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http://neuroimage.usc.edu/brainstorm/

Software as collaborative, open source application dedicated to analysis of brain recordings: MEG, EEG, fNIRS, ECoG, depth electrodes and animal invasive neurophysiology. User-Friendly Application for MEG/EEG Analysis.

Proper citation: Brainstorm (RRID:SCR_001761) Copy   

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http://incf.org/about/programs/modeling/blue-gene-access

Through this site, INCF provides he neuroinformatics community with access to an IBM Blue Gene/L supercomputer. INCF owns a share of a BlueGene/L (BG/L) supercomputer located at the Parallel Computer Center (PDC) at The Royal Institute of Technology (KTH) in Stockholm. Allocations are now available through the INCF Secretariat. During an initial evaluation phase, a limited numbers of large-scale computing projects will be selected, based on the suitability of the project for supercomputing. Research groups with limited access to supercomputers at their home institutions are given priority. Approved projects are regularly re-evaluated. New projects are approved based on availability and usage load of the BG/L. The Blue Gene/L supercomputer project is aimed at expanding the horizon of high-performance computing to unprecedented levels of scale and performance. Blue Gene/L is the first supercomputer in the Blue Gene family. The full Blue Gene/L consists of 64 racks containing 65,536 high-performance compute nodes. Each node (nodes and chips are the same in the Blue Gene system) contains two embedded 32-bit PowerPC processors. Furthermore, the same chip that is used for compute nodes is also used for the 1,024 I/O nodes. A three-dimensional torus network and a collective network are used to interconnect all nodes. The full system contains 33 terabytes of main memory; it is designed to achieve 183.5 teraflops peak performance using one of the processors of each node for computation and the other processor for communication, and 367 teraflops using both processors for computation. Another key architectural feature of this supercomputer is the link chip component and five Blue Gene/L networks, the PowerPC 440 core and floating-point enhancements, the on-chip and off-chip distributed memory system, the node- and system-level design for high reliability, and the comprehensive approach to fault isolation. One of the key objectives in Blue Gene/L design is to achieve cost/performance comparable to the COTS (Commodity Off The Shelf) approach, while at the same time incorporating a processor and network combination so powerful that it revolutionizes the performance of supercomputer systems. Sponsors: This resource is supported by the INCF.

Proper citation: International Neuroinformatics Coordinating Facility: Blue Gene/L Access (RRID:SCR_001755) Copy   

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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   

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https://github.com/INCF/MUSIC

Software that allows large scale neuron simulators to communicate during runtime. It allows exchange of data among parallel applications in a cluster environment, interconnects large-scale neuronal network simulators with each other or with other tools, participates in multi-simulations, and is continuously developed and extended. Three simulators currently have MUSIC interfaces: Moose, NEURON and NEST. Three applications execute in parallel while exchanging data via MUSIC. The software interface promotes interoperability by allowing models written for different simulators to be simulated together in a larger system. It enables re-usability of models or tools by providing a standard interface. As data are distributed over a number of processors, it is non-trivial to coordinate data transfer so that it reaches the correct destination at the correct time. Current and future simulators can make use of MUSIC - compliant general purpose tools and participate in multi-simulations, for example when: * Different parts of a complex nervous system model are optimally implemented in different simulators, and need to communicate with each other. * Post-processing of generated data is needed, where the amounts of data are too large for intermediate storage, and requires the simulator to pass the data directly to the post-processing module. A standard interface enables straight-forward independent third-party development and community sharing of interoperable software tools for parallel processing. * Library and utilities are written in C++, uses MPI. * It is possible to add a MUSIC interface to existing simulators. * Works independently, no assumptions are made about other applications to facilitate development of general purpose tools. * Performance Data transport with high bandwidth and low latency.

Proper citation: MUlti SImulation Coordinator (RRID:SCR_001756) Copy   

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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   

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http://dynamicbrain.neuroinf.jp/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on January 19. 2022. Platform to promote studies on dynamic principles of brain functions through unifying experimental and computational approaches in cellular, local circuit, global network and behavioral levels. Provides services such as data sets, popular research findings and articles and current developments in field. This site has been archived since FY2019 and is no longer updated.

Proper citation: Dynamic Brain Platform (RRID:SCR_001754) Copy   

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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   

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http://csg.sph.umich.edu//abecasis/MACH/index.html

A Markov Chain based software tool for haplotyping, genotype imputation and disease association analysis that can resolve long haplotypes or infer missing genotypes in samples of unrelated individuals.

