Tools   Select Another Resource Report Type

http://publications.nigms.nih.gov/insidelifescience/

The NIGMS Inside Life Science series brings you inside the science of health. Each story shows how basic biomedical researchfrom the history of a field to the people doing cutting-edge work todaylays the foundation for advances in disease diagnosis, treatment and prevention. Through explorations of how the body works and highlights from recent studies, you''ll discover even more on what scientists have found and are finding about fundamental life processes. NIGMS supported all of the featured research.

Proper citation: NIGMS Inside Life Science (RRID:SCR_005852) Copy   

•   Source: SciCrunch Registry

http://www.mouse-genome.bcm.tmc.edu/ENU/MutagenesisProj.asp

THIS RESOURCE IS NO LONGER IN SERVICE. For updated mutant information, please visit MMRRC or The Jackson Laboratory. Produces, characterizes, and distributes mutant mouse strains with defects in embryonic and postembryonic development. The goal of the ENU Mutagenesis project III is to determine the function of genes on mouse Chromosome 11 by saturating the chromosome with recessive mutations. The distal 40 cM of mouse Chr 11 exhibits linkage conservation with human Chromosome 17. We are using the chemical N-ethyl-N-nitrosourea (ENU) to saturate wild type chromosomes with point mutations. By determining the function of genes on a mouse chromosome, we can extrapolate to predict function on a human chromosome. We expect many of the new mutants to represent models of human diseases such as birth defects, patterning defects, growth and endocrine defects, neurological anomalies, and blood defects. Because many of the mutations we expect to isolate may be lethal or detrimental to the mice, we are using a unique approach to isolate mutations. This approach uses a balancer chromosome that is homozygous lethal and carries a dominant coat color marker to suppress recombination over a reasonable interval.

Proper citation: Mouse Mutagenesis Center for Developmental Defects (RRID:SCR_007321) Copy   

•   Source: SciCrunch Registry

http://web.cbio.uct.ac.za/~darren/rdp.html

Software package to analyse nucleotide sequence data and identify evidence of genetic recombination. RDP3 is version of RDP program for characterizing recombination events in DNA-sequence alignments. RDP4 is version of RDP program for detection and analysis of recombination patterns in virus genomes.

Proper citation: Recombination Detection Program (RRID:SCR_018537) Copy   

•   Source: SciCrunch Registry

https://ohlerlab.mdc-berlin.de/software/RiboTaper_126/

Software tool as analysis pipeline for ribosome profiling experiments, which exploits triplet periodicity of ribosomal footprints to call translated regions. Statistical approach that identifies translated regions on basis of characteristic three nucleotide periodicity of Ribo-seq data.

Proper citation: RiboTaper (RRID:SCR_018880) Copy   

•   Source: SciCrunch Registry

http://www.kojak-ms.org/

Software tool for identification of cross-linked peptides from mass spectra. Used for analysis of chemically cross-linked protein complexes. Used to analyze both novel and existing data sets.

Proper citation: Kojak (RRID:SCR_021028) Copy   

•   Source: SciCrunch Registry

https://www.rdocumentation.org/packages/qtl2/versions/0.24

Software R package for mapping quantitative trait loci with high dimensional data and multiparent populations. Used for analysis of high dimensional data and complex crosses. Interactive software environment for mapping quantitative trait loci in experimental populations.R/qtl2 software expands scope of R/qtl software package to include multiparent populations derived from more than two founder strains, such as Collaborative Cross and Diversity Outbred mice, heterogeneous stocks, and MAGIC plant populations.

Proper citation: R/qtl2 (RRID:SCR_018181) Copy   

•   Source: SciCrunch Registry

https://github.com/nskvir/RepEnrich

Software tool to profile enrichment of next generation sequencing reads at transposable elements. Method to estimate repetitive element enrichment using high throughput sequencing data. Used to study genome wide transcriptional regulation of repetitive elements.RepEnrich2 is updated method to estimate repetitive element enrichment using high-throughput sequencing data.

