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https://www.objtables.org

Software toolkit for creating reusable datasets that are both human and machine readable, combining spreadsheets with schemas including classes, their attributes, type of each attribute, and possible relationships between instances of classes.Consists of format for describing schemas for spreadsheets, numerous data types for science, syntax for indicating class and attribute represented by each table and column in workbook, and software for using schemas to rigorously validate, merge, split, compare, and revision datasets. Used for supplementary materials of journal article, as well as for emerging domains which need to quickly build new formats for new types of data and associated software with minimal effort.

Proper citation: ObjTables (RRID:SCR_018652) Copy   

•   Source: SciCrunch Registry

http://www.ccb.jhu.edu/software/centrifuge/

Software for rapid and sensitive classification of metagenomic sequences. Used for the classification of DNA sequences from microbial samples and analysis of large metagenomics data sets on conventional desktop computers.

Proper citation: Centrifuge Classifier (RRID:SCR_016665) Copy   

•   Source: SciCrunch Registry

https://github.com/iychoi/libra

Hadoop based tool for massive comparative metagenomics analysis. Compute the similarity between metagenomic samples.

Proper citation: Libra (RRID:SCR_016608) Copy   

•   Source: SciCrunch Registry

http://zhoulab.usc.edu/TopDom/

Software tool to identify Topological Domains, which are basic builiding blocks of genome structure. Detects topological domains in a linear time., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: TopDom (RRID:SCR_016964) Copy   

•   Source: SciCrunch Registry

https://picrust.github.io/picrust/

Software package to predict metagenome functional content from marker gene (e.g., 16S rRNA) surveys and full genomes. Used to predict which gene families are present and then combines gene families to estimate the composite metagenome.

Proper citation: PICRUSt (RRID:SCR_016855) Copy   

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https://github.com/macmanes-lab/BinPacker/blob/master/README

Software tool as de novo trascriptome assembler for RNA-Seq data. Used to assemble full length transcripts by remodeling problem as tracking set of trajectories of items over splicing graph. Input RNA-Seq reads in fasta or fastq format, and ouput all assembled candidate transcripts in fasta format. Operating system Unix/Linux.

Proper citation: BinPacker (RRID:SCR_017038) Copy   

•   Source: SciCrunch Registry

http://yogo.msu.montana.edu/

A set of software tools created to rapidly build scientific data-management applications. These applications will enhance the process of data annotation, analysis, and web publication. The system provides a set of easy-to-use software tools for data sharing by the scientific community. It enables researchers to build their own custom-designed data management systems. The problem of scientific data management rests on several challenges. These include flexible data storage, a way to share the stored data, tools to curate the data, and history of the data to show provenance. The Yogo Framework gives you the ability to build scientific data management applications that address all of these challenges. The Yogo software is being developed as part of the NeuroSys project. All tools created as part of the Yogo Data Management Framework are open source and released under an OSI approved license.

Proper citation: Yogo Data Management System (RRID:SCR_004239) Copy   

•   Source: SciCrunch Registry

http://openconnectomeproject.org/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 9, 2023. Connectomes repository to facilitate the analysis of connectome data by providing a unified front for connectomics research. With a focus on Electron Microscopy (EM) data and various forms of Magnetic Resonance (MR) data, the project aims to make state-of-the-art neuroscience open to anybody with computer access, regardless of knowledge, training, background, etc. Open science means open to view, play, analyze, contribute, anything. Access to high resolution neuroanatomical images that can be used to explore connectomes and programmatic access to this data for human and machine annotation are provided, with a long-term goal of reconstructing the neural circuits comprising an entire brain. This project aims to bring the most state-of-the-art scientific data in the world to the hands of anybody with internet access, so collectively, we can begin to unravel connectomes. Services: * Data Hosting - Their Bruster (brain-cluster) is large enough to store nearly any modern connectome data set. Contact them to make your data available to others for any purpose, including gaining access to state-of-the-art analysis and machine vision pipelines. * Web Viewing - Collaborative Annotation Toolkit for Massive Amounts of Image Data (CATMAID) is designed to navigate, share and collaboratively annotate massive image data sets of biological specimens. The interface is inspired by Google Maps, enhanced to allow the exploration of 3D image data. View the fork of the code or go directly to view the data. * Volume Cutout Service - RESTful API that enables you to select any arbitrary volume of the 3d database (3ddb), and receive a link to download an HDF5 file (for matlab, C, C++, or C#) or a NumPy pickle (for python). Use some other programming language? Just let them know. * Annotation Database - Spatially co-registered volumetric annotations are compactly stored for efficient queries such as: find all synapses, or which neurons synapse onto this one. Create your own annotations or browse others. *Sample Downloads - In addition to being able to select arbitrary downloads from the datasets, they have also collected a few choice volumes of interest. * Volume Viewer - A web and GPU enabled stand-alone app for viewing volumes at arbitrary cutting planes and zoom levels. The code and program can be downloaded. * Machine Vision Pipeline - They are building a machine vision pipeline that pulls volumes from the 3ddb and outputs neural circuits. - a work in progress. As soon as we have a stable version, it will be released. * Mr. Cap - The Magnetic Resonance Connectome Automated Pipeline (Mr. Cap) is built on JIST/MIPAV for high-throughput estimation of connectomes from diffusion and structural imaging data. * Graph Invariant Computation - Upload your graphs or streamlines, and download some invariants. * iPad App - WholeSlide is an iPad app that accesses utilizes our open data and API to serve images on the go.

