch Registry

https://satijalab.org/seurat/get_started.html

Software as R package designed for QC, analysis, and exploration of single cell RNA-seq data. Enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data.

Proper citation: Seurat (RRID:SCR_016341) 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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

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   

•   Source: SciCrunch Registry

https://biocodecommons.org/

Platform provides resources for genomic observations from collection to analysis and publication. Works with standards community to ensure clear vocabularies and useful ontologies for biological resources and related assets. Biocode Commons is also collaborating on development of Biological Collections Ontology, working to better integrate ontologies, vocabularies, and relevant standards that are related to BCO.

Proper citation: Biocode Commons (RRID:SCR_024553) Copy   

•   Source: SciCrunch Registry

http://rfmri.org/DPARSF

A MATLAB toolbox forpipeline data analysis of resting-state fMRI that is based on Statistical Parametric Mapping (SPM) and a plug-in software within DPABI. After the user arranges the Digital Imaging and Communications in Medicine (DICOM) files and click a few buttons to set parameters, DPARSF will then give all the preprocessed (slice timing, realign, normalize, smooth) data and results for functional connectivity, regional homogeneity, amplitude of low-frequency fluctuation (ALFF), fractional ALFF, degree centrality, voxel-mirrored homotopic connectivity (VMHC) results. DPARSF can also create a report for excluding subjects with excessive head motion and generate a set of pictures for easily checking the effect of normalization. In addition, users can also use DPARSF to extract time courses from regions of interest. DPARSF basic edition is very easy to use while DPARSF advanced edition (alias: DPARSFA) is much more flexible and powerful. DPARSFA can parallel the computation for each subject, and can be used to reorient images interactively or define regions of interest interactively. Users can skip or combine the processing steps in DPARSF advanced edition freely.

Proper citation: DPARSF (RRID:SCR_002372) Copy   

•   Source: SciCrunch Registry

https://www.upf.edu/web/ntsa/downloads/-/asset_publisher/xvT6E4pczrBw/content/2012-nonrandomness-nonlinear-dependence-and-nonstationarity-of-electroencephalographic-recordings-from-epilepsy-patients

THIS RESOURCE IS NO LONGER IN SERVICE, documented November 23, 2020; EEG data set, source code, and results from 7500 signal pairs from 5 epilepsy patients analyzed in the manuscript, Andrzejak RG, Schindler K, Rummel C. Nonrandomness, nonlinear dependence, and nonstationarity of electroencephalographic recordings from epilepsy patients. Phys. Rev. E, 86, 046206, 2012. All Matlab source codes are included in the file ASR_Sources_2012_10_16.zip. The clinical purpose of these recordings was to delineate the brain areas to be surgically removed in each individual patient in order to achieve seizure control.

Proper citation: Bern-Barcelona EEG database (RRID:SCR_001582) Copy   

•   Source: SciCrunch Registry

http://www.genetrap.org/

Consortium represents all publicly available gene trap cell lines, which are available on non-collaborative basis for nominal handling fees. Researchers can search and browse IGTC database for cell lines of interest using accession numbers or IDs, keywords, sequence data, tissue expression profiles and biological pathways, can find trapped genes of interest on IGTC website, and order cell lines for generation of mutant mice through blastocyst injection. Consortium members include: BayGenomics (USA), Centre for Modelling Human Disease (Toronto, Canada), Embryonic Stem Cell Database (University of Manitoba, Canada), Exchangeable Gene Trap Clones (Kumamoto University, Japan), German Gene Trap Consortium provider (Germany), Sanger Institute Gene Trap Resource (Cambridge, UK), Soriano Lab Gene Trap Resource (Mount Sinai School of Medicine, New York, USA), Texas Institute for Genomic Medicine - TIGM (USA), TIGEM-IRBM Gene Trap (Naples, Italy).