Proper citation: MACH 1.0 (RRID:SCR_001759) Copy   

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

An interactive web tool for visualizing differential gene expression data.

Proper citation: Degust (RRID:SCR_001878) Copy   

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http://www.kaust.edu.sa/

Private research university in Thuwal, Saudi Arabia that offers undergraduate and graduate degree programs in Biological and Environmental Science and Engineering (BESE), Computer, Electrical, and Mathematical Science and Engineering (CEMSE), and Physical Science and Engineering (PSE).

Proper citation: King Abdullah University of Science and Technology; Makkah Province; Saudi Arabia (RRID:SCR_001758) 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://www.aamc.org/start.htm

Not-for-profit association representing all 141 accredited U.S. and 17 accredited Canadian medical schools; nearly 400 major teaching hospitals and health systems, including 51 Department of Veterans Affairs medical centers; and 90 academic and scientific societies. Through these institutions and organizations, the AAMC represents 128,000 faculty members, 75,000 medical students, and 110,000 resident physicians. Through its programs and services, it strengthens the world's most advanced medical care by supporting the entire spectrum of education, research, and patient care activities conducted by member institutions. The AAMC and its members are dedicated to the communities they serve and steadfast in their desire to earn and keep the public's trust for the role they play in improving the nation's health. The vision of the AAMC and its members is a healthy nation and world in which: - America's system of medical education, through continual renewal and innovation, prepares physicians and scientists to meet the nation's evolving health needs. - The nation's medical students, biomedical graduate students, residents, fellows, faculty, and the health care workforce are diverse and culturally competent. - Advances in medical knowledge, therapies, and technologies prevent disease, alleviate suffering, and improve quality of life. - The nation's health system meets the needs of all. - Concern for compassion, quality, safety, efficacy, accountability, affordability, professionalism, and the public good guide the health care community.

Proper citation: Association of American Medical Colleges (RRID:SCR_001670) 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
  

https://atcp.org/

The Ataxia Telangiectasia Children's Project, better known as the A-T Children's Project, was founded in late 1993 by a family in Florida with two young sons who have A-T. It is a public, tax-exempt, non-profit organization pursuant to Section 501(c)(3) of the Internal Revenue Code, and all gifts and donations to the Project are tax deductible. The A-T Children's Project was formed to raise funds through events and contributions from corporations, foundations and friends. These funds are then used to accelerate first-rate, international scientific research aimed at finding a cure and improving the lives of all children with ataxia-telangiectasia. - To encourage and support excellent laboratory research which will accelerate the discovery of a cure or possible therapies for ataxia-telangiectasia by: - awarding competitive research grants to top scientists using a peer-review board comprised of top scientists and physicians, - organizing and sponsoring workshops and symposiums in order to encourage cooperation among laboratories and to generate new research strategies, and - working with Congress and the National Institutes of Health to encourage the funding of active research on A-T by agencies of the U.S. government. - To improve the accurate and timely diagnosis of A-T patients by increasing public awareness and by educating physicians. - To develop and maintain an international patient registry of A-T patients with objective, neutral oversight, while leaving ultimate control in the hands of treating physicians, so that up-to-date clinical information about A-T patients can be obtained for researchers and so that when a treatment is developed, all patients can be reached through their physicians. - To support and oversee a clinical center and information clearinghouse at a top-rated, world-class medical center for the evaluation of A-T patients by a multidisciplinary team of specialists, and for the accumulation of experience in managing the many facets of A-T such as the ataxia, cancer and immune problems. - To develop quantitative endpoints for objectively measuring the progression rate and severity of the symptoms of A-T. - To maintain and enlarge a tissue/cell bank with objective, neutral oversight and control in order to ensure free access of existing and new researchers to A-T patient specimens. Sponsors: The A-T Children's Project is a non-profit organization that raises funds to support and coordinate first-rate biomedical research projects, scientific conferences and a clinical center aimed at finding a cure or life-improving therapies for ataxia-telangiectasia, a lethal genetic disease that attacks children, causing progressive loss of muscle control, immune system problems, and a strikingly high rate of cancer, especially leukemia and lymphoma.

