Tools   Select Another Resource Report Type

http://csbio.unc.edu/genescissors/

Software for detecting and correcting spurious transcriptome inference due to RNAseq reads misalignment.

Proper citation: GeneScissors (RRID:SCR_003146) Copy   

•   Source: SciCrunch Registry

http://www.nematodes.org/

Nematode & Neglected Genomics (at) The Blaxter Lab is a nematode related portal including databases and services. Resources include genomic and transcriptomic databases for nematodes and other metazoan phyla and freely downloadable software tools for expressed sequence tag analysis, DNA barcode analysis and phylogenomics. Major categories include: * GenePool * 959 Nematode Genomes * Teaching * Research Projects * Bioinformatics Software Tools * Lab Personnel * Lab Wiki * Genomics Databases * NEMBASE4 * Tardigrada: Hypsibius dujardini * Earthworm: Lumbricus rubellus * MolluscDB * ArthropodDB * other Neglected Genomes

Proper citation: nematodes.org (RRID:SCR_003267) Copy   

•   Source: SciCrunch Registry

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

Ontology developed as an application ontology as part of the Biocode Commons project whose goal is to support the interoperability of biodiversity data, including data on museum collections, environmental and metagenomic samples, and ecological surveys. It includes consideration of the distinctions between individuals, organisms, voucher specimens, lots, and samples the relations between these entities, and processes governing the creation and use of samples. Within scope as well are properties including collector, location, time, storage environment, containers, institution, and collection identifiers.

Proper citation: Biological Collections Ontology (RRID:SCR_003262) Copy   

•   Source: SciCrunch Registry

http://primer3.ut.ee

Tool used to design PCR primers from DNA sequence - often in high-throughput genomics applications. It does everything from mispriming libraries to sequence quality data to the generation of internal oligos.

Proper citation: Primer3 (RRID:SCR_003139) Copy   

•   Source: SciCrunch Registry

http://www.genome.gov/Glossary/

Glossary of Genetic Terms to help everyone understand the terms and concepts used in genetic research. In addition to definitions, specialists in the field of genetics share their descriptions of terms, and many terms include images, animation and links to related terms.

Proper citation: Talking Glossary of Genetic Terms (RRID:SCR_003215) 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://phenome.jax.org/

Database enables integration of genomic and phenomic data by providing access to primary experimental data, data collection protocols and analysis tools. Data represent behavioral, morphological and physiological disease-related characteristics in naive mice and those exposed to drugs, environmental agents or other treatments. Collaborative standardized collection of measured data on laboratory mouse strains to characterize them in order to facilitate translational discoveries and to assist in selection of strains for experimental studies. Includes baseline phenotype data sets as well as studies of drug, diet, disease and aging effect., protocols, projects and publications, and SNP, variation and gene expression studies. Provides tools for online analysis. Data sets are voluntarily contributed by researchers from variety of institutions and settings, or retrieved by MPD staff from open public sources. MPD has three major types of strain-centric data sets: phenotype strain surveys, SNP and variation data, and gene expression strain surveys. MPD collects data on classical inbred strains as well as any fixed-genotype strains and derivatives that are openly acquirable by the research community. New panels include Collaborative Cross (CC) lines and Diversity Outbred (DO) populations. Phenotype data include measurements of behavior, hematology, bone mineral density, cholesterol levels, endocrine function, aging processes, addiction, neurosensory functions, and other biomedically relevant areas. Genotype data are primarily in the form of single-nucleotide polymorphisms (SNPs). MPD curates data into a common framework by standardizing mouse strain nomenclature, standardizing units (SI where feasible), evaluating data (completeness, statistical power, quality), categorizing phenotype data and linking to ontologies, conforming to internal style guides for titles, tags, and descriptions, and creating comprehensive protocol documentation including environmental parameters of the test animals. These elements are critical for experimental reproducibility.

Proper citation: Mouse Phenome Database (MPD) (RRID:SCR_003212) Copy   

•   Source: SciCrunch Registry

https://github.com/brunonevado/Pipeliner

Software for evaluating the performance of bioinformatics pipelines for Next Generation re-Sequencing.

Proper citation: Pipeliner (RRID:SCR_003171) Copy   

•   Source: SciCrunch Registry

http://www.emdataresource.org/

Portal for deposition and retrieval of cryo electron microscopy (3DEM) density maps, atomic models, and associated metadata. Global resource for 3 Dimensional Electron Microscopy structure data archiving and retrieval, news, events, software tools, data standards, validation methods.