Proper citation: International Gene Trap Consortium (RRID:SCR_002305) Copy   

•   Source: SciCrunch Registry

http://icatb.sourceforge.net/fusion/fusion_startup.php

A MATLAB toolbox which implements the joint Independent Component Analysis (ICA), parallel ICA and CCA with joint ICA methods. It is used to to extract the shared information across modalities like fMRI, EEG, sMRI and SNP data. * Environment: Win32 (MS Windows), Gnome, KDE * Operating System: MacOS, Windows, Linux * Programming Language: MATLAB * Supported Data Format: ANALYZE, NIfTI-1

Proper citation: Fusion ICA Toolbox (RRID:SCR_003494) Copy   

•   Source: SciCrunch Registry

http://gephi.org/

Open-source software for network visualization and analysis helping data analysts to intuitively reveal patterns and trends, highlight outliers and tells stories with their data. It uses a 3D render engine to display large graphs in real-time and to speed up the exploration. Gephi combines built-in functionalities and flexible architecture to: explore, analyze, spatialize, filter, cluterize, manipulate and export all types of networks. Gephi runs on Windows, Linux and Mac OS X. Gephi is based on a visualize-and-manipulate paradigm which allow any user to discover networks and data properties. Moreover, it is designed to follow the chain of a case study, from data file to nice printable maps. It is open-source and free (GNU General Public License). Applications: * Exploratory Data Analysis: intuition-oriented analysis by networks manipulations in real time. * Link Analysis: revealing the underlying structures of associations between objects, in particular in scale-free networks. * Social Network Analysis: easy creation of social data connectors to map community organizations and small-world networks. * Biological Network analysis: representing patterns of biological data. * Poster creation: scientific work promotion with hi-quality printable maps. Gephi 0.7 architecture is modular and therefore allows developers to add and extend functionalities with ease. New features like Metrics, Layout, Filters, Data sources and more can be easily packaged in plugins and shared. The built-in Plugins Center automatically gets the list of plugins available from the Gephi Plugin portal and takes care of all software updates. Download, comment, and rate plugins provided by community members and third-party companies, or post your own contributions!

Proper citation: Gephi (RRID:SCR_004293) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.biol.rug.nl/standalone/fiva/

Functional Information Viewer and Analyzer (FIVA) aids researchers in the prokaryotic community to quickly identify relevant biological processes following transcriptome analysis. Our software is able to assist in functional profiling of large sets of genes and generates a comprehensive overview of affected biological processes. Currently, seven different modules containing functional information have been implemented: (i) gene regulatory interactions, (ii) cluster of orthologous groups (COG) of proteins, (iii) gene ontologies (GO), (iv) metabolic pathways (v) Swiss Prot keywords, (vi) InterPro domains - and (vii) generic functional categories. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: FIVA - Functional Information Viewer and Analyzer (RRID:SCR_005776) Copy   

•   Source: SciCrunch Registry

http://ftp://ftp.geneontology.org/pub/go/www/GO.tools_by_type.term_enrichment.shtml#gobean

GoBean is a Java application for gene ontology enrichment analysis. It utilizes the NetBeans platform framework. Features * Graphical comparison of multiple enrichment analysis results * Versatile filter facility for focused analysis of enrichment results * Effective exploitation of the graphical/hierarchical structure of GO * Evidence code based association filtering * Supports local data files such as the ontology obo file and gene association files * Supports late enrichment methods and multiple testing corrections * Built-in ID conversion for common species using Ensembl biomart service Platform: Windows compatible, Mac OS X compatible, Linux compatible

Proper citation: GoBean - a Java application for Gene Ontology enrichment analysis (RRID:SCR_005808) Copy   

•   Source: SciCrunch Registry

http://www.clcbio.com/products/clc-main-workbench/

A suite of software for DNA, RNA and protein sequence data analysis. The software allows for the analysis and visualization of Sanger sequencing data as well as gene expression analysis, molecular cloning, primer design, phylogenetic analyses, and sequence data management.

Proper citation: CLC Main Workbench (RRID:SCR_000354) Copy   

•   Source: SciCrunch Registry