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https://github.com/aidenlab/juicer.git

Software platform for analyzing kilobase resolution Hi-C data. Open source tool for analyzing terabase scale Hi-C datasets. Allowes to transform raw sequence data into normalized contact maps.

Proper citation: Juicer (RRID:SCR_017226) Copy   

•   Source: SciCrunch Registry

http://www.datamonkey.org/

Web-based suite of phylogenetic analysis tools for use in evolutionary biology. Web application for comparative analysis of sequence alignments using statistical models. Used for analyzing evolutionary signatures in sequence data. Datamonkey 2.0 provides curated collection of methods for interrogating coding-sequence alignments for imprints of natural selection, packaged as a responsive (i.e. can be viewed on tablet and mobile devices), fully interactive, and API-enabled web application.

Proper citation: Datamonkey (RRID:SCR_010278) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/InterNano

A custom-built terminology to describe the nanomanufacturing enterprise.

Proper citation: InterNano Nanomanufacturing Taxonomy (RRID:SCR_010346) Copy   

•   Source: SciCrunch Registry

http://www.aoncadis.org

A repository and data management services for Arctic research data. Data include long-term observational timeseries, local, regional, and system-scale research from many diverse domains.

Proper citation: ACADIS Gateway (RRID:SCR_010473) Copy   

•   Source: SciCrunch Registry

http://evolution.genetics.washington.edu/phylip/software.html

392 phylogeny software packages and 54 free web servers describing all known software for inferring phylogenies (evolutionary trees). Submissions are welcome. Programs are listed by methods available, by computer systems on which they work, cross-referenced by method and by computer system, by ones which analyze particular kinds of data, to show the most recent listings, or to show ones most recently changed.

Proper citation: Phylogeny Programs (RRID:SCR_010797) Copy   

•   Source: SciCrunch Registry

http://casp.rnet.missouri.edu/soydb/

A Knowledge Database of Soybean Transcription Factors. PSI-BLAST is available to find hits from the database.

Proper citation: SoyDB (RRID:SCR_010900) Copy   

•   Source: SciCrunch Registry

http://opm.phar.umich.edu/

Database that provides a collection of transmembrane, monotopic and peripheral proteins from the Protein Data Bank whose spatial arrangements in the lipid bilayer have been calculated theoretically and compared with experimental data. The database allows analysis, sorting and searching of membrane proteins based on their structural classification, species, destination membrane, numbers of transmembrane segments and subunits, numbers of secondary structures and the calculated hydrophobic thickness or tilt angle with respect to the bilayer normal.

Proper citation: Orientations of Proteins in Membranes database (RRID:SCR_011961) Copy   

•   Source: SciCrunch Registry

http://cs.nyu.edu/~bingsun/

NYU Bioinformatics group applies algorithmic, statistical, and mathematical techniques to solve problems of interest to biology, biotechnology and biomedicine. The group focuses on bioinformatics, computational biology and systems biology with many active projects in areas ranging from single molecules to entire populations: Analysis of Single-Molecule/Single-Cell Data, SPM-based Transcriptomic Profiling, Whole-Genome Haplotype Sequencing using SMASH (Single Molecule Approaches to Haplotype Sequencing), SUTTA (Scoring and Unfolding Trimmed Tree Assembler) assembly algorithm, Analysis of Spatio-Temporal Data, Model Checking and Model Building for Systems Biology, GOALIE-based Phenomenological Models and their Verification, Causality Analysis, Causal Models and their Verification, Analysis of EHR (Electronic Health Record Data) and Disease Models (e.g., Chronic Fatigue Syndrome, Congestive Heart Failure, Deep Vein Thrombosis, etc.), Models of Cancer, Applications to Pancreatic Cancer, Polymorphisms and Biomarkers, Strategies for Group Testing, Epidemiological and Bio-Warfare Models, Planning with Large Agent Networks against Catastrophes (PLAN C), Population Genomics, and Genome Wide Association Studies (GWAS). The group has received its funding from Air Force, Army, CCPR, DARPA, NIH, NIST, NSF, NYSTAR, etc. and various other governmental and commercial entities. Currently, the group is part of an NSF funded Expedition in Computing project (CMACS: Center for Modeling and Analysis of Complex Systems at CMU) and collaborates widely, both nationally and internationally. The group is highly multi-disciplinary, attracting researchers and students from mathematics, statistics, computer science, and biology who team up with physicians, physicists, and chemists as well as professionals in their own disciplines. This group is led by Prof. Bud Mishra, a professor of computer science and mathematics at NYU''s Courant Institute of Mathematical Sciences.