Proper citation: Open Connectome Project (RRID:SCR_004232) Copy   

•   Source: SciCrunch Registry

http://www.cs.gsu.edu/~serghei/?q=drut

Software for Discovery and Reconstruction of Unannotated Transcripts in Partially Annotated Genomes from High-Throughput RNA-Seq Data.

Proper citation: DRUT (RRID:SCR_004351) Copy   

•   Source: SciCrunch Registry

http://www.digimorph.org/

A dynamic archive of information on digital morphology and high-resolution X-ray computed tomography of biological specimens serving imagery for more than 750 specimens contributed by almost 150 collaborating researchers from the world''s premiere natural history museums and universities. Browse through the site and see spectacular imagery and animations and details on the morphology of many representatives of the Earth''s biota. Digital Morphology, part of the National Science Foundation Digital Libraries Initiative, develops and serves unique 2D and 3D visualizations of the internal and external structure of living and extinct vertebrates, and a growing number of ''invertebrates.'' The Digital Morphology library contains nearly a terabyte of imagery of natural history specimens that are important to education and central to ongoing cutting-edge research efforts. Digital Morphology visualizations are now in use in classrooms and research labs around the world and can be seen in a growing number of museum exhibition halls. The Digital Morphology site currently presents: * QuickTime animations of complete stacks of serial CT sections * Animated 3D volumetric movies of complete specimens * Stereolithography (STL) files of 3D objects that can be viewed interactively and rapidly prototyped into scalable physical 3D objects that can be handled and studied as if they were the original specimens * Informative introductions to the scanned organisms, often written by world authorities * Pertinent bibliographic information on each specimen * Useful links * A course resource for our ''Digital Methods for Paleontology'' course, in which students learn how to generate all of the types of imagery displayed on the Digital Morphology site

Proper citation: DigiMorph (RRID:SCR_004416) Copy   

•   Source: SciCrunch Registry

http://research.calacademy.org/redirect?url=http://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on January 11, 2023. The Catalog of Fishes is the authoritative reference for taxonomic fish names, featuring a searchable on-line database. The Catalog of Fishes covers more than 53,000 species and subspecies, over 10,000 genera and subgenera, and includes in excess of 16,000 bibliographic references. The Catalog of Fishes consists of three hardbound volumes of 900-1000 pages each, along with a CD-ROM. The online database is updated about every 8 weeks and is now about twice the size of the published version. It is one of the oldest and most complete databases for any large animal group. References are over 30,000. Valid species are over 30,000. This work is an essential reference for taxonomists, scientific historians, and for any specialist dealing with fishes. Entries for species, for example, consist of species/subspecies name, genus, author, date, publication, pages, figures, type locality, location of type specimen(s), current status (with references), family/subfamily, and important publication, taxonomic, or nomenclatural notes. Nearly all original descriptions have been examined, and much effort has gone into determining the location of type specimens. The Genera are updated from Eschmeyer''s 1990 Genera of Recent Fishes. Both genera and species are listed in a classification using recent taxonomic schemes. Also included are a lengthy list of museum acronyms, an interpretation of the International Code of Zoological Nomenclature, and Opinions of the International Commission involving fishes.

