Tools   Select Another Resource Report Type

https://bioweb.pasteur.fr/packages/pack@[email protected]

Open source software tool for analysing trace files generated by Bayesian MCMC runs. Software package for visualising and analysing MCMC trace files generated through Bayesian phylogenetic inference. Provides kernel density estimation, multivariate visualisation, demographic trajectory reconstruction, conditional posterior distribution summary and more.

Proper citation: Tracer (RRID:SCR_019121) Copy   

•   Source: SciCrunch Registry

https://brainlife.io/

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   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/gimme/

Software Matlab toolbox for directed functional connectivity analysis of fMRI BOLD signal from predefined regions of interest. It recovers true structure of connections and estimates weights attributed to each connection. Obtains patterns at group and individual levels.

Proper citation: GIMME (RRID:SCR_014115) Copy   

•   Source: SciCrunch Registry

http://www.scite.ai

Web tool to classify citation statements from scientific articles using deep learning. Used for discovering and evaluating scientific articles via Smart Citations.

Proper citation: scite (RRID:SCR_018568) Copy   

•   Source: SciCrunch Registry

https://github.com/PetaVision/OpenPV

Open source software neural simulation toolbox. C++ library for designing and deploying large scale neurally inspired computational models. Object oriented neural simulation toolbox optimized for high performance multi core, multi node computer architectures.

Proper citation: PetaVision (RRID:SCR_017451) Copy   

•   Source: SciCrunch Registry

https://github.com/denisecailab/minian

Software miniscope analysis pipeline that requires low memory and computational demand so it can be run without specialized hardware. Offers interactive visualization that allows users to see how parameters in each step of pipeline affect output.

Proper citation: Minian (RRID:SCR_022601) Copy   

•   Source: SciCrunch Registry

https://github.com/compbiolabucf/APA-Scan

Software Python tool for detection and visualization of annotated and potential alternative polyadenylation events in downstream 3'-UTR of gene among two different biological conditions. Used for detection and visualization of 3'-UTR alternative polyadenylation with RNA-seq and 3'-end-seq data.

Proper citation: APA-Scan (RRID:SCR_022974) Copy   

•   Source: SciCrunch Registry

http://sonorus.princeton.edu/hefalmp/

HEFalMp (Human Experimental/FunctionAL MaPper) is a tool developed by Curtis Huttenhower in Olga Troyanskaya's lab at Princeton University. It was created to allow interactive exploration of functional maps. Functional mapping analyzes portions of these networks related to user-specified groups of genes and biological processes and displays the results as probabilities (for individual genes), functional association p-values (for groups of genes), or graphically (as an interaction network). HEFalMp contains information from roughly 15,000 microarray conditions, over 15,000 publications on genetic and physical protein interactions, and several types of DNA and protein sequence analyses and allows the exploration of over 200 H. sapiens process-specific functional relationship networks, including a global, process-independent network capturing the most general functional relationships. Looking to download functional maps? Keep an eye on the bottom of each page of results: every functional map of any kind is generated with a Download link at the bottom right. Most functional maps are provided as tab-delimited text to simplify downstream processing; graphical interaction networks are provided as Support Vector Graphics files, which can be viewed using the Adobe Viewer, any recent version of Firefox, or the excellent open source Inkscape tool.

Proper citation: Human Experimental/FunctionAL MaPper: Providing Functional Maps of the Human Genome (RRID:SCR_003506) Copy   

•   Source: SciCrunch Registry

http://biosearch.berkeley.edu/

Developed as part of the BioText project at the University of California, Berkeley, the BioText Search Engine is a freely available Web-based application that provides biologists with new ways to access the scientific literature. The system indexes all open access articles available at PubMed Central. New articles are indexed daily. The current collection consists of more than 300 journals, 40,000 articles, 100,000 figures, and 60,000 tables. The Full Text & Abstract view searches the full text of articles (in addition to title, author, and abstract information) and returns full-text excerpts that match users' queries. Three selection boxes at the top (ABSTRACTS, FULL-TEXT EXCERPTS and FIGURES allow users to choose what the view displays. The BioText Search Engine allows users to search in tables. When the table view is selected, BioText searches in article titles, table captions, and table contents. The Grid View allows users to search over captions. It returns figures and truncated captions in a grid arrangement.

