Tools   Select Another Resource Report Type

http://ctsaconnect.org/

THIS RESOURCE IS NO LONGER IS SERVICE. Documented on December 5th, 2022. Semantic framework to integrate information about research activities, clinical activities, and scientific resources to facilitate the production and consumption of Linked Open Data about investigators, physicians, biomedical research resources, services, and clinical activities. The goal is to enable software to consume data from multiple sources and allow the broadest possible representation of researchers'''' and clinicians'''' activities and research products. Current research tracking and networking systems rely largely on publications, but clinical encounters, reagents, techniques, specimens, model organisms, etc., are equally valuable for representing expertise. CTSAConnect will provide linkage between semantic representations of a wide range of clinical and research data using controlled vocabularies mapped to the Unified Medical Language System (UMLS) as a bridge between the two subject areas. The data sources include data from Medicaid, hospital billing systems, CTSAShareCenter, and other CTSA resource data, eagle-i and VIVO. It allows institutions to leverage existing tools and data sources by making the information they contain more discoverable and easier to integrate. For instance, with the ISF, researchers can be characterized by organizational affiliations, grant and project participation, research resources that they have generated, and publications that they have (co)-authored. Clinicians can be characterized by training and credentials, by clinical research topic, and by the kinds of procedures and specialization that can be inferred from encounter data. LOD refers to data that has been given a specific Uniform Resource Identifier (URI), for the purpose of sharing and linking data and information on the Semantic Web. While a large amount of data is published as LOD, there remains a significant gap in the representation of research resources and clinical expertise. Researchers can be characterized by the organization to which they belong, the grants and research in which they have participated, the research topics and research resources (reagents, biospecimens, animal models) they have generated, as well as the publications they have (co)-authored. Clinician profiles on the other hand, can be defined by their credentials, clinical research topics, and the kinds of procedures and specialization that can be inferred from clinical encounter data. They believe that integrating and relating this diversity of information sources and platforms requires addressing the overlap between research resources and the attributes and activities of researchers and clinicians. CTSAconnect aims to promote integration and discovery of research activities, resources, and clinical expertise. To this end, they will publish their ontologies and LOD via their website, which will also illustrate repeatable methods and examples of how to extract, consume, and utilize this valuable new LOD using freely available tools like VIVO, eagle-i, and Google APIs. CTSAconnect is a collaboration between Oregon Health & Science University, Stony Brook University, Cornell University, Harvard University, University at Buffalo, and the University of Florida, and leverages the work of eagle-i (eagle-i.net), VIVO (vivoweb.org), and ShareCenter (ctsasharecenter.org).

Proper citation: CTSAconnect (RRID:SCR_005225) Copy   

•   Source: SciCrunch Registry

http://www.qcmg.org/bioinformatics/tiki-index.php

A single nucleotide variant caller optimised for identifying somatic variants in low cellularity cancer samples.

Proper citation: qSNP (RRID:SCR_005105) Copy   

•   Source: SciCrunch Registry

http://www.damonrunyon.org/

The Damon Runyon Cancer Research Foundation funds early career cancer researchers who have the energy, drive and creativity to become leading innovators in their fields. We identify the best young scientists in the nation and support them through four award programs: our Fellowship, Pediatric Cancer Fellowship, Clinical Investigator and Innovation Awards. Damon Runyon awards give young scientists: * Freedom to follow their own ideas, explore new paths and take risks * A prestigious endorsement that attracts further funding, advances their careers and accelerates their research * Guaranteed financial support, sparing them hours applying for grants Since 1946, Damon Runyon has invested more than $240 million in the best young minds in the nation. Our alumni include 11 Nobel Laureates and leaders of major cancer centers across the United States. Many of our 3,300 scientists have gone on to make breakthroughs in the way we prevent, diagnose and treat many forms of cancer. The Damon Runyon Cancer Research Foundation is a registered nonprofit with 501(c)(3) status.

Proper citation: Damon Runyon Cancer Research Foundation (RRID:SCR_005106) Copy   

•   Source: SciCrunch Registry

http://samtools.sourceforge.net/mpileup.shtml

Provide various utilities for manipulating alignments in the SAM format, including sorting, merging, indexing and generating alignments in a per-position format.

Proper citation: SAMtools/BCFtools (RRID:SCR_005227) Copy   

•   Source: SciCrunch Registry

http://www.raetschlab.org/suppl/mitie

Software framework for simultaneous RNA-Seq-based Transcript Identification and Quantification in Multiple Samples. They define a likelihood function based on the negative binomial distribution, use a regularization approach to select a few transcripts collectively explaining the observed read data, and show how to find the optimal solution using Mixed Integer Programming. MiTie can a) take advantage of known transcripts, b) reconstruct and quantify transcripts simultaneously in multiple samples, as well as c) resolve the location of multi-mapping reads. It is designed for genome- and assembly-based transcriptome reconstruction.

Proper citation: MiTie (RRID:SCR_005228) Copy   

•   Source: SciCrunch Registry

http://orman.sourceforge.net/Home

A software tool for resolving multi-mappings within an RNA-Seq SAM file.