Proper citation: Catalog of Fishes (RRID:SCR_004408) Copy   

•   Source: SciCrunch Registry

http://pgn.cornell.edu/

Resource for the storage, retrieval and annotation of plant ESTs, with a focus on comparative genomics. PGN comprises an analysis pipeline and a website, and presently contains mainly data from the Floral Genome Project. However, it accepts submission from other sources. All data in PGN is directly derived from chromatograms and all original and intermediate data are stored in the database. The current datasets on PGN come from the floral genome project and includes the following species: Acorus americanus, Amborella trichopoda, Asparagus officinalis, Cucumis sativus, Eschscholzia californica, Eschscholzia californica, Illicium parviflorum, Ipomopsis aggregata, Liriodendron tulipifera, Mesembryanthemum crystallinum, Mimulus guttatus, Nuphar advena, Papaver somniferum, Persea americana, Prymnesium parvum, Ribes americanum, Saruma henryi, Stenogyne rugosa, Vaccinium corymbosa, Welwitschia mirabilis, Yucca filamentosa, Zamia fischeri. For functional annotation, blast is used to compare find the best match of each unigene sequence to in the Genbank NR database, and the in complete coding sequences from Arabidopsis. These annotations are stored in the database and serve as the primary source of annotation. The annotation framework will be extended to Gene Ontology annotations in the future.

Proper citation: PGN (RRID:SCR_004559) Copy   

•   Source: SciCrunch Registry

http://function.princeton.edu/GOLEM/index.html

THIS RESOURCE IS NO LONGER IN SERVICE, documented July 7, 2017. Welcome to the home of GOLEM: An interactive, graphical gene-ontology visualization, navigation,and analysis tool on the web. GOLEM is a useful tool which allows the viewer to navigate and explore a local portion of the Gene Ontology (GO) hierarchy. Users can also load annotations for various organisms into the ontology in order to search for particular genes, or to limit the display to show only GO terms relevant to a particular organism, or to quickly search for GO terms enriched in a set of query genes. GOLEM is implemented in Java, and is available both for use on the web as an applet, and for download as a JAR package. A brief tutorial on how to use GOLEM is available both online and in the instructions included in the program. We also have a list of links to libraries used to make GOLEM, as well as the various organizations that curate organism annotations to the ontology. GOLEM is available as a .jar package and a macintosh .app for use on- or off- line as a stand-alone package. You will need to have Java (v.1.5 or greater) installed on your system to run GOLEM. Source code (including Eclipse project files) are also available. GOLEM (Gene Ontology Local Exploration Map)is a visualization and analysis tool for focused exploration of the gene ontology graph. GOLEM allows the user to dynamically expand and focus the local graph structure of the gene ontology hierarchy in the neighborhood of any chosen term. It also supports rapid analysis of an input list of genes to find enriched gene ontology terms. The GOLEM application permits the user either to utilize local gene ontology and annotations files in the absence of an Internet connection, or to access the most recent ontology and annotation information from the gene ontology webpage. GOLEM supports global and organism-specific searches by gene ontology term name, gene ontology id and gene name. CONCLUSION: GOLEM is a useful software tool for biologists interested in visualizing the local directed acyclic graph structure of the gene ontology hierarchy and searching for gene ontology terms enriched in genes of interest. It is freely available both as an application and as an applet.

Proper citation: GOLEM An interactive, graphical gene-ontology visualization, navigation, and analysis tool (RRID:SCR_003191) Copy   

•   Source: SciCrunch Registry

http://rostlab.org/services/nlsdb/

A database of nuclear localization signals (NLSs) and of nuclear proteins targeted to the nucleus by NLS motifs. NLSs are short stretches of residues mediating transport of nuclear proteins into the nucleus. The database contains 114 experimentally determined NLSs that were obtained through an extensive literature search. Using "in silico mutagenesis" this set was extended to 308 experimental and potential NLSs. This final set matched over 43% of all known nuclear proteins and matches no currently known non-nuclear protein. NLSdb contains over 6000 predicted nuclear proteins and their targeting signals from the PDB and SWISS-PROT/TrEMBL databases. The database also contains over 12 500 predicted nuclear proteins from six entirely sequenced eukaryotic proteomes (Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana and Saccharomyces cerevisiae). NLS motifs often co-localize with DNA-binding regions. This observation was used to also annotate over 1500 DNA-binding proteins. From this site you can: * Query NLSdb * Find out how to use NLSdb * Browse the entries in NLSdb * Find out if your protein has an NLS using PredictNLS * Predict subcellular localization of your protein using LOCtree

