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SciCrunch Registry is a curated repository of scientific resources, with a focus on biomedical resources, including tools, databases, and core facilities - visit SciCrunch to register your resource.
Community-driven organization that develops and disseminates software for geophysics and related fields. They host codes in a wide range of disciplines in geodynamics and computational science including geodynamo, long-term tectonics, magma migration, mantle dynamics, seismology, and short-term crustal dynamics.
Proper citation: Computational Infrastructure for Geodynamics (RRID:SCR_003371) Copy
http://research.amnh.org/atol/files/
Project whose aim is to produce a robust phylogeny of all the deepest branches within a mega-diverse group, the spiders, by combining a massive amount of newly generated comparative genomic data with a substantial set of new and re-assessed data on morphology and behavior. They propose to collect a huge amount of genomic information in order to test and improve the results achieved by over 50 detailed morphological cladistic analyses conducted by more than 30 investigators during the past 15 years. The insignificant amount of genomic work to date on spiders has been uncoordinated and of little utility for broad-scale phylogenetic investigation. The advent of high-throughput DNA sequencing, however, makes it feasible to examine substantial parts of the genome across a dense sampling of spider taxa. They propose to sequence at least 50 loci (genome samples of 500-1,000 or more base pairs that can be sequenced as single pieces in both directions simultaneously) for representatives of at least 500 genera of spiders and their closest relatives (the whipscorpion orders Amblypygi, Uropygi, and Schizomida). These genera will be carefully selected by a sampling strategy designed to maximize the resolution of deep branches within spider phylogeny, and will purposefully include all the previously most-favored study organisms of ethologists, ecologists, physiologists, and developmental and molecular biologists, thus integrating and contextualizing their research. Data matrices will be produced that combine the new genomic data with a new, comprehensive survey of morphological and behavioral homologies, offering a unique index to all comparative data on one large group. New computer software, designed in large part by members of their group and using massively parallel processing to achieve supercomputing capability, makes such analyses feasible.
Proper citation: Tree of Life: Phylogeny of Spiders (RRID:SCR_003801) Copy
https://www.opensciencedatacloud.org/
Service that provides petabyte-scale cloud resources to analyze, manage, and share scientific data. It is designed to serve medium to large sized research projects by managing and operating a secure cloud computing infrastructure that can be shared across a project. This Science as a Service approach to research saves scientists and their funders valuable time and money. All of the software developed is open source and hosted on GitHub. The OSDC also has 1PB of public data in a wide variety of disciplines. The data sets can downloaded over the internet or high performance networks such as Internet2, as well as computed over directly on the OSDC.
Proper citation: Open Science Data Cloud (RRID:SCR_003523) Copy
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
A web application which provides altmetrics to help researchers measure and share the impacts of their research outputs. After making a profile, scientists can track which of their publications are most popular through number of citations, frequency of PDF downloads, etc. Information from research outputs such as journal articles, blog posts, datasets, and software contribute to a user's impact, which is viewable in their profile.
Proper citation: ImpactStory (RRID:SCR_002632) Copy
Project to create a scalable infrastructure that enables linking phenotypes across different fields of biology by the semantic similarity of their descriptions.
Proper citation: Phenoscape (RRID:SCR_003799) Copy
http://www.cs.cmu.edu/~jernst/stem/
The Short Time-series Expression Miner (STEM) is a Java program for clustering, comparing, and visualizing short time series gene expression data from microarray experiments (~8 time points or fewer). STEM allows researchers to identify significant temporal expression profiles and the genes associated with these profiles and to compare the behavior of these genes across multiple conditions. STEM is fully integrated with the Gene Ontology (GO) database supporting GO category gene enrichment analyses for sets of genes having the same temporal expression pattern. STEM also supports the ability to easily determine and visualize the behavior of genes belonging to a given GO category or user defined gene set, identifying which temporal expression profiles were enriched for these genes. (Note: While STEM is designed primarily to analyze data from short time course experiments it can be used to analyze data from any small set of experiments which can naturally be ordered sequentially including dose response experiments.) Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible
Proper citation: Short Time-series Expression Miner (STEM) (RRID:SCR_005016) Copy
An open-source general packing algorithm that packs 3D objects onto surfaces, into volumes, and around volumes. It provides a general architecture to allow various packing algorithms to interoperate efficiently in the same model. autoPack can incorporate any packing solution into its modular python program architecture, but is currently optimized to provide a novel solution to the loose packing problem which places objects of discrete size into place (compared to advancing front, popcorn, or other fast tight-packing solutions that allow objects to scale to arbitrary masses.) Most popular 3D software programs now contain robust physics engines based on Bullet that can separate small collections of overlapping objects or allow volumes to be filled by pouring shapes from generators, but these approaches fails for large complex systems and result in either overlapping geometry, crashed software, or non-random gradients. Most packing algorithms are designed to position objects as efficiently as possible, but autoPack allows the user to select from random loose packing to highly organized packing methods����??even to choose both methods at the same time. autoPack positions 3D geometries into, onto, and around volumes with minimal to zero overlap. autoPack mixes several packing approaches and procedural growth algorithms. autoPack can thus place objects with forces and constraints to allow a high degree of control ranging from completely random distributions to highly ordered structures. * zero to minimal overlaps depending on the method used * accuracy vs speed parameters selected by the user * zero edge effects * complete control, from fully random to fully ordered distributions * agent-based interaction, weighting, and collision control
Proper citation: Autopack (RRID:SCR_006830) Copy
Natural history collections. Offers a stable, straightforward and flexible framework for biodiversity data. Community-developed biodiversity data standard. It includes a glossary of terms (in other contexts these might be called properties, elements, fields, columns, attributes, or concepts) intended to facilitate the sharing of information about biological diversity by providing identifiers, labels, and definitions. Darwin Core is primarily based on taxa, their occurrence in nature as documented by observations, specimens, samples, and related information.