Proper citation: NYU Bioinformatics Group (RRID:SCR_005697) Copy   

•   Source: SciCrunch Registry

https://datadryad.org

International, curated, digital repository that makes the data underlying scientific publications discoverable, freely reusable, and citable. Particularly data for which no specialized repository exists. Provides the infrastructure for, and promotes the re-use of, data underlying the scholarly literature. Governed by a nonprofit membership organization. Membership is open to any stakeholder organization, including but not limited to journals, scientific societies, publishers, research institutions, libraries, and funding organizations. Most data are associated with peer-reviewed articles, although data associated with non-peer reviewed publications from reputable academic sources, such as dissertations, are also accepted. Used to validate published findings, explore new analysis methodologies, repurpose data for research questions unanticipated by the original authors, and perform synthetic studies.UC system is member organization of Dryad general subject data repository.

Proper citation: Dryad Digital Repository (RRID:SCR_005910) Copy   

•   Source: SciCrunch Registry

http://martinos.org/mne/

Software suite for processing magnetoencephalography and electroencephalography data. Open source Python software for exploring, visualizing, and analyzing human neurophysiological data including MEG, EEG, sEEG, ECoG . Implements all functionality of MNE Matlab tools in Python and extends capabilities of MNE Matlab tools to, e.g., frequency-domain and time-frequency analyses and non-parametric statistics.

Proper citation: MNE software (RRID:SCR_005972) Copy   

•   Source: SciCrunch Registry

http://www.mitre.org/news/digest/archives/2002/neuroinformatics.html

This resource''s long-term goal is to develop informatics methodologies and tools that will increase the creativity and productivity of neuroscience investigators, as they work together to use shared human brain mapping data to generate and test ideas far beyond those pursued by the data''s originators. This resource currently has four major projects supporting this goal: * Database tools: The goal of the NeuroServ project is to provide neuroscience researchers with automated information management tools that reduce the effort required to manage, analyze, query, view, and share their imaging data. It currently manages both structural magnetic resonance image (MRI) datasets and diffusion tensor image (DTI) datasets. NeuroServ is fully web-enabled: data entry, query, processing, reporting, and administrative functions are performed by qualified users through a web browser. It can be used as a local laboratory repository, to share data on the web, or to support a large distributed consortium. NeuroServ is based on an industrial-quality query middleware engine MRALD. NeuroServ includes a specialized neuroimaging schema and over 40 custom Java Server Pages supporting data entry, query, and reporting to help manage and explore stored images. NeuroServ is written in Java for platform independence; it also utilizes several open source components * Data sharing: DataQuest is a collaborative forum to facilitate the sharing of neuroimaging data within the neuroscience community. By publishing summaries of existing datasets, DataQuest enables researchers to: # Discover what data is available for collaborative research # Advertise your data to other researchers for potential collaborations # Discover which researchers may have the data you need # Discover which researchers are interested in your data. * Image quality: The approach to assessing the inherent quality of an image is to measure how distorted the image is. Using what are referred to as no-reference or blind metrics, one can measure the degree to which an image is distorted. * Content-based image retrieval: NIRV (NeuroImagery Retrieval & Visualization) is a work environment for advanced querying over imagery. NIRV will have a Java-based front-end for users to issue queries, run processing algorithms, review results, visualize imagery and assess image quality. NIRV interacts with an image repository such as NeuroServ. Users can also register images and will soon be able to filter searches based on image quality.

Proper citation: MITRE Neuroinformatics (RRID:SCR_006508) Copy   

•   Source: SciCrunch Registry

http://www.maizegdb.org

Collection of data related to crop plant and model organism Zea mays. Used to synthesize, display, and provide access to maize genomics and genetics data, prioritizing mutant and phenotype data and tools, structural and genetic map sets, and gene models and to provide support services to the community of maize researchers. Data stored at MaizeGDB was inherited from the MaizeDB and ZmDB projects. Sequence data are from GenBank. Data are searchable by phenotype, traits, Pests, Gel Pattern, and Mutant Images.

Proper citation: MaizeGDB (RRID:SCR_006600) Copy   

•   Source: SciCrunch Registry

http://wiki.chasmsoftware.org/index.php/Main_Page

CHASM is a method that predicts the functional significance of somatic missense mutations observed in the genomes of cancer cells, allowing mutations to be prioritized in subsequent functional studies, based on the probability that they give the cells a selective survival advantage. SNV-Box is a database of pre-computed features of all possible amino acid substitutions at every position of the annotated human exome. Users can rapidly retrieve features for a given protein amino acid substitution for use in machine learning.

Proper citation: CHASM/SNV-Box (RRID:SCR_006445) Copy   

•   Source: SciCrunch Registry

https://sites.google.com/site/functionalconnectivitytoolbox/

MATLAB toolbox for performing functional connectivity analyses includes many of the most commonly-used approaches researchers have utilized to date for the identification of condition-dependent functional interactions between fMRI time-series obtained from two or more brain regions. The approaches are either bivariate or multivariate methods defined in time or frequency domains that emphasize distinct features of relationships among the time-series.