Proper citation: EMDataResource.org (RRID:SCR_003207) Copy   

•   Source: SciCrunch Registry

http://dip.doe-mbi.ucla.edu/

Database to catalog experimentally determined interactions between proteins combining information from a variety of sources to create a single, consistent set of protein-protein interactions that can be downloaded in a variety of formats. The data were curated, both, manually and also automatically using computational approaches that utilize the the knowledge about the protein-protein interaction networks extracted from the most reliable, core subset of the DIP data. Because the reliability of experimental evidence varies widely, methods of quality assessment have been developed and utilized to identify the most reliable subset of the interactions. This CORE set can be used as a reference when evaluating the reliability of high-throughput protein-protein interaction data sets, for development of prediction methods, as well as in the studies of the properties of protein interaction networks. Tools are available to analyze, visualize and integrate user's own experimental data with the information about protein-protein interactions available in the DIP database. The DIP database lists protein pairs that are known to interact with each other. By interact they mean that two amino acid chains were experimentally identified to bind to each other. The database lists such pairs to aid those studying a particular protein-protein interaction but also those investigating entire regulatory and signaling pathways as well as those studying the organization and complexity of the protein interaction network at the cellular level. Registration is required to gain access to most of the DIP features. Registration is free to the members of the academic community. Trial accounts for the commercial users are also available.

Proper citation: Database of Interacting Proteins (DIP) (RRID:SCR_003167) Copy   

•   Source: SciCrunch Registry

http://www.sysbio.se/piano/

Software R-package for running gene set analysis using various statistical methods, from different gene level statistics and a wide range of gene-set collections. The Piano package contains functions for combining the results of multiple runs of gene set analyses.

Proper citation: Piano (RRID:SCR_003200) Copy   

•   Source: SciCrunch Registry

http://www.allelebiotech.com/

A private company offering a wide variety of Molecular Biology reagents, fluorescent proteins, luciferase assay substrates, genotyping kits, and various custom services. The company isalso in the RNAi field with its recent patents in Pol III promoter-driven siRNA, shRNA, and miRNA. They introduced high titer lentivirus, camelid antibodies, and cell based assays and also offer Baculovirus protein expression and Gryphon retrovirus systems.

Proper citation: Allele Biotechnology (RRID:SCR_003164) Copy   

•   Source: SciCrunch Registry

http://cmb.molgen.mpg.de/2ndGenerationSequencing/Solas/

Software package for the statistical language R, devoted to the analysis of next generation short read data of RNA-seq transcripts. It provides predictions of alternative exons in a single condition/cell sample, predictions of differential alternative exons between two conditions/cell samples, and quantification of alternative splice forms in a single condition/cell sample.

Proper citation: Solas (RRID:SCR_003168) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/cancer/software/genepattern

A powerful genomic analysis platform that provides access to hundreds of tools for gene expression analysis, proteomics, SNP analysis, flow cytometry, RNA-seq analysis, and common data processing tasks. A web-based interface provides easy access to these tools and allows the creation of multi-step analysis pipelines that enable reproducible in silico research.

Proper citation: GenePattern (RRID:SCR_003201) Copy   

•   Source: SciCrunch Registry

http://cbio.mskcc.org/public/raetschlab/user/drewe/rdiff/

Software tool for detecting differential RNA processing from RNA-Seq data. It implements two statistical tests, rDiff.parametric and rDiff.nonparametric, to detect changes of the RNA processing between two samples.

Proper citation: rDiff (RRID:SCR_003162) Copy   

•   Source: SciCrunch Registry

http://www.histmed.org/

A professional association of historians, physicians, nurses, archivists, curators, librarians, and others that promotes and encourages research, study, writing, and interest in the history of medicine and allied fields.

Proper citation: American Association for the History of Medicine (RRID:SCR_003161) 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://biomed.emory.edu/PROGRAM_SITES/MSP/

The Molecular and Systems Pharmacology graduate program at Emory University offers broad training in the biomedical sciences for students interested in learning how the drugs of today work and how the novel therapeutics of tomorrow can be developed. The Program offers a specialization in toxicology. Emory University was recently rated by The Scientist magazine as the number 1 university in the world in terms of impact in pharmacology and toxicology research. Particular strengths within the MSP graduate school program at Emory include neuropharmacology, cancer biology, AIDS research, cardiovascular pharmacology, toxicology, and chemical biology. Ph.D. training in the Emory MSP program provides students with an ideal preparation for successful careers in the biotechnology and pharmaceutical industries as well as in academic research, teaching, government research, patent law and other disciplines that depend upon knowledge of fundamental pharmacological principles.

Proper citation: Emory University, Molecular and Systems Pharmacology (RRID:SCR_003351) Copy   

•   Source: SciCrunch Registry

http://www.gwumc.edu/pharm/

The Department of Pharmacology & Physiology at the George Washington University provides unique research opportunities at the predoctoral and postdoctoral levels. They also offer courses in Pharmacology and Physiology for Medical and Health Sciences students, as well as a number of graduate-level courses. Our mission is to provide the highest quality of educational opportunities to our community, and to advance scientific knowledge and improve human health through leading-edge research in the biomedical sciences.

Proper citation: George Washington University, Department of Pharmacology and Physiology (RRID:SCR_003358) 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