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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   

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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   

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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   

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http://pdbfun.uniroma2.it/

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. PDBfun is a web server for structural and functional analysis of proteins at the residue level. pdbFun gives fast access to the whole Protein Data Bank (PDB) organized as a database of annotated residues. The available data (features) range from solvent exposure to ligand binding ability, location in a protein cavity, secondary structure, residue type, sequence functional pattern, protein domain and catalytic activity. PDBfun is an integrated web tool for querying the PDB at the residue level and for local structural comparison. It integrates knowledge on single residues in protein structures coming from other databases or calculated with available or in-house developed instruments for structural analysis. Each set of different annotations represents a feature. Features are listed in PDBfun main page in orange. Features can be used for building residues selections.

Proper citation: Protein Databank Fun (RRID:SCR_008226) Copy   

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http://connectomics.org/viewer

Extensible, scriptable, pythonic software tool for visualization and analysis in structural neuroimaging research on many spatial scales. Employing the Connectome File Format, diverse data such as networks, surfaces, volumes, tracks and metadata are handled and integrated. The field of Connectomics research benefits from recent advances in structural neuroimaging technologies on all spatial scales. The need for software tools to visualize and analyze the emerging data is urgent. The ConnectomeViewer application was developed to meet the needs of basic and clinical neuroscientists, as well as complex network scientists, providing an integrative, extensible platform to visualize and analyze Connectomics data. With the Connectome File Format, interlinking different datatypes such as hierarchical networks, surface data, volumetric data is easy and might provide new ways of analyzing and interacting with data. Furthermore, ConnectomeViewer readily integrates with: * ConnectomeWiki: a semantic knowledge base representing connectomics data at a mesoscale level across various species, allowing easy access to relevant literature and databases. * ConnectomeDatabase: a repository to store and disseminate Connectome files.

Proper citation: ConnectomeViewer: Multi-Modal Multi-Level Network Visualization and Analysis (RRID:SCR_008312) Copy   

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http://www.broad.mit.edu/cancer/software/genecluster2/gc2.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 17, 2013. A software package for analyzing gene expression and other bioarray data, giving users a variety of methods to build and evaluate class predictors, visualize marker lists, cluster data and validate results. GeneCluster 2.0 greatly expands the data analysis capabilities of GeneCluster 1.0 by adding supervised classification, gene selection, class discovery and permutation test methods. It includes algorithms for building and testing supervised models using weighted voting (WV) and k-nearest neighbor (KNN) algorithms, a module for systematically finding and evaluating clustering via self-organizing maps, and modules for marker gene selection and heat map visualization that allow users to view and sort samples and genes by many criteria. It enhances the clustering capabilities of GeneCluster 1.0 by adding a module for batch SOM clustering, and also includes a marker gene finder based on a KNN analysis and a visualization module. GeneCluster 2.0 is a stand-alone Java application and runs on any platform that supports the Java Runtime Environment version 1.3.1 or greater.

Proper citation: GeneCluster 2: An Advanced Toolset for Bioarray Analysis (RRID:SCR_008446) Copy   

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http://www.uni-koeln.de/med-fak/cgars/

Software package to dissect random from non-random patterns in copy number data and thereby to assess significantly enriched somatic copy number aberrations (SCNA) across a set of tumor specimens or cell lines.

Proper citation: CGARS (RRID:SCR_006404) Copy   

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http://bioinformatics.ubc.ca/ermineJ/

Data analysis software for gene sets in expression microarray data or other genome-wide data that results in rankings of genes. A typical goal is to determine whether particular biological pathways are doing something interesting in the data. The software is designed to be used by biologists with little or no informatics background. A command-line interface is available for users who wish to script the use of ermineJ. Major features include: * Implementation of multiple methods for gene set analysis: ** Over-representation analysis ** A resampling-based method that uses gene scores ** A rank-based method that uses gene scores ** A resampling-based method that uses correlation between gene expression profiles (a type of cluster-enrichment analysis). * Gene sets receive statistical scores (p-values), and multiple test correction is supported. * Support of the Gene Ontology terminology; users can choose which aspects to analyze. * User files use simple text formats. * Users can modify gene sets or create new ones. * The results can be visualized within the software. * It is simple to compare multiple analyses of the same data set with different settings. * User-definable hyperlinks are provided to external sites to allow more efficient browsing of the results. * For programmers, there is a command line interface as well as a simple application programming interface that can be used to plug ermineJ functionality into your own code Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: ErmineJ (RRID:SCR_006450) Copy   

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http://www.ebi.ac.uk/ena/

Public archive providing a comprehensive record of the world''''s nucleotide sequencing information, covering raw sequencing data, sequence assembly information and functional annotation. All submitted data, once public, will be exchanged with the NCBI and DDBJ as part of the INSDC data exchange agreement. The European Nucleotide Archive (ENA) captures and presents information relating to experimental workflows that are based around nucleotide sequencing. A typical workflow includes the isolation and preparation of material for sequencing, a run of a sequencing machine in which sequencing data are produced and a subsequent bioinformatic analysis pipeline. ENA records this information in a data model that covers input information (sample, experimental setup, machine configuration), output machine data (sequence traces, reads and quality scores) and interpreted information (assembly, mapping, functional annotation). Data arrive at ENA from a variety of sources including submissions of raw data, assembled sequences and annotation from small-scale sequencing efforts, data provision from the major European sequencing centers and routine and comprehensive exchange with their partners in the International Nucleotide Sequence Database Collaboration (INSDC). Provision of nucleotide sequence data to ENA or its INSDC partners has become a central and mandatory step in the dissemination of research findings to the scientific community. ENA works with publishers of scientific literature and funding bodies to ensure compliance with these principles and to provide optimal submission systems and data access tools that work seamlessly with the published literature. ENA is made up of a number of distinct databases that includes the EMBL Nucleotide Sequence Database (Embl-Bank), the newly established Sequence Read Archive (SRA) and the Trace Archive. The main tool for downloading ENA data is the ENA Browser, which is available through REST URLs for easy programmatic use. All ENA data are available through the ENA Browser. Note: EMBL Nucleotide Sequence Database (EMBL-Bank) is entirely included within this resource.

