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http://discovery.hsci.harvard.edu/

An online database of curated cancer stem cell (CSC) experiments coupled to the Galaxy analytical framework. Driven by a need to improve our understanding of molecular processes that are common and unique across cancer stem cells (CSCs), the SCDE allows users to consistently describe, share and compare CSC data at the gene and pathway level. The initial focus has been on carefully curating tissue and cancer stem cell-related experiments from blood, intestine and brain to create a high quality resource containing 53 public studies and 1098 assays. The experimental information is captured and stored in the multi-omics Investigation/Study/Assay (ISA-Tab) format and can be queried in the data repository. A linked Galaxy framework provides a comprehensive, flexible environment populated with novel tools for gene list comparisons against molecular signatures in GeneSigDB and MSigDB, curated experiments in the SCDE and pathways in WikiPathways. Investigation/Study/Assay (ISA) infrastructure is the first general-purpose format and freely available desktop software suite targeted to experimentalists, curators and developers and that: * assists in the reporting and local management of experimental metadata (i.e. sample characteristics, technology and measurement types, sample-to-data relationships) from studies employing one or a combination of technologies; * empowers users to uptake community-defined minimum information checklists and ontologies, where required; * formats studies for submission to a growing number of international public repositories endorsing the tools, currently ENA (genomics), PRIDE (proteomics) and ArrayExpress (transcriptomics). Galaxy allows you to do analyses you cannot do anywhere else without the need to install or download anything. You can analyze multiple alignments, compare genomic annotations, profile metagenomic samples and much much more. Best of all, Galaxy''''s history system provides a complete analyses record that can be shared. Every history is an analysis workflow, which can be used to reproduce the entire experiment. The code for this Galaxy instance is available for download from BitBucket.

Proper citation: Stem Cell Discovery Engine (RRID:SCR_004453) Copy   

•   Source: SciCrunch Registry

http://www.trex.uqam.ca/

A web server dedicated to the reconstruction of phylogenetic trees, reticulation networks and to the inference of horizontal gene transfer (HGT) events.

Proper citation: Tree and reticulogram REConstruction (RRID:SCR_004497) Copy   

•   Source: SciCrunch Registry

https://www.fieldtriptoolbox.org

Software toolbox for analysis of MEG, EEG, and other electrophysiological data. Used by experimental neuroscientists.

Proper citation: FieldTrip (RRID:SCR_004849) Copy   

•   Source: SciCrunch Registry

http://www.well.ox.ac.uk/~kgaulton/chaos.shtml

A Perl-based system for annotation of variants identified in high-throughput sequencing experiments. Functionality includes annotation of variants with information relating to population genetics, known transcripts, positional records, and sequence motif-based prediction. In addition, annotated variants can be summarized and extracted to facilitate downstream analysis. There is also basic support for gene-based biological annotation, and eventually will include tools for variant and genotype analysis and visualization.

Proper citation: CHAoS (RRID:SCR_005174) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/hivcd/

Informatics software tool to identify patient sequences that are too similar to happen by chance alone. Highly similar sequences are likely to occur from contamination or other situations like geographic linkage.

Proper citation: HIVCD (RRID:SCR_005201) Copy   

•   Source: SciCrunch Registry

http://bio.math.berkeley.edu/eXpress/index.html

THIS RESOURCE IS NO LONGER IN SERVICE. Documented January 29, 2018.
From website: "Note that the eXpress software is also no longer being developed. We recommend you use kallisto instead." Kallisto can be found at http://pachterlab.github.io/kallisto/.

Software for streaming quantification for high-throughput DNA/RNA sequencing.
Can be used in any application where abundances of target sequences need to be estimated from short reads sequenced from them.

Proper citation: eXpress (RRID:SCR_006873) Copy   

•   Source: SciCrunch Registry

http://intramural.nimh.nih.gov/sscc/index.html

Scientific and Statistical Computing Core of the NIMH Intramural Research Program supporting functional neuroimaging research at the NIH. This includes development of new data analysis techniques, their implementation in the AFNI software, advising researchers on the analysis methods, and instructing them in the use of software tools. Support methods: A. Provision of software for analysis for FMRI data (AFNI package: http://afni.nimh.nih.gov) * AFNI has been developed for the last 10 years by Dr Cox, et al. (6 years in Milwaukee, 4 years at NIMH) * Formal and informal instruction in the use of AFNI, including outlines of the statistical methods used in the programs * Installation of AFNI on NIH computers (Mac OS X, Unix, Linux) approximately 120 NIH systems have used AFNI in the last month (80 NIMH, 20 NINDS, 20 other) * Realtime monitoring of FMRI data at scanners * Continuing development of new modules for AFNI to meet needs of NIH researchers B. Consulting with NIH researchers about FMRI data analysis issues, concerns, and methods