Proper citation: RepEnrich (RRID:SCR_021733) Copy   

•   Source: SciCrunch Registry

https://maayanlab.cloud/chea3/

Web based transcription factor enrichment analysis. Web server ranks TFs associated with user-submitted gene sets. ChEA3 background database contains collection of gene set libraries generated from multiple sources including TF-gene co-expression from RNA-seq studies, TF-target associations from ChIP-seq experiments, and TF-gene co-occurrence computed from crowd-submitted gene lists. Enrichment results from these distinct sources are integrated to generate composite rank that improves prediction of correct upstream TF compared to ranks produced by individual libraries.

Proper citation: ChIP-X Enrichment Analysis 3 (RRID:SCR_023159) Copy   

•   Source: SciCrunch Registry

http://www.csardock.org

Experimental datasets of crystal structures and binding affinities for diverse protein-ligand complexes. Some datasets are generated in house while others are collected from the literature or deposited by academic labs, national centers, and the pharmaceutical industry. For the community to improve their approaches, they need exceptional datasets to train scoring functions and develop new docking algorithms. They aim to provide the highest quality data for a diverse collection of proteins and small molecule ligands. They need input from the community in developing target priorities. Ideal targets will have many high-quality crystal structures (apo and 10-20 bound to diverse ligands) and affinity data for 25 compounds that range in size, scaffold, and logP. It is best if the ligand set has several congeneric series that span a broad range of affinity, with low nanomolar to mid-micromolar being most desirable. They prefer Kd data over Ki data over IC50 data (no % activity data). They will determine solubility, pKa, logP/logD data for the ligands whenever possible. They have augmented some donated IC50 data by determining Kon/Koff and ITC data.

Proper citation: Community Structure-Activity Resource (RRID:SCR_002206) Copy   

•   Source: SciCrunch Registry

https://simtk.org

A National NIH Center for Biomedical Computing that focuses on physics-based simulation of biological structures and provides open access to high quality simulation tools, accurate models and the people behind them. It serves as a repository for models that are published (as well as the associated code) to create a living archive of simulation scholarship. Simtk.org is organized into projects. A project represents a research endeavor, a software package or a collection of documents and publications. Includes sharing of image files, media, references to publications and manuscripts, as well as executables and applications for download and source code. Simulation tools are free to download and space is available for developers to manage, share and disseminate code.

Proper citation: Simtk.org (RRID:SCR_002680) Copy   

•   Source: SciCrunch Registry

http://bioportal.bioontology.org/

Open repository of biomedical ontologies that provides access via Web browsers and Web services to ontologies. It supports ontologies in OBO format, OWL, RDF, Rich Release Format (RRF), Protege frames, and LexGrid XML. Functionality includes the ability to browse, search and visualize ontologies as well as to comment on, and create mappings for ontologies. Any registered user can submit an ontology. The NCBO Annotator and NCBO Resource Index can also be accessed via BioPortal. Additional features: * Add Reviews: rate the ontology according to several criteria and describe your experience using the ontology. * Add Mappings: submit point-to-point mappings or upload bulk mappings created with external tools. Notification of new Mappings is RSS-enabled and Mappings can be browsed via BioPortal and accessed via Web services. * NCBO Annotator: Tool that tags free text with ontology terms. NCBO uses the Annotator to generate ontology annotations, creating an ontology index of these resources accessible via the NCBO Resource Index. The Annotator can be accessed through BioPortal or directly as a Web service. The annotation workflow is based on syntactic concept recognition (using the preferred name and synonyms for terms) and on a set of semantic expansion algorithms that leverage the ontology structure (e.g., is_a relations). * NCBO Resource Index: The NCBO Resource Index is a system for ontology based annotation and indexing of biomedical data; the key functionality of this system is to enable users to locate biomedical data linked via ontology terms. A set of annotations is generated automatically, using the NCBO Annotator, and presented in BioPortal. This service uses a concept recognizer (developed by the National Center for Integrative Biomedical Informatics, University of Michigan) to produce a set of annotations and expand them using ontology is_a relations. * Web services: Documentation on all Web services and example code is available at: BioPortal Web services.