Proper citation: Functional Connectivity Toolbox (RRID:SCR_006394) Copy   

•   Source: SciCrunch Registry

http://flybase.org/

Database of Drosophila genetic and genomic information with information about stock collections and fly genetic tools. Gene Ontology (GO) terms are used to describe three attributes of wild-type gene products: their molecular function, the biological processes in which they play a role, and their subcellular location. Additionally, FlyBase accepts data submissions. FlyBase can be searched for genes, alleles, aberrations and other genetic objects, phenotypes, sequences, stocks, images and movies, controlled terms, and Drosophila researchers using the tools available from the "Tools" drop-down menu in the Navigation bar.

Proper citation: FlyBase (RRID:SCR_006549) Copy   

•   Source: SciCrunch Registry

http://songbirdtranscriptome.net/

Database containing cDNA clone information of the brains of songbirds. These clones are annotated with behavioral information, as well as links to information of homologous genes of other species. The database includes over 91,000 zebra finch brain cDNAs (2009) sequenced by Duke, ESTIMA, and Rockefeller research groups. The project is a collaborative effort of the Jarvis Laboratory of Duke University, Duke Bioinformatics, and The Genomics group of RIKEN, with Erich D. Jarvis as P.I. and Kazuhiro Wada as Co-P.I. Microarrays with the cDNAs in this database are available at Duke http://mgm.duke.edu/genome/dna_micro/core/spotted.htm and through the NIH Neurosciences Microarray Consortium http://arrayconsortium.tgen.org/np2/public/overview.jsp

Proper citation: Songbird Brain Transcriptome Database (RRID:SCR_006182) Copy   

•   Source: SciCrunch Registry

http://divvy.ucsd.edu/

Software application for performing unsupervised machine learning and visualization with a focus on the clustering (separating data into groups) and dimensionality reduction (finding low dimensional structure in high dimensional data) subfields of machine learning. For visualization we provide support for both the whole dataset (e.g. a scatter plot) and points (e.g. transforming a particular point into an image). * Endlessly extensible. Every clusterer, reducer, point visualizer and dataset visualizer in Divvy is a plugin. We''ve provided a few big ones (K-means, PCA, scatter plot, &c.) and we''re hoping the community will use our plugin protocol to build many more. Each plugin defines its own UI, so your algorithm can look and behave the way that you want it to without top-down constraints. * Have lots of cores? Divvy is both task and data parallel. No longer will you be waiting for one algorithm to complete before you start another. Start as many as you want and keep using the UI. Only started one? With data parallelism we''ll still push your new MacBook Pro to 800% CPU utilization. * Part of your workflow: Export your clusterings and reductions to .csv and your visualizations to .png. Use your Matlab or R data with our Matlab/R to Divvy export tools available at http://github.com/jmlewis/divvy.

Proper citation: Divvy (RRID:SCR_006336) Copy   

•   Source: SciCrunch Registry

http://datastar.mannlib.cornell.edu/

A single library software prototype transitioning to a to an open-source platform ready for adoption and extension at other institutions wishing to provide research data sharing and discovery services. Datastar''''s ability to expose metadata about research datasets in a standard semantic format called Linked Data will be enhanced to support selective interchange of related information with VIVO, an open-source semantic researcher networking tool gaining prominence through adoption at multiple U.S. universities, in the federal government, and internationally.

Proper citation: DataStaR (RRID:SCR_006381) Copy   

•   Source: SciCrunch Registry

http://galaxyproject.org/

Open, web-based platform providing bioinformatics tools and services for data intensive genomic research. Platform may be used as a service or installed locally to perform, reproduce, and share complete analyses. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Community has created Galaxy instances in many different forms and for many different applications including Galaxy servers, cloud services that support Galaxy instances, and virtual machines and containers that can be easily deployed for your own server.The Galaxy team is a part of BX at Penn State, and the Biology and Mathematics and Computer Science departments at Emory University.Training Infrastructure as a Service (TIaaS) is a service offered by some UseGalaxy servers to specifically support training use cases.

Proper citation: Galaxy (RRID:SCR_006281) Copy   

•   Source: SciCrunch Registry

http://criticalzone.org/

Data related to the National Critical Zone Observatory Program including in-situ environmental sensors, field instruments, remote sensing, and surface and subsurface imaging. The Program serves the international scientific community through research, infrastructure, data, and models. They focus on how components of the Critical Zone interact, shape Earth's surface, and support life. A primary goal is to develop high-resolution 4D datasets that inform our theoretical framework, constrain our conceptual and coupled systems models, and test our model-generated hypotheses. They are developing cross-CZO capabilities to easily share, integrate, analyze and preserve the wide range of multi-disciplinary data generated by CZOs.

Proper citation: Critical Zone Observatories (RRID:SCR_002199) Copy   

•   Source: SciCrunch Registry