Proper citation: NIMH DIRP Scientific and Statistical Computing Core (RRID:SCR_006958) Copy   

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http://www.icpsr.umich.edu/SAMHDA/

Database of the nation''s substance abuse and mental health research data providing public use data files, file documentation, and access to restricted-use data files to support a better understanding of this critical area of public health. The goal is to increase the use of the data to most accurately understand and assess substance abuse and mental health problems and the impact of related treatment systems. The data include the U.S. general and special populations, annual series, and designs that produce nationally representative estimates. Some of the data acquired and archived have never before been publicly distributed. Each collection includes survey instruments (when provided), a bibliography of related literature, and related Web site links. All data may be downloaded free of charge in SPSS, SAS, STATA, and ASCII formats and most studies are available for use with the online data analysis system. This system allows users to conduct analyses ranging from cross-tabulation to regression without downloading data or relying on other software. Another feature, Quick Tables, provides the ability to select variables from drop down menus to produce cross-tabulations and graphs that may be customized and cut and pasted into documents. Documentation files, such as codebooks and questionnaires, can be downloaded and viewed online.

Proper citation: Substance Abuse and Mental Health Data Archive (RRID:SCR_007002) Copy   

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http://mga.bionet.nsc.ru/soft/maia-1.0/

Software package of programs for complex segregation analysis in animal pedigrees.

Proper citation: MAIA (RRID:SCR_007153) Copy   

•   Source: SciCrunch Registry

http://www.biomanta.org/

This project encompasses development of novel biological network analysis methods and infrastructure for querying biological data in a semantically-enabled format, and aims to create a semantic interactome model. Research within the BioMANTA project will focus on computational modelling and analysis, primarily using Semantic Web technologies and Machine Learning methods, of large-scale protein-protein interaction and compound activity networks across a wide variety of species. A range of information such as kinetic activity, tissue expression, and subcellular localization and disease state attributes will be included in the resulting data model. Protein interactions are a fundamental component of biological processes. Many proteins are functional only in multimeric complexes, or require interaction partners to achieve their correct localisation or function. For this reason, the study of protein-protein interaction (PPI) networks has become an area of growing interest in computational biology. Through the use of Semantic Web technologies such as Resource Description Framework (RDF) and Web Ontology Language (OWL), interaction data is modelled to create a knowledge representation in which meaning is vested in the ontology rather than instances of data. Stochastic and computational intelligence methods are applied to this data to infer high coverage networks. Semantic inferencing is used to infer previously unknown and meaningful pathways. Major project components: - The BioMANTA Ontology:- An OWL DL ontology incorporating the PSI-MI Ontology, the NCBI Taxonomy, and elements of BioPax ontology and Gene Ontology (describing subcellular localisation). This allows us to re-use existing ontologies, thereby reducing overheads associated with knowledge acquisition in the ontology development process. We are able to integrate existing public data that contain annotation in these formats. - Data conversion & semantic protein integration:- A set of software components that convert protein-protein databases (DIP, MPact, IntAct, etc.) from PSI-MI XML to RDF compliant with the BioMANTA ontology. These software allow us to make these protein-protein interaction datasets (and more generally, any PSI-MI XML data) semantically available for querying and inference within BioMANTA. - A RDF triple store based on RDF Molecules and the MapReduce architecture:- A proof-of-concept RDF triple store using RDF molecules and Hadoop scale-out architectures. Regular RDF graphs are deconstructed into RDF molecules, which are distributed over distributed compute nodes in the MapReduce architecture, and are subsequently combined to form equivalent RDF graphs. Such an approach makes the distributed SPARQL querying and reasoning on RDF triple stores possible. - A quantitative framework to integrate networks extracted from independent data sources (gene expression, subcellular localization, and ortholog mapping):- The model is multi-layer, with a first layer based on Decision Trees where each Decision tree is built on each dataset independently. The tree nodes are cut using Shannon''s entropy (mutual information); the decision of these independent trees is integrated using logistic regression, and the parameters are optimised using maximum likelihood. Sponsors: This resource is supported by the Pfizer Global Research and Development, the Institute for Molecular Bioscience (IMB), and the University of Queensland, Australia.

Proper citation: BioMANTA (RRID:SCR_007177) Copy   

•   Source: SciCrunch Registry

http://www.ccp4.ac.uk/

Portal for Macromolecular X-Ray Crystallography to produce and support an integrated suite of programs that allows researchers to determine macromolecular structures by X-ray crystallography, and other biophysical techniques. Used in the education and training of scientists in experimental structural biology for determination and analysis of protein structure.