Proper citation: BioPortal (RRID:SCR_002713) Copy   

•   Source: SciCrunch Registry

https://simtk.org/home/foldvillin

An archive of hundreds of all-atom, explicit solvent molecular dynamics simulations that were performed on a set of nine unfolded conformations of a variant of the villin headpiece subdomain (HP-35 NleNle). It includes scripts for accessing the archive of villin trajectories as well as a VMD plug-in for viewing the trajectories. In addition, all starting structures used in the trajectories are also provided. The simulations were generated using a distributed computing method utilizing the symmetric multiprocessing paradigm for individual nodes of the Folding_at_home distributed computing network. The villin trajectories in the archive are divided into two projects: PROJ3036 and PROJ3037. PROJ3036 contains trajectories starting from nine non-folded configurations. PROJ3037 contains trajectories starting from the native (folded) state. Runs 0 through 8 (in PROJ3036) correspond to starting configurations 0 through 8 discussed in the paper in J. Mol. Biol. (2007) 374(3):806-816 (see the publications tab for a full reference), whereas RUN9 uses the same starting configuration as RUN8. Each run contains 100 trajectories (named clone 0-99), each with the same starting configuration but different random velocities. Trajectories vary in their length of time and are subdivided into frames, also known as a generation. Each frame contains around 400 configurational snapshots, or timepoints, of the trajectory, with the last configurational snapshot of frame i corresponding to the first configurational snapshot of generation i+1. The goal is to allow researchers to analyze and benefit from the many trajectories produced through the simulations.

Proper citation: Molecular Simulation Trajectories Archive of a Villin Variant (RRID:SCR_002704) Copy   

•   Source: SciCrunch Registry

http://cytoscape.org

Software platform for complex network analysis and visualization. Used for visualization of molecular interaction networks and biological pathways and integrating these networks with annotations, gene expression profiles and other state data.

Proper citation: Cytoscape (RRID:SCR_003032) Copy   

•   Source: SciCrunch Registry

http://pir.georgetown.edu/pro/

An ontological representation of protein-related entities, explicitly defining them and showing the relationships between them. Each PRO term represents a distinct class of entities (including specific modified forms, orthologous isoforms, and protein complexes) ranging from the taxon-neutral to the taxon-specific. PRO encompasses three sub-ontologies: proteins based on evolutionary relatedness (ProEvo); protein forms produced from a given gene locus (ProForm); and protein-containing complexes (ProComp).

Proper citation: PRO (RRID:SCR_002902) Copy   

•   Source: SciCrunch Registry

http://www.bioperl.org

BioPerl is a community effort to produce Perl code which is useful in biology. This toolkit of perl modules is useful in building bioinformatics solutions in Perl. It is built in an object-oriented manner so that many modules depend on each other to achieve a task. The collection of modules in the bioperl-live repository consist of the core of the functionality of bioperl. Additionally auxiliary modules for creating graphical interfaces (bioperl-gui), persistent storage in RDMBS (bioperl-db), running and parsing the results from hundreds of bioinformatics applications (Run package), software to automate bioinformatic analyses (bioperl-pipeline) are all available as Git modules in our repository. The BioPerl toolkit provides a library of hundreds of routines for processing sequence, annotation, alignment, and sequence analysis reports. It often serves as a bridge between different computational biology applications assisting the user to construct analysis pipelines. This chapter illustrates how BioPerl facilitates tasks such as writing scripts summarizing information from BLAST reports or extracting key annotation details from a GenBank sequence record. BioPerl includes modules written by Sohel Merchant of the GO Consortium for parsing and manipulating OBO ontologies. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: BioPerl (RRID:SCR_002989) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/gsea/

Software package for interpreting gene expression data. Used for interpretation of a large-scale experiment by identifying pathways and processes.