Proper citation: ORMAN (RRID:SCR_005188) Copy   

•   Source: SciCrunch Registry

https://github.com/vezzi/FRC_align

Software package containing tools to process bam files in order to evaluate and analyze de novo assembly / assemblers and identify Structural Variations suspicious genomics regions. The tools have been already successfully applied in several de novo and resequencing projects. This package contains two tools: # FRCbam: tool to compute Feature Response Curves in order to validate and rank assemblies and assemblers # FindTranslocations: tool to identify chromosomal rearrangements using Mate Pairs

Proper citation: FRCbam (RRID:SCR_005189) Copy   

•   Source: SciCrunch Registry

http://www.ukzn.ac.za/

University with five campuses in the province of KwaZulu-Natal in South Africa. It was formed on 1 January 2004 after the merger between the University of Natal and the University of Durban-Westville.

Proper citation: University of KwaZulu-Natal; Durban; South Africa (RRID:SCR_005222) Copy   

•   Source: SciCrunch Registry

http://seqant.genetics.emory.edu/

A free web service and open source software package that performs rapid, automated annotation of DNA sequence variants (single base mutations, insertions, deletions) discovered with any sequencing platform. Variant sites are characterized with respect to their functional type (Silent, Replacement, 5' UTR, 3' UTR, Intronic, Intergenic), whether they have been previously submitted to dbSNP, and their evolutionary conservation. Annotated variants can be viewed directly on the web browser, downloaded in a tab delimited text file, or directly uploaded in a Browser Extended Data (BED) format to the UCSC genome browser. SeqAnt further identifies all loci harboring two or more coding sequence variants that help investigators identify potential compound heterozygous loci within exome sequencing experiments. In total, SeqAnt resolves a significant bottleneck by allowing an investigator to rapidly prioritize the functional analysis of those variants of interest.

Proper citation: SeqAnt (RRID:SCR_005186) Copy   

•   Source: SciCrunch Registry

http://stothard.afns.ualberta.ca/downloads/NGS-SNP/

A collection of command-line scripts for providing rich annotations for SNPs identified by the sequencing of transcripts or whole genomes from organisms with reference sequences in Ensembl. Included among the annotations, several of which are not available from any existing SNP annotation tools, are the results of detailed comparisons with orthologous sequences. These comparisons allow, for example, SNPs to be sorted or filtered based on how drastically the SNP changes the score of a protein alignment. Other fields indicate the names of overlapping protein domains or features, and the conservation of both the SNP site and flanking regions. NCBI, Ensembl, and Uniprot IDs are provided for genes, transcripts, and proteins when applicable, along with Gene Ontology terms, a gene description, phenotypes linked to the gene, and an indication of whether the SNP is novel or known. A ?Model_Annotations? field provides several annotations obtained by transferring in silico the SNP to an orthologous gene, typically in a well-characterized species.

Proper citation: NGS-SNP (RRID:SCR_005182) Copy   

•   Source: SciCrunch Registry

http://www.broadinstitute.org/cancer/cga/indelocator

A software tool for calling short indels in next generation sequencing data.

Proper citation: Indelocator (RRID:SCR_005258) Copy   

•   Source: SciCrunch Registry

http://bioinformatics.knowledgeblog.org/

Series of tutorial material covering a broad selection of topics in bioinformatics. Categories include: Bioinformatics APIs, Data integration, Events, Grid and Cloud Computing, Metabolic modelling, Reviews, Software Comparisons, Statistics, Systems Biology, Transcriptomics, Tutorials.

Proper citation: Bioinformatics Knowledgeblog (RRID:SCR_005379) Copy   

•   Source: SciCrunch Registry

http://compbio.cs.brown.edu/projects/gasv/

Software tool combining both paired read and read depth signals into probabilistic model which can analyze multiple alignments of reads. Used to find structural variation in both normal and cancer genomes using data from variety of next-generation sequencing platforms. Used to predict structural variants directly from aligned reads in SAM/BAM format.Combines read depth information along with discordant paired read mappings into single probabilistic model two common signals of structural variation. When multiple alignments of read are given, GASVPro utilizes Markov Chain Monte Carlo procedure to sample over the space of possible alignments.

Proper citation: GASVPro (RRID:SCR_005259) Copy   

•   Source: SciCrunch Registry

http://bejerano.stanford.edu/prism/public/html/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 5,2022.Tool that predicts interactions between transcription factors and their regulated genes from binding motifs. Understanding vertebrate development requires unraveling the cis-regulatory architecture of gene regulation. PRISM provides accurate genome-wide computational predictions of transcription factor binding sites for the human and mouse genomes, and integrates the predictions with GREAT to provide functional biological context. Together, accurate computational binding site prediction and GREAT produce for each transcription factor: 1. putative binding sites, 2. putative target genes, 3. putative biological roles of the transcription factor, and 4. putative cis-regulatory elements through which the factor regulates each target in each functional role., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: PRISM (Stanford database) (RRID:SCR_005375) Copy   

•   Source: SciCrunch Registry

https://code.google.com/p/knime4bio/

A set of custom nodes for the KNIME (The Konstanz Information Miner) graphical workbench, for analysing next-generation sequencing (NGS) data without the requirement of programming skills.