Proper citation: CCP4 (RRID:SCR_007255) Copy   

•   Source: SciCrunch Registry

http://www.mbio.ncsu.edu/BioEdit/bioedit.html

Software tool as biological sequence alignment editor written for Windows 95/98/NT/2000/XP/7 and sequence analysis program. Provides sequence manipulation and analysis options and links to external analysis programs to view and manipulate sequences with simple point and click operations., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: BioEdit (RRID:SCR_007361) Copy   

•   Source: SciCrunch Registry

http://www.cephb.fr/

The Centre d''Etude du Polymorphisme Humain (CEPH) is a research laboratory, the main activities of which are the setting up, storage, processing and distribution of DNA collections for the identification of genetic factors conferring susceptibility to complex disorders. These collections are established in partnership and full collaboration with external French or international research groups. The Foundation currently hosts the CEPH reference panel, the HGDP panel (Human genome Diversity Cell Line Panel) and several collections amounting mid-2008 to more than 250 000 samples. The goal of CEPH is to understand complex multifactorial disorders necessitates the establishment of structures facilitating access to large and integrated collection of individuals, characterized by a large number of variables emanating from different technologies and platforms. To achieve this goal, CEPH facilitates the setting up of integrated analyses combining clinical, genetic and environmental data, for the identification of susceptibility factors to complex multifactorial disorders Additionally, CEHP allows the reception, storage, processing and distribution of biological sample collections. At the same time, it promotes and participates in the design and setting up of genetic studies: - in partnership and full collaboration with external research groups - giving access to a large number of variables - in a sufficient number of subjects - allowing large scale integrated analyses

Proper citation: Centre dEtude du Polymorphisme Humain (RRID:SCR_008026) Copy   

•   Source: SciCrunch Registry

https://www.bi.mpg.de/borst

Merger of the Max Planck Institute of Neurobiology and the Max Planck Institute of Ornithology and has been renamed to Circuits - Computation – Models. Department devoted to the study of how the brain computes to understand neural information processing at the level of individual neurons and small neural circuits.

Proper citation: Max Planck Institute for Biological Intelligence Circuits - Computation – Models (RRID:SCR_008048) Copy   

•   Source: SciCrunch Registry

http://www.imagwiki.nibib.nih.gov/

Special interest group that brings together program officers who have a shared interest in applying modeling and analysis methods to biomedical systems. The meetings are formatted to facilitate an open discussion of what is currently being supported, and for planning future directions in these areas. At each meeting, time is allotted to hear focused presentations from one or two participants to discuss issues relating to modeling and analysis across the government agencies. Discussions also occur online, and participants are informed of talks, conferences and other activities of interest to the group. IMAG recognized that the modeling community is on the forefront of thinking across the biological continuum, rather than just focusing at one scale or level of resolution. In addition IMAG identified a strong desire among modelers to form multi-disciplinary partnerships across varied research communities. Overall Intent of IMAG through the MSM Consortium is: * To develop new methodologies that span across biological scales * To develop multiscale methodologies applicable to biomedical, biological and behavioral research * To develop methodologies within the local multidisciplinary team and within the larger Framework environment * To further promote multiscale modeling through model sharing This wiki contains information relevant to the IMAG (Interagency Modeling and Analysis Group) and the MSM (Multi-scale Modeling Consortium).

Proper citation: Interagency Modeling and Analysis Group and Multi-scale Modeling Consortium Wiki (RRID:SCR_008046) Copy   