Proper citation: Darwin Core (RRID:SCR_016778) Copy
Web tool to search multiple public variant databases simultaneously and provide a unified interface to facilitate the search process. Used for integration of human and model organism genetic resources to facilitate functional annotation of the human genome. Used for analysis of human genes and variants by cross-disciplinary integration of records available in public databases to facilitate clinical diagnosis and basic research.
Proper citation: MARRVEL (RRID:SCR_016871) Copy
https://github.com/yarden/MISO/blob/fastmiso/docs/source/sashimi.rst
Software tool for quantitative visualization of aligned RNA-Seq reads that enables quantitative comparison of exon usage across samples or experimental conditions.
Proper citation: Sashimiplot (RRID:SCR_016861) Copy
https://github.com/schatzlab/genomescope
Open source software package for fast genome analysis from unassembled short reads. Used to estimate genome heterozygosity, repeat content, and size from sequencing reads using a kmer-based statistical approach.
Proper citation: GenomeScope (RRID:SCR_017014) Copy
http://pklab.med.harvard.edu/scde/pagoda.links.html
Software tool for analyzing transcriptional heterogeneity to detect statistically significant ways in which measured cells can be classified. Used to resolve multiple, potentially overlapping aspects of transcriptional heterogeneity by testing gene sets for coordinated variability among measured cells.
Proper citation: PAGODA (RRID:SCR_017099) Copy
Portal to provide integrated and federated system that interconnects diverse microbiome data sets, bioinformatics tools, and community resources. Built on guiding principles for FAIR data. Open source, community driven microbiome data marketplace and tool exchange for users to integrate their own data and tools with broader community. Partners with CyVerse and XSEDE.
Proper citation: iMicrobe (RRID:SCR_017108) Copy
https://combine-lab.github.io/salmon/
Software tool for quantifying expression of transcripts using RNA-seq data. Provides fast and bias-aware quantification of transcript expression. Transcriptome-wide quantifier to correct for fragment GC-content bias.
Proper citation: Salmon (RRID:SCR_017036) Copy
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
http://www.dibsvis.com/biowheel
Software tool for interactive graphs of high dimensional data. Interactive cloud based software tool by DIBSVIS.
Proper citation: BioWheel (RRID:SCR_017441) Copy
https://www.mesquiteproject.org/
Software tool as modular system for evolutionary analysis. Software for evolutionary biology, designed to organize and analyze comparative data about organisms. Its emphasis is on phylogenetic analysis, but some of its modules concern population genetics, while others do non-phylogenetic multivariate analysis. Analyses available depend on modules installed. Comes with many packages already installed.
Proper citation: Mesquite (RRID:SCR_017994) Copy
Free cloud platform for secure neuroscience data analysis. Allows to manage data, processing and results, sharing projects privately with collaborators or publicly with brainlife.io community.Promotes engagement and education in reproducible neuroscience.You can share your neuroimaging data publicly or privately. Data on brainlife.io is organized as Datatypes to allow interoperability between Apps.
Proper citation: brainlife (RRID:SCR_020940) Copy
http://code.google.com/p/amap-align/
Source code that performs multiple alignment of peptidic sequences. It utilizes posterior decoding and a sequence-annealing alignment, instead of the traditional progressive alignment method.
Proper citation: AMAP (RRID:SCR_015969) Copy
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