Proper citation: Knime4Bio (RRID:SCR_005376) Copy   

•   Source: SciCrunch Registry

http://ergatis.sourceforge.net/

A web interface and scalable software system for bioinformatics workflows that is used to create, run, and monitor reusable computational analysis pipelines. It contains pre-built components for common bioinformatics analysis tasks. These components can be arranged graphically to form highly-configurable pipelines. Each analysis component supports multiple output formats, including the Bioinformatic Sequence Markup Language (BSML). The current implementation includes support for data loading into project databases following the CHADO schema, a highly normalized, community-supported schema for storage of biological annotation data. Ergatis uses the Workflow engine to process its work on a compute grid. Workflow provides an XML language and processing engine for specifying the steps of a computational pipeline. It provides detailed execution status and logging for process auditing, facilitates error recovery from point of failure, and is highly scalable with support for distributed computing environments. The XML format employed enables commands to be run serially, in parallel, and in any combination or nesting level.

Proper citation: Ergatis (RRID:SCR_005377) Copy   

•   Source: SciCrunch Registry

http://toolshed.g2.bx.psu.edu/repository/display_tool?repository_id=5d0de444b1f9ac52&tool_config=database%2Fcommunity_files%2F000%2Frepo_136%2Fcrest.xml&changeset_revision=4f6952e0af48

An algorithm for detecting genomic structural variations at base-pair resolution using next-generation sequencing data. CREST uses pieces of DNA called soft clips to find structural variations. Soft clips are the DNA segments produced during sequencing that fail to properly align to the reference genome as the sample genome is reassembled. CREST uses the soft clips to precisely identify sites of chromosomal rearrangement or where pieces of DNA are inserted or deleted.

Proper citation: CREST (RRID:SCR_005257) Copy   

•   Source: SciCrunch Registry

https://www.har.mrc.ac.uk/about/mammalian-genetics-unit

It is now widely known that animals share many genes with humans and can suffer from the same diseases, for example diabetes or deafness. Investigating these diseases in animals can provide vital leads to understanding both their causes and ways to treat them in humans. This approach to medical research lies at the heart of work at the MRC Mammalian Genetics Unit (MGU) at Harwell in Oxfordshire. In 1995 the MRC Radiobiology Unit was reconstituted to form two new units, the Radiation and Genome Stability Unit and the MGU. These opened in January 1996, together with the UK Mouse Genome Centre which is now part of MGU, making MRC Harwell a unique campus for multi-disciplinary genetics research. Since MGU's Director Steve Brown took the reins in 1996, the unit has dramatically expanded its scientific scope and increased its personnel from 40 to over 100. It now has 13 research programs encompassing molecular genetics, genomics, genetic manipulation and data analysis at all levels, from single genes to the whole genome. With a combination of cutting-edge facilities and expertise unrivaled in Europe, MGU Harwell has become firmly established as one of the world's leading academic centres for mouse genetics.

Proper citation: MRC Mammalian Genetics Unit (RRID:SCR_005378) Copy   

•   Source: SciCrunch Registry

http://mcb.berkeley.edu/labs/werblin/index.html

The goal of our research is to uncover the neural circuitry that mediates some of the remarkable processing capabilities of the retina. The retina to operates at high spatial and temporal resolution over more than 7 orders of magnitude, to detect the direction of motion, to blank and then recover after saccades, to generate at least a dozen different abstract representations of the visual world. How is all of this, and much more, possible in this tissuepaper-thin array of neurons? Videos and images describing this include: * The retinal hypercircuit. * How the Retina Works * Take a Tour through the Retina * Cartoon of the retina showing layering of neurons * Directional Selectivity * Feedback and Crossover inhibition * Multiple Representations of the Visual Scene ** Pseudo Array Recording Methods ** Multiple Representations of the Visual World ** Regions of Frequency Space * Regions of space/time frequency * Space-time rasters for ON and OFF cells * Patching a neuron in a retinal slice * Targeting Retinal Neuron Subregions with Arficial Rhodopsins

Proper citation: Werblin Lab (RRID:SCR_005251) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/projects/molbiolib/

A compact, portable, and extensively tested C++11 software framework and set of applications tailored to the demands of next-generation sequencing data and applicable to many other applications. It is designed to work with common file formats and data types used both in genomic analysis and general data analysis. A central relational-database-like Table class is a flexible and powerful object to intuitively represent and work with a wide variety of tabular datasets, ranging from alignment data to annotations. MolBioLib includes programs to perform a wide variety of analysis tasks such as computing read coverage, annotating genomic intervals, and novel peak calling with a wavelet algorithm. This package assumes fluency in both UNIX and C++.

Proper citation: MolBioLib (RRID:SCR_005372) Copy   

•   Source: SciCrunch Registry