Proper citation: BioText Search Engine (RRID:SCR_003600) Copy   

•   Source: SciCrunch Registry

http://cmr.jcvi.org/cgi-bin/CMR/shared/GenomePropertiesHomePage.cgi

The Genome Properties system consists of a suite of Properties which are carefully defined attributes of prokaryotic organisms whose status can be described by numerical values or controlled vocabulary terms for individual completely sequenced genomes. The system has been designed to capture the widest possible range of attributes and currently encompasses taxonomic terms, genometric calculations, metabolic pathways, systems of interacting macromolecular components and quantitative and descriptive experimental observations (phenotypes) from the literature. You may search the Genome Properties Database in 1 of 3 ways: * Search For Predicted Properties in the CMR: The Genome Property Search allows you to search the Genome Property database for state information for selected genomes and properties. * Perform a Keyword Search for a Specific Property: Lists all Genome Properties that match a specific text string. You can choose to search All Fields within a genome property or the Property Name. * Browse Top Level Genome Properties: Click on the properties to see the specific genome property report page. The Genome Properties system presents key aspects of prokaryotic biology using standardized computational methods and controlled vocabularies. Properties reflect gene content, phenotype, phylogeny and computational analyses. The results of searches using hidden Markov models allow many properties to be deduced automatically, especially for families of proteins (equivalogs) conserved in function since their last common ancestor. Additional properties are derived from curation, published reports and other forms of evidence. Genome Properties system was applied to 156 complete prokaryotic genomes, and is easily mined to find differences between species, correlations between metabolic features and families of uncharacterized proteins, or relationships among properties.

Proper citation: JCVI GenProp (RRID:SCR_004592) Copy   

•   Source: SciCrunch Registry

http://pilgrm.princeton.edu

PILGRM (the platform for interactive learning by genomics results mining) puts advanced supervised analysis techniques applied to enormous gene expression compendia into the hands of bench biologists. This flexible system empowers its users to answer diverse biological questions that are often outside of the scope of common databases in a data-driven manner. This capability allows domain experts to quickly and easily generate hypotheses about biological processes, tissues or diseases of interest. Specifically PILGRM helps biologists generate these hypotheses by analyzing the expression levels of known relevant genes in large compendia of microarray data. PILGRM is for the biologist with a set of proteins relevant to a disease, biological function or tissue of interest who wants to find additional players in that process. It uses a data driven method that provides added value for literature search results by mining compendia of publicly available gene expression datasets using lists of relevant and irrelevant genes (standards). PILGRM produces publication quality PDFs usable as supplementary material to describe the computational approach, standards and datasets. Each PILGRM analysis starts with an important biological question (e.g. What genes are relevant for breast cancer but not mammary tissue in general?). For PILGRM to discover relevant genes, it needs examples of both genes that you would (positive) and would not (negative) find interesting. Lists of these genes are what we call standards and in PILGRM you can build your own standards or you can use standards from common sources that we pre-load for your convenience. PILGRM lets you build your own literature-documented standards so that processes, disease, and tissues that are not well covered in databases of tissue expression, disease, or function can still be used for an analysis.

Proper citation: PILGRM (RRID:SCR_004749) Copy   

•   Source: SciCrunch Registry

http://nbc.ece.drexel.edu/

Webserver for taxonomic classification of metagenomic reads.

Proper citation: NBC (RRID:SCR_004772) Copy   

•   Source: SciCrunch Registry

http://gila.bioengr.uic.edu/snp/toposnp

A topographic database for analyzing non-synonymous SNPs (nsSNPs) that can be mapped onto known 3D structures of proteins. These include disease- associated nsSNPs derived from the Online Mendelian Inheritance in Man (OMIM) database and other nsSNPs derived from dbSNP, a resource at the National Center for Biotechnology Information that catalogs SNPs. TopoSNP further classifies each nsSNP site into three categories based on their geometric location: those located in a surface pocket or an interior void of the protein, those on a convex region or a shallow depressed region, and those that are completely buried in the interior of the protein structure. These unique geometric descriptions provide more detailed mapping of nsSNPs to protein structures. It also includes relative entropy of SNPs calculated from multiple sequence alignment as obtained from the Pfam database (a database of protein families and conserved protein motifs) as well as manually adjusted multiple alignments obtained from ClustalW. These structural and conservational data can be useful for studying whether nsSNPs in coding regions are likely to lead to phenotypic changes. TopoSNP includes an interactive structural visualization web interface, as well as downloadable batch data.

Proper citation: TopoSNP (RRID:SCR_005572) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.clemson.edu/G-SESAME/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 2,2025. G-SESAME contains a set of tools. They include: tools for measuring the semantic similarity of GO terms; tools for measuring the functional similarity of genes; and tools for clustering genes based on their GO term annotation information. Platform: Online tool, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: G-SESAME - Gene Semantic Similarity Analysis and Measurement Tools (RRID:SCR_005816) Copy   