Proper citation: NLSdb: a database of nuclear localization signals (RRID:SCR_003273) Copy   

•   Source: SciCrunch Registry

http://www.morphobank.org

Web application providing online database and workspace for evolutionary research, specifically systematics (the science of determining the evolutionary relationships among species). It enables researchers to upload images and affiliate data with those images (labels, species names, etc.) and allows researchers to upload morphological data and affiliate it with phylogenetic matrices. MorphoBank is project-based, meaning a team of researchers can create a project and share the images and associated data exclusively with each other. When a paper associated with the project is published, the research team can make their data permanently available for view on MorphoBank where it is now archived.

Proper citation: MorphoBank (RRID:SCR_003213) Copy   

•   Source: SciCrunch Registry

http://pir.georgetown.edu/pirwww/dbinfo/pirsf.shtml

A SuperFamily classification system, with rules for functional site and protein name, to facilitate the sensible propagation and standardization of protein annotation and the systematic detection of annotation errors. The PIRSF concept is being used as a guiding principle to provide comprehensive and non-overlapping clustering of UniProtKB sequences into a hierarchical order to reflect their evolutionary relationships. The PIRSF classification system is based on whole proteins rather than on the component domains; therefore, it allows annotation of generic biochemical and specific biological functions, as well as classification of proteins without well-defined domains. There are different PIRSF classification levels. The primary level is the homeomorphic family, whose members are both homologous (evolved from a common ancestor) and homeomorphic (sharing full-length sequence similarity and a common domain architecture). At a lower level are the subfamilies which are clusters representing functional specialization and/or domain architecture variation within the family. Above the homeomorphic level there may be parent superfamilies that connect distantly related families and orphan proteins based on common domains. Because proteins can belong to more than one domain superfamily, the PIRSF structure is formally a network. The FTP site provides free download for PIRSF.

Proper citation: PIRSF (RRID:SCR_003352) Copy   

•   Source: SciCrunch Registry

http://mouseNET.princeton.edu

A functional network for laboratory mouse based on integration of diverse genetic and genomic data. It allows the users to accurately predict novel functional assignments and network components. MouseNET uses a probabilistic Bayesian algorithm to identify genes that are most likely to be in the same pathway/functional neighborhood as your genes of interest. It then displays biological network for the resulting genes as a graph. The nodes in the graph are genes (clicking on each node will bring up SGD page for that gene) and edges are interactions (clicking on each edge will show evidence used to predict this interaction). Most likely, the first results to load on the results page will be a list of significant Gene Ontology terms. This list is calculated for the genes in the biological network created by the mouseNET algorithm. If a gene ontology term appears on this list with a low p-value, it is statistically significantly overrepresented in this biological network. The graph may be explored further. As you move the mouse over genes in the network, interactions involving these genes are highlighted.If you click on any of the highlighted interactions graph, evidence pop-up window will appear. The Evidence pop-up lists all evidence for this interaction, with links to the papers that produced this evidence - clicking these links will bring up the relevant source citation(s) in PubMed.

Proper citation: MouseNET (RRID:SCR_003357) Copy   

•   Source: SciCrunch Registry

http://www.socr.ucla.edu/

A hierarchy of portable online interactive aids for motivating, modernizing probability and statistics applications. The tools and resources include a repository of interactive applets, computational and graphing tools, instructional and course materials. The core SOCR educational and computational components include the following suite of web-based Java applets: * Distributions (interactive graphs and calculators) * Experiments (virtual computer-generated games and processes) * Analyses (collection of common web-accessible tools for statistical data analysis) * Games (interfaces and simulations to real-life processes) * Modeler (tools for distribution, polynomial and spectral model-fitting and simulation) * Graphs, Plots and Charts (comprehensive web-based tools for exploratory data analysis), * Additional Tools (other statistical tools and resources) * SOCR Java-based Statistical Computing Libraries * SOCR Wiki (collaborative Wiki resource) * Educational Materials and Hands-on Activities (varieties of SOCR educational materials), * SOCR Statistical Consulting In addition, SOCR provides a suite of tools for volume-based statistical mapping (http://wiki.stat.ucla.edu/socr/index.php/SOCR_EduMaterials_AnalysesCommandLine) via command-line execution and via the LONI Pipeline workflows (http://www.nitrc.org/projects/pipeline). Course instructors and teachers will find the SOCR class notes and interactive tools useful for student motivation, concept demonstrations and for enhancing their technology based pedagogical approaches to any study of variation and uncertainty. Students and trainees may find the SOCR class notes, analyses, computational and graphing tools extremely useful in their learning/practicing pursuits. Model developers, software programmers and other engineering, biomedical and applied researchers may find the light-weight plug-in oriented SOCR computational libraries and infrastructure useful in their algorithm designs and research efforts. The three types of SOCR resources are: * Interactive Java applets: these include a number of different applets, simulations, demonstrations, virtual experiments, tools for data visualization and analysis, etc. All applets require a Java-enabled browser (if you see a blank screen, see the SOCR Feedback to find out how to configure your browser). * Instructional Resources: these include data, electronic textbooks, tutorials, etc. * Learning Activities: these include various interactive hands-on activities. * SOCR Video Tutorials (including general and tool-specific screencasts).

