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https://datacommons.cancer.gov

Cloud based data science infrastructure that provides secure access to cancer research data from NCI programs and key external cancer programs. Serves as coordinated resource for public data sharing of NCI funded programs. Users can explore and use analytical and visualization tools for data analysis. Enables to search and aggregate data across repositories including Cancer Data Service, Clinical Trial Data Commons, Genomic Data Commons, Imaging Data Commons, Integrated Canine Data Commons, Proteomic Data Commons.

Proper citation: Cancer Research Data Commons (RRID:SCR_019128) Copy   

•   Source: SciCrunch Registry

https://www.fdilab.org

UCSD based bioinformatics lab composed of several projects in different biomedical disciplines. Established in 2008 as Neuroscience Information Framework and has since expanded to include broader field of biomedical research. Leader in developing and providing novel informatics infrastructure and tools for making data FAIR: Findable, Accessible, Interoperable and Reusable. FAIR Data informatics laboratory develops SciCrunch.org platform.

Proper citation: FAIR Data Informatics Laboratory (RRID:SCR_019235) Copy   

•   Source: SciCrunch Registry

https://my.locuszoom.org

Web tool to investigate genome wide association results in their local genomic context. Adds new features to LocusZoom such as Manhattan plots, annotation options, and calculations that put findings in context. Used for interactive and embeddable visualization of genetic association study results.Javascript/d3 embeddable plugin for interactively visualizing statistical genetic data from customizable sources.

Proper citation: LocusZoom.org (RRID:SCR_021374) Copy   

•   Source: SciCrunch Registry

http://dockground.bioinformatics.ku.edu/

Data sets, tools and computational techniques for modeling of protein interactions, including docking benchmarks, docking decoys and docking templates. Adequate computational techniques for modeling of protein interactions are important because of the growing number of known protein 3D structures, particularly in the context of structural genomics. The first release of the DOCKGROUND resource (Douguet et al., Bioinformatics 2006; 22:2612-2618) implemented a comprehensive database of cocrystallized (bound) protein-protein complexes in a relational database of annotated structures. Additional releases added features to the set of bound structures, such as regularly updated downloadable datasets: automatically generated nonredundant set, built according to most common criteria, and a manually curated set that includes only biological nonobligate complexes along with a number of additional useful characteristics. Also included are unbound (experimental and simulated) protein-protein complexes. Complexes from the bound dataset are used to identify crystallized unbound analogs. If such analogs do not exist, the unbound structures are simulated by rotamer library optimization. Thus, the database contains comprehensive sets of complexes suitable for large scale benchmarking of docking algorithms. Advanced methodologies for simulating unbound conformations are being explored for the next release. The Dockground project is developed by the Vakser lab at the Center for Bioinformatics at the University of Kansas. Parts of Dockground were co-developed by Dominique Douguet from the Center of Structural Biochemistry (INSERM U554 - CNRS UMR5048), Montpellier, France.

Proper citation: Dockground: Benchmarks, Docoys, Templates, and other knowledge resources for DOCKING (RRID:SCR_007412) Copy   

•   Source: SciCrunch Registry

https://cosmiic.org/

Open source neurostimulation and recording hardware instrument platform. Part of the SPARC project. COSMIIC is based on the Networked Neuroprosthesis developed at Case Western Reserve University.

Proper citation: COSMIIC HORNET (RRID:SCR_023679) Copy   

•   Source: SciCrunch Registry

http://www.nlm.nih.gov/NIHbmic/nih_data_sharing_repositories.html

A listing of NIH supported data sharing repositories that make data accessible for reuse. Most accept submissions of appropriate data from NIH-funded investigators (and others), but some restrict data submission to only those researchers involved in a specific research network. Also included are resources that aggregate information about biomedical data and information sharing systems. The table can be sorted according by name and by NIH Institute or Center and may be searched using keywords so that you can find repositories more relevant to your data. Links are provided to information about submitting data to and accessing data from the listed repositories. Additional information about the repositories and points-of-contact for further information or inquiries can be found on the websites of the individual repositories.