•   Source: SciCrunch Registry

http://dommino.org

DOMMINO is a comprehensive structural database on macromolecular interactions. As of June, 2011, it contains more than 407,000 binary interactions. The distinctive features of DOMMINO are: # Automated updates: DOMMINO is fully automated and is designed to update itself on a weekly basis, one day after a PDB weekly update. Thus, the community will be able to study macromolecular interactions almost immediately after they are released by PDB. # Coverage of non-domain mediated interactions: In addition to domain-domain and domain-peptide interactions the database characterizes the interaction between domains and unstructured protein regions that are not parts of a domain, such as inter-domain linkers and N- and C-termini. The interactions that involve the latter unstructured parts of proteins have been included to the database for the first time providing additional ~186,000 interactions (~45% of the total number of interactions, as of June, 2011). # Coverage of new structural domains: DOMMINO employs one of the most accurate structural classifications of proteins, SCOP. In addition to the existing SCOP-annotated domains, we employ a state-of-the-art machine learning approach to classify newer protein structures into existing SCOP families. With the progress of structural genomics, we do not expect a significant growth of the number of structurally novel folds or protein families and therefore our method allows covering almost all new protein structures. In total, using this predictive approach has allowed us to add more than 261,000 new interactions, almost twice as many as existing SCOP-annotated interactions. # The web-interface is designed to give the user a possibility of a flexible search as well as the capability to study macromolecular interactions in a PDB structure at the interaction network level and at the individual interface level. The web interface of the DOMMINO database includes a comprehensive list of help topics linked to the specific actions. In addition, we have designed a step-by-step tutorial that covers all aspects of working with the data from DOMMINO using the web interface.

Proper citation: DOMMINO - Database Of MacroMolecular INteractiOns (RRID:SCR_005958) Copy   

•   Source: SciCrunch Registry

http://code.google.com/p/lapdftext/

Software that facilitates accurate extraction of text from PDF files of research articles for use in text mining applications. It is intended for both scientists and natural language processing (NLP) engineers interested in getting access to text within specific sections of research articles. The system extracts text blocks from PDF-formatted full-text research articles and classifies them into logical units based on rules that characterize specific sections. The LA-PDFText system focuses only on the textual content of the research articles. The current version of LA-PDFText is a baseline system that extracts text using a three-stage process: * identification of blocks of contiguous text * classification of these blocks into rhetorical categories * extraction of the text from blocks grouped section-wise.

Proper citation: lapdftext (RRID:SCR_006167) Copy   

•   Source: SciCrunch Registry

https://nationalmaglab.org/user-facilities/icr

Facility provides service operations for sample analysis that requires ultrahigh resolution and high mass accuracy of Fourier Transform Ion Cyclotron Resonance. Used for research in biomolecular analysis, hydrogen-deuterium exchange and environmental and petrochemical analysis. Four FT-ICR mass spectrometers feature high magnetic fields including the world-record 21 tesla and are compatible with multiple ionization and fragmentation techniques.

Proper citation: National High Magnetic Field Laboratory Ion Cyclotron Resonance Core Facility (RRID:SCR_017361) Copy   

•   Source: SciCrunch Registry

https://nationalmaglab.org/user-facilities/emr/

EMR Facility offers home-built, high-frequency and high-field continuous-wave instruments providing frequency coverage from 9 GHz to 1 THz, with additional frequencies available up to 2.5 THz using molecular gas laser. EMR covers variety of magnetic resonance techniques associated with electron like Electron Paramagnetic/Spin Resonance (EPR/ESR). EPR/ESR can be performed on any sample that has unpaired electron spins and used in applications in physics, materials science, chemistry and biology, including studies of impurity states, molecular clusters, antiferromagnetic, ferromagnetic and thin film compounds, natural or induced radicals, optically excited paramagnetic states, electron spin-based quantum information devices, transition-metal based catalysts; and for structural and dynamical studies of metallo-proteins, spin-labeled proteins and other complex bio-molecules and their synthetic models.

Proper citation: National High Magnetic Field Laboratory Electron Magnetic Resonance Core Facility (RRID:SCR_017359) Copy   

•   Source: SciCrunch Registry

https://nationalmaglab.org/user-facilities/dc-field

Facility located at MagLab headquarters near Florida State University in Tallahassee. Contains 14 resistive magnet cells connected to 56 megawatt DC power supply and 15,000 square feet of cooling equipment to remove heat generated by magnets. Includes several superconducting magnets operating at millikelvin temperatures. Among these instruments is 45-tesla hybrid magnet, which offers scientists strongest continuous magnetic field in world. Research is supported by magnet plant and cryogenic system operators. Technicians design, build and repair instruments for user research.

Proper citation: National High Magnetic Field Lab DC Field Core Facility (RRID:SCR_017358) Copy   

•   Source: SciCrunch Registry

https://sites.northwestern.edu/nucapt/

Facility specializes in high resolution chemical imaging by three dimensional atom probe tomography. APT produces three-dimensional (3D) atom-by-atom elementally and isotopically resolved image with sub-nanometer spatial resolution of sample volume typically 100 x 100 x 300 nm^3, by simultaneous high resolution direct-space imaging and atom-by-atom time of flight mass spectrometry. APT is particularly suited to study nano- or nanostructured materials.

Proper citation: Northwestern University Center for Atom Probe Tomography Core Facility (RRID:SCR_017770) Copy   

•   Source: SciCrunch Registry