Proper citation: European Nucleotide Archive (ENA) (RRID:SCR_006515) Copy   

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http://brainproducts.com/productdetails.php?id=17

Software to manage the daily work of analyzing various neurophysiological data. Features include a history tree, automated analysis, various data format readers, and more.

Proper citation: BrainVision Analyzer (RRID:SCR_002356) Copy   

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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   

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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   

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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   

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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   

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http://rana.lbl.gov/drosophila

A single source for sequences, assemblies, annotations and analyses of the genomes of members of the fruitfly genus Drosophlia. It is meant as resource for Drosophilists and other researchers interested in comparative analysis of these species and their genomes. There are pages for each species, as well as pages for different types of multi-species resources (e.g. alignments). If you have a public resource that will help this project, please consider making it available through this page by emailing multiple_at_fruitfly.org.

Proper citation: Assembly/Alignment/Annotation of 12 Related Drosophila Species (RRID:SCR_002921) Copy   

•   Source: SciCrunch Registry

http://www.brainscape.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on May 23, 2013. Database for resting state functional connectivity studies. Functional connectivity has shown tremendous promise in mapping the intrinsic functional topography of the brain, evaluating neuroanatomical models, and investigating neurological and psychiatric disease. Brainscape includes a repository of public and private data and an analysis engine for exploring the correlation structure of spontaneous fluctuations in the fMRI BOLD signal. (DICOM data is the image format that can be uploaded.) With Brainscape you can upload, analyze, and share your own data. You can search for, download, and analyze studies in the repository of shared data. The analysis engine works by selecting one or more studies, typing in the coordinates of a brain region of interest, and the seed-region correlation engine computes the correlation structure across the whole brain. (T1, T2 and EPI data are the scan types Brainscape can process.) You decide who can access your data. You can keep it to yourself, share with select colleagues, or share it with everyone. The Brainscape database and analysis tools are open source and freely available.

Proper citation: Brainscape (RRID:SCR_002962) Copy   

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http://www.bioinformatics.babraham.ac.uk/projects/chipmonk/

Software tool to visualize and analyse ChIP-on-chip array data. Main features: * Import of data from Nimblegen arrays (other formats can be added if people send us examples) * Normalization of data (both per array and per probe) * Various data plotting options to assess data quality and the effectiveness of normalization * Creation of data groups for visualization and analysis * Visualization of data against an annotated genome. * Statistical analysis of data to find probes of interest * Creation of reports containing probes, data and genome annotation Note: This project is no longer being developed, but critical bug fixes will still be provided

Proper citation: ChIPMonk (RRID:SCR_002975) Copy   

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http://www.atgc-montpellier.fr/

A bioinformatics platform that is a joint project of several South of France laboratories with available services based on their expertise, issued from their research activities which involve phylogenetics, population genetics, molecular evolution, genome dynamics, comparative and functional genomics, and transcriptome analysis. Most of the software and databases on ATGC are (co)authored by researchers from South of France teams. Some are widely used and highly cited. South of France laboratories: * CRBM (transcriptomes and stem cells). * IBC (computational biology). * MiVEGEC (evolution and phylogeny). * LGDP (plant genomics). * LIRMM (computer science). * South Green (plant genomics).

Proper citation: ATGC: Montpellier bioinformatics platform (RRID:SCR_002917) Copy   

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http://www.broadinstitute.org/scientific-community/science/programs/medical-and-population-genetics/haploview/haploview

A Java based software tool designed to simplify and expedite the process of haplotype analysis by providing a common interface to several tasks relating to such analyses. Haploview currently allows users to examine block structures, generate haplotypes in these blocks, run association tests, and save the data in a number of formats. All functionalities are highly customizable. (entry from Genetic Analysis Software) * LD & haplotype block analysis * haplotype population frequency estimation * single SNP and haplotype association tests * permutation testing for association significance * implementation of Paul de Bakker's Tagger tag SNP selection algorithm. * automatic download of phased genotype data from HapMap * visualization and plotting of PLINK whole genome association results including advanced filtering options Haploview is fully compatible with data dumps from the HapMap project and the Perlegen Genotype Browser. It can analyze thousands of SNPs (tens of thousands in command line mode) in thousands of individuals. Note: Haploview is currently on a development and support freeze. The team is currently looking at a variety of options in order to provide support for the software. Haploview is an open source project hosted by SourceForge. The source can be downloaded at the SourceForge project site.

Proper citation: Haploview (RRID:SCR_003076) Copy   

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http://www.mrc-cbu.cam.ac.uk/Imaging

Portal where neuroimaging studies are carried out using a Siemens 3T Tim Trio Magnetic Resonance Imaging (or MRI) scanner that is wholly dedicated to studies in Cognitive Neuroscience. From emotions and memories to language and learning, functional neuroimaging is being applied in many different areas of Cognitive Neuroscience. In many cases, this research relies upon support from healthy volunteers although neuroimaging studies are also being conducted in various clinical populations, including depression, anxiety, Parkinson's disease and Alzheimer's disease.

Proper citation: CBU Imaging Wiki (RRID:SCR_003014) Copy   

•   Source: SciCrunch Registry