Proper citation: NIH Data Sharing Repositories (RRID:SCR_003551) Copy   

•   Source: SciCrunch Registry

https://metagenote.niaid.nih.gov/

Quick and intuitive way to annotate data from genomics studies including microbiome. Project to aid researchers in applying standardized metadata describing what, where, how, and when of samples collected in genomics study. Collection of METAdata of GEnomics studies on web based NOTEbook. Metadata are stored in centralized repository and validated according to guidelines from Genomics Standard Consortium, which are also supported by repositories and large microbiome initiatives such as NCBI, European Bioinformatics Institute (EBI), and Earth Microbiome Project. Upon request from researchers, data will also be submitted for publication via NCBI Sequence Read Archive (SRA) repository.

Proper citation: METAGENOTE (RRID:SCR_018494) Copy   

•   Source: SciCrunch Registry

https://commonfund.nih.gov/hmp/

NIH Project to generate resources to characterize the human microbiota and to analyze its role in human health and disease at several different sites on the human body, including nasal passages, oral cavities, skin, gastrointestinal tract, and urogenital tract using metagenomic and traditional approach to genomic DNA sequencing studies.HMP was supported by the Common Fund from 2007 to 2016.

Proper citation: Human Microbiome Project (RRID:SCR_012956) Copy   

•   Source: SciCrunch Registry

https://github.com/YuanXue1993/SegAN

Image analysis software for medical image segmentation. The software is fueled by an end-to-end adversarial neural network that generates segmentation label maps.

Proper citation: SegAN (RRID:SCR_016215) Copy   

•   Source: SciCrunch Registry

http://nhprtr.org/

Nonhuman Primate reference transcriptome resource consisting of deep sequencing complete transcriptomes (RNA-seq) from multiple NHP species.

Proper citation: Nonhuman Primate Reference Transcriptome Resource (RRID:SCR_017534) Copy   

•   Source: SciCrunch Registry

https://cic.ini.usc.edu/

Project to create complete mesoscale connectivity atlas of the C57Black/6 mouse brain and to subsequently generate its global neural networks.

Proper citation: Mouse Connectome Project (RRID:SCR_017313) Copy   

•   Source: SciCrunch Registry

https://mibig.secondarymetabolites.org/

MIBiG is genomic standards consortium project and biosynthetic gene cluster database used as reference dataset. Provides community standard for annotations and metadata on biosynthetic gene clusters and their molecular products. Standardised data format that describes minimally required information to uniquely characterise biosynthetic gene clusters. MIBiG 2.0 is expended repository for biosynthetic gene clusters of known function. MIBiG 3.0 is database update comprising large scale validation and re-annotation of existing entries and new entries. Community driven effort to annotate experimentally validated biosynthetic gene clusters.

Proper citation: Minimum Information about Biosynthetic Gene cluster (RRID:SCR_023660) Copy   

•   Source: SciCrunch Registry

http://www.midasplatform.org/

Open-source toolkit that enables the rapid creation of tailored, web-enabled data storage and provides a cohesive system for data management, visualization, and processing. At its core, Midas Platform is implemented as a PHP modular framework with a backend database (PostGreSQL, MySQL and non-relational databases). While the Midas Platform system can be installed and deployed without any customization, the framework has been designed with customization in mind. As building one system to fit all is not optimal, the framework has been extended to support plugins and layouts. Through integration with a range of other open-source toolkits, applications, or internal proprietary workflows, Midas Platform offers a solid foundation to meet the needs of data-centric computing. Midas Platform provides a variety of data access methods, including web, file system and DICOM server interfaces, and facilitates extending the methods in which data is stored to other relational and non-relational databases.

Proper citation: Midas Platform (RRID:SCR_002186) Copy   

•   Source: SciCrunch Registry

http://www.sph.umich.edu/csg/abecasis/CaTS

Software tool for carrying out power calculations for large genetic association studies, including two stage genome wide association studies.