•   Source: SciCrunch Registry

https://wiki.med.harvard.edu/SysBio/Megason/GoFigure

GoFigure is a software platform for quantitating complex 4d in vivo microscopy based data in high-throughput at the level of the cell. A prime goal of GoFigure is the automatic segmentation of nuclei and cell membranes and in temporally tracking them across cell migration and division to create cell lineages. GoFigure v2.0 is a major new release of our software package for quantitative analysis of image data. The research focuses on analyzing cells in intact, whole zebrafish embryos using 4d (xyzt) imaging which tends to make automatic segmentation more difficult than with 2d or 2d+time imaging of cells in culture. This resource has developed an automatic segmentation pipeline that includes ICA based channel unmixing, membrane nuclear channel subtraction, Gaussian correlation, shape models, and level set based variational active contours. GoFigure was designed to meet the challenging requirements of in toto imaging. In toto imaging is a technology that we are developing in which we seek to track all the cell movements and divisions that form structures during embryonic development of zebrafish and to quantitate protein expression and localization on top of this digital lineage. For in toto imaging, GoFigure uses zebrafish embryos in which the nuclei and cell membranes have been marked with 2 different color fluorescent proteins to allow cells to be segmented and tracked. A transgenic line in a third color can be used to mark protein expression and localization using a genetic approach that this resource developed called FlipTraps or using traditional transgenic approaches. Embryos are imaged using confocal or 2-photon microscopy to capture high-resolution xyzt image sets used for cell tracking. The GoFigure GUI will provide many tools for visualization and analysis of bioimages. Since fully automatic segmentation of cells is never perfect, GoFigure will provide easy to use tools for semi-automatically and manually adding, deleting, and editing traces in 2d (figures-xy, xz, or yz), 3d (meshes- xyz), 4d (tracks- xyzt) and 4d+cell division (lineages). GoFigure will also provide a number of views into complex image data sets including 3d XYZ and XYT image views, tabular list views of traces, histograms, and scattergrams. Importantly, all these views will be linked together to allow the user to explore their data from multiple angles. Data will be easily sorted and color-coded in many ways to explore correlations in higher dimensional data. The GoFigure architecture is designed to allow additional segmentation, visualization, and analysis filters to be plugged in. Sponsors: GoFigure is developed by Harvard University., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Harvard Medical School, Department of Systems Biology: The Megason Lab -GoFigure Software (RRID:SCR_008037) Copy   

•   Source: SciCrunch Registry

Ratings or validation data are available for this resource

http://www.graphpad.com/

Statistical analysis software that combines scientific graphing, comprehensive curve fitting (nonlinear regression), understandable statistics, and data organization. Designed for biological research applications in pharmacology, physiology, and other biological fields for data analysis, hypothesis testing, and modeling.

Proper citation: GraphPad Prism (RRID:SCR_002798) Copy   

•   Source: SciCrunch Registry

http://camera.calit2.net/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 26, 2016; however, the URL provides links to associated projects and data. A suite of data query, download, upload, analysis and sharing tools serving the needs of the microbial ecology research community, and other scientists using metagenomics data.

Proper citation: Community Cyberinfrastructure for Advanced Marine Microbial Ecology Research and Analysis (RRID:SCR_002676) Copy   

•   Source: SciCrunch Registry

https://github.com/UCSFBiomagneticImagingLab/nutmeg

Software MEG/EEG analysis toolbox for reconstructing neural activation and overlaying it onto structural MR images. Toolbox runs under MATLAB in conjunction with SPM2 and can be used with Linux/UNIX, Mac OS X, and Windows platforms.

Proper citation: NUTMEG (RRID:SCR_002748) Copy   

•   Source: SciCrunch Registry

http://perso.telecom-paristech.fr/~cardoso/guidesepsou.html

Blind Source Separation and Independent Component Analysis (ICA) algorithms including: An efficient batch algorithm: JADE and Adaptive algorithms: relative gradient algorithms. Associated papers / documentation are included as well as thoughts on Multi-dimensional independent component analysis. * An efficient batch algorithm: JADE - For off-line ICA, an algorithm has been developed based on the (joint) diagonalization of cumulant matrices. "Good" statistical performance is achieved by involving all the cumulants of order 2 and 4 while a fast optimization is obtained by the device of joint diagonalization. JADE has been successfully applied to the processing of real data sets, such as found in mobile telephony and in airport radar as well as to bio-medical signals (ECG, EEG, multi-electrode neural recordings). The strongest point of JADE for applications of ICA is that it works off-the-shelf (no parameter tuning). They advocate using the code provided as a plug-in replacement for PCA (whenever one is willing to investigate if such a replacement is appropriate). The weakest point of the current implementation is that the number of sources (but not of sensors) is limited in practice (by the available memory) to something like 40 or 50 depending on your computer. The JADE algorithm was originally developed to process complex signals, motivated by applications to digital communications. Another implementation is now available which is tuned to process more efficiently real-valued signals. * Adaptive algorithms: relative gradient algorithms - For adaptive source separation, they have developed a class of equivariant algorithms. This means that their performance is independent of the mixing matrix. They are obtained as stochastic relative gradient algorithms. * Multi-dimensional independent component analysis - Performing ICA on ECG signals with the JADE algorithm, it was realized that an interesting extension of the notion of independent component analysis would be to consider an analysis into linear components that would be "as independent as possible" as in ICA, but would be "livin" in subspaces of dimension greater than 1. This could be called "MICA" for Multi-dimensional Independent Component Analysis.

Proper citation: Blind Source Separation and Independent Component Analysis (RRID:SCR_002812) Copy   

•   Source: SciCrunch Registry