Proper citation: Ataxia-Telangiectasia Childrens Project (RRID:SCR_001671) Copy   

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http://ahd.cbi.pku.edu.cn

Database providing a systematic and comprehensive view of morphological phenotypes regulated by plant hormones, as well as regulatory genes participating in numerous plant hormone responses. By integrating the data from mutant studies, transgenic analysis and gene ontology annotation, genes related to the stimulus of eight plant hormones were identified, including abscisic acid, auxin, brassinosteroid, cytokinin, ethylene, gibberellin, jasmonic acid and salicylic acid. Another pronounced characteristics of this database is that a phenotype ontology was developed to precisely describe all kinds of morphological processes regulated by plant hormones with standardized vocabularies. To increase the coverage of phytohormone related genes, the database has been updated from AHD to AHD2.0 adding and integrating several pronounced features: (1) added 291 newly published Arabidopsis hormone related genes as well as corrected information (e.g. the arguable ABA receptors) based on the recent 2-year literature; (2) integrated orthologues of sequenced plants in OrthoMCLDB into each gene in the database; (3) integrated predicted miRNA splicing site in each gene in the database; (4) provided genetic relationship of these phytohormone related genes mining from literature, which represents the first effort to construct a relatively comprehensive and complex network of hormone related genes as shown in the home page of our database; (5) In convenience to in-time bioinformatics analysis, they also provided links to a powerful online analysis platform Weblab that they have recently developed, which will allow users to readily perform various sequence analysis with these phytohormone related genes retrieved from AHD2.0; (6) provided links to other protein databases as well as more expression profiling information that would facilitate users for a more systematic analysis related to phytohormone research. Please help to improve the database with your contributions.

Proper citation: Arabidopsis Hormone Database (RRID:SCR_001792) Copy   

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https://github.com/hms-dbmi/spp

R analysis and processing package for Illumina platform Chip-Seq data.

Proper citation: SPP (RRID:SCR_001790) Copy   

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http://www.myexperiment.org/

Community repository and virtual research environment where scientists can safely publish their workflows and experiment plans, share them with groups and find and use those of others. Workflows, other digital objects and collections (called Packs) can be swapped, sorted and searched. It supports Linked data, has a SPARQL Endpoint and REST API and is based on an open source Ruby on Rails codebase. Scientific workflows in various formats can be uploaded. Specific support is provided for Taverna workflows for which the system displays relevant metadata, components and visual previews, that are retrieved directly from workflow files. Version history for workflows is collected. This feature allows the contributor to keep previous versions of the workflow available, when the latest one is uploaded. This brings additional benefit for the users by allowing them to view the development stages of the workflow towards its latest implementation.

Proper citation: myExperiment (RRID:SCR_001795) Copy   

•   Source: SciCrunch Registry
  

http://www.genes2cognition.org/resources/

Biological resources, including gene-targeting vectors, ES cell lines, antibodies, and transgenic mice, generated for its phenotyping pipeline as part of the Genes to Cognition research program are freely-available to interested researchers. Available Transgenic Mouse Lines: *Hras1 (H-ras) knockout,C57BL/6J *Dlg4 (PSD-95) knockout,129S5 *Dlg4 (PSD-95) knockout,C57BL/6J *Dlg3 (SAP102) knockout with hprt mutation,129S5 *Dlg3 (SAP102) knockout (wild-type for hprt,C57BL/6J *Syngap1 (SynGAP) knockout (from 8.24 clone), C57BL/6J *Dlg4 (PSD-95) guanylate kinase domain deletion, C57BL/6J *Ptk2 (FAK) knockout,C57BL/6J

Proper citation: Genes to Cognition - Biological Resources (RRID:SCR_001675) Copy   

•   Source: SciCrunch Registry
  

https://github.com/JialiUMassWengLab/TEMP

Software package for detecting transposable elements (TEs) insertions and excisions from pooled high-throughput sequencing data.

Proper citation: TEMP (RRID:SCR_001788) Copy   

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http://www.ncbi.nlm.nih.gov/projects/homology/maps/

This page provides quick access to the Comparative mapping functions available in the Map Viewer. Currently, comparative maps are calculated using HomoloGene orthology predictions. Once the gene pairs have been established, blocks of conserved syteny can be established using the positions of each gene object in their respective builds. Sponsors: This resource is supported by NCBI.

Proper citation: Homology Maps Page (RRID:SCR_001666) Copy   

•   Source: SciCrunch Registry