Proper citation: Statistics Online Computational Resource (RRID:SCR_003378) Copy   

•   Source: SciCrunch Registry

http://protege.stanford.edu

Protege is a free, open-source platform that provides a growing user community with a suite of tools to construct domain models and knowledge-based applications with ontologies. At its core, Protege implements a rich set of knowledge-modeling structures and actions that support the creation, visualization, and manipulation of ontologies in various representation formats. Protege can be customized to provide domain-friendly support for creating knowledge models and entering data. Further, Protege can be extended by way of a plug-in architecture and a Java-based Application Programming Interface (API) for building knowledge-based tools and applications. An ontology describes the concepts and relationships that are important in a particular domain, providing a vocabulary for that domain as well as a computerized specification of the meaning of terms used in the vocabulary. Ontologies range from taxonomies and classifications, database schemas, to fully axiomatized theories. In recent years, ontologies have been adopted in many business and scientific communities as a way to share, reuse and process domain knowledge. Ontologies are now central to many applications such as scientific knowledge portals, information management and integration systems, electronic commerce, and semantic web services. The Protege platform supports two main ways of modeling ontologies: * The Protege-Frames editor enables users to build and populate ontologies that are frame-based, in accordance with the Open Knowledge Base Connectivity protocol (OKBC). In this model, an ontology consists of a set of classes organized in a subsumption hierarchy to represent a domain's salient concepts, a set of slots associated to classes to describe their properties and relationships, and a set of instances of those classes - individual exemplars of the concepts that hold specific values for their properties. * The Protege-OWL editor enables users to build ontologies for the Semantic Web, in particular in the W3C's Web Ontology Language (OWL). An OWL ontology may include descriptions of classes, properties and their instances. Given such an ontology, the OWL formal semantics specifies how to derive its logical consequences, i.e. facts not literally present in the ontology, but entailed by the semantics. These entailments may be based on a single document or multiple distributed documents that have been combined using defined OWL mechanisms (see the OWL Web Ontology Language Guide). Protege is based on Java, is extensible, and provides a plug-and-play environment that makes it a flexible base for rapid prototyping and application development.

Proper citation: Protege (RRID:SCR_003299) Copy   

•   Source: SciCrunch Registry

http://sbgrid.org/

Computing resources structural biologists need to discover the shapes of the molecules of life, it provides access to web-enabled structural biology applications, data sharing facilities, biological data sets, and other resources valuable to the computational structural biology community. Consortium includes X-ray crystallography, NMR and electron microscopy laboratories worldwide.SBGrid Service Center is located at Harvard Medical School.SBGrid's NIH-compliant Service Center supports SBGrid operations and provides members with access to Software Maintenance, Computing Access, and Training. Consortium benefits include: * remote management of your customized collection of structural biology applications on Linux and Mac workstations; * access to commercial applications exclusively licensed to members of the Consortium, such as NMRPipe, Schrodinger Suite (limited tokens) and the Incentive version of Pymol; remote management of supporting scientific applications (e.g., bioinformatics, computational chemistry and utilities); * access to SBGrid seminars and events; and * advice about hardware configurations, operating system installations and high performance computing. Membership is restricted to academic/non-profit research laboratories that use X-ray crystallography, 2D crystallography, NMR, EM, tomography and other experimental structural biology technologies in their research. Most new members are fully integrated with SBGrid within 2 weeks of the initial application.

Proper citation: Structural Biology Grid (RRID:SCR_003511) Copy   

•   Source: SciCrunch Registry