Proper citation: Gene Set Enrichment Analysis (RRID:SCR_003199) Copy   

•   Source: SciCrunch Registry

http://bio3d.colorado.edu/imod

A free, cross-platform set of image processing, modeling and display programs used for tomographic reconstruction and for 3D reconstruction of EM serial sections and optical sections. The package contains tools for assembling and aligning data within multiple types and sizes of image stacks, viewing 3-D data from any orientation, and modeling and display of the image files. IMOD 4.1.8 Is Now Available for Linux, Windows, and Mac OS X

Proper citation: IMOD (RRID:SCR_003297) Copy   

•   Source: SciCrunch Registry

http://mods.rna.albany.edu

The RNA modification database provides a comprehensive listing of posttranscriptionally modified nucleosides from RNA. Information provided for each nucleoside includes: the type of RNA in which it occurs and phylogenetic distribution; common chemical name and symbol; Chemical Abstracts registry number and index name; chemical structure; initial literature citations for structural characterization or occurrence, and for chemical synthesis. Both the structural diversity and extent of posttranscriptional modification in RNA is remarkable, with 107 different nucleosides presently known in all types of RNA. The discovery of new modified nucleosides as well as increasing knowledge of the array of functional roles of modification, based largely on extensive studies of tRNA, mandates a need for a comprehensive database of RNA nucleosides. The RNA Modification Database is maintained as an extension of the initial version published in mid-1994. The database consists of all RNA-derived ribonucleosides of known structure, including those from established sequence positions, as well as those detected or characterized from hydrolysates of RNA. The information provided permits access to the modified nucleoside literature through provision of both computer-searchable Chemical Abstracts registry numbers and key literature citations. This database also provides an historical record of the initial reports of occurrence, characterization and chemical synthesis of modified nucleosides from RNA. It is our judgement that the total number of RNA nucleosides listed, and the chemical structures reported, are very accurate. However, the distributions listed are in some cases a matter of concern, due primarily to the possibility of inhomogeneity of the RNA isolate and the use of methods of nucleoside identification that are not sufficiently rigorous. Reinvestigation of some of the unusual or single-report source distributions is warranted, and will likely lead to future refinements in the listings. The authors invite comments concerning new entries, errors or omissions and on the format presently used for electronic access to the database.

Proper citation: RNA Modification Database (RRID:SCR_003535) Copy   

•   Source: SciCrunch Registry

https://www.cgl.ucsf.edu/chimera/

Software tool for interactive visualization and analysis of molecular structures and related data, including density maps, supramolecular assemblies, sequence alignments, docking results, trajectories, and conformational ensembles. High-quality images and animations can be generated. Chimera includes complete documentation and several tutorials.

Proper citation: UCSF Chimera (RRID:SCR_004097) Copy   

•   Source: SciCrunch Registry

https://simtk.org/home/allopathfinder

Software application and code base that allows users to compute likely allosteric pathways in proteins. The underlying assumption is that residues participating in allosteric communication should be fairly conserved and that communication happens through residues that are close in space. The initial application for the code provided was to study the allosteric communication in myosin. Myosin is a well-studied molecular motor protein that walks along actin filaments to achieve cellular tasks such as movement of cargo proteins. It couples ATP hydrolysis to highly-coordinated conformational changes that result in a power-stroke motion, or "walking" of myosin. Communication between a set of residues must link the three functional regions of myosin and transduce energy: the catalytic ATP binding region, the lever arm, and the actin-binding domain. They are investigating which residues are likely to participate in allosteric communication pathways. The application is a collection of C++/QT code, suitable for reproducing the computational results of the paper. (PMID 17900617) In addition, they provide input and alignment information to reproduce Figure 3 (a key figure) in the paper. Examples provided will show users how to use AlloPathFinder with other protein families, assumed to exhibit an allosteric communication. To run the application a multiple sequence alignment of representative proteins from the protein family is required along with at least one protein structure.

Proper citation: Allopathfinder (RRID:SCR_002702) Copy   

•   Source: SciCrunch Registry