Proper citation: Calculator for Association with Two Stage design (RRID:SCR_007238) Copy   

•   Source: SciCrunch Registry

http://compbio.cs.princeton.edu/conservation/

Software for scoring protein sequence conservation using the Jensen-Shannon divergence. It can be used to predict catalytic sites and residues near bound ligands.

Proper citation: Conservation (RRID:SCR_016064) Copy   

•   Source: SciCrunch Registry

https://github.com/marbl/Mash

Software tool for genome and metagenome distance estimation using MinHash. Reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated.

Proper citation: Mash (RRID:SCR_019135) Copy   

•   Source: SciCrunch Registry

http://science.education.nih.gov/SciEdBlog

A blog put out by the NIH Office of Science Education.

Proper citation: NIH SciEd Blog (RRID:SCR_005499) Copy   

•   Source: SciCrunch Registry

http://llama.mshri.on.ca/funcassociate/

A web-based tool that accepts as input a list of genes, and returns a list of GO attributes that are over- (or under-) represented among the genes in the input list. Only those over- (or under-) representations that are statistically significant, after correcting for multiple hypotheses testing, are reported. Currently 37 organisms are supported. In addition to the input list of genes, users may specify a) whether this list should be regarded as ordered or unordered; b) the universe of genes to be considered by FuncAssociate; c) whether to report over-, or under-represented attributes, or both; and d) the p-value cutoff. A new version of FuncAssociate supports a wider range of naming schemes for input genes, and uses more frequently updated GO associations. However, some features of the original version, such as sorting by LOD or the option to see the gene-attribute table, are not yet implemented. Platform: Online tool

Proper citation: FuncAssociate: The Gene Set Functionator (RRID:SCR_005768) Copy   

•   Source: SciCrunch Registry

http://crcview.hegroup.org/

Web-based microarray data analysis and visualization system powered by CRC, or Chinese Restaurant cluster, a Dirichlet process model-based clustering algorithm recently developed by Dr. Steve Qin. It also incorporates several gene expression analysis programs from Bioconductor, including GOStats, genefilter, and Heatplus. CRCView also installs from the Bioconductor system 78 annotation libraries of microarray chips for human (31), mouse (24), rat (14), zebrafish (1), chicken (1), Drosophila (3), Arabidopsis (2), Caenorhabditis elegans (1), and Xenopus Laevis (1). CRCView allows flexible input data format, automated model-based CRC clustering analysis, rich graphical illustration, and integrated Gene Ontology (GO)-based gene enrichment for efficient annotation and interpretation of clustering results. CRC has the following features comparing to other clustering tools: 1) able to infer number of clusters, 2) able to cluster genes displaying time-shifted and/or inverted correlations, 3) able to tolerate missing genotype data and 4) provide confidence measure for clusters generated. You need to register for an account in the system to store your data and analyses. The data and results can be visited again anytime you log in.

Proper citation: CRCView (RRID:SCR_007092) Copy   

•   Source: SciCrunch Registry

http://www.sb.cs.cmu.edu/drem

The Dynamic Regulatory Events Miner (DREM) allows one to model, analyze, and visualize transcriptional gene regulation dynamics. The method of DREM takes as input time series gene expression data and static transcription factor-gene interaction data (e.g. ChIP-chip data), and produces as output a dynamic regulatory map. The dynamic regulatory map highlights major bifurcation events in the time series expression data and transcription factors potentially responsible for them. DREM 2.0 was released and supports a number of new features including: * new static binding data for mouse, human, D. melanogaster, A. thaliana * a new and more flexible implementation of the IOHMM supports dynamic binding data for each time point or as a mix of static/dynamic TF input * expression levels of TFs can be used to improve the models learned by DREM * the motif finder DECOD can be used in conjuction with DREM and help find DNA motifs for unannotated splits * new features for the visualization of expressed TFs, dragging boxes in the model view, and switching between representations

Proper citation: Dynamic Regulatory Events Miner (RRID:SCR_003080) Copy   

•   Source: SciCrunch Registry