Tools   Select Another Resource Report Type

http://seqbarracuda.sourceforge.net/

A sequence mapping software that utilizes the massive parallelism of graphics processing units to accelerate the inexact alignment of short sequence reads to a particular location on a reference genome. It can align a paired-end library containing 14 million pairs of 76bp reads to the Human genome in about 27 minutes (from fastq files to SAM alignment) using a ��380 NVIDIA Geforce GTX 680*. The alignment throughput can be boosted further by using multiple GPUs (up to 8) at the same time. Being based on BWA (http://bio-bwa.sf.net) from the Sanger Institute, BarraCUDA delivers a high level of alignment fidelity and is comparable to other mainstream alignment programs. It can perform gapped alignment with gap extensions, in order to minimise the number of false variant calls in re-sequencing studies.

Proper citation: BarraCUDA (RRID:SCR_006881) Copy   

•   Source: SciCrunch Registry

http://code.google.com/p/crop-tingchenlab/

A clustering tool designed mainly for Metagenomics studies, which clusters 16S rRNA sequences into Operational Taxonomic Units (OTU). By using a Gaussian Mixture model, CROP can automatically determine the best clustering result for 16S rRNA sequences at different phylogenetic levels without setting a hard cutoff threshold as hierarchical clustering does. Yet, at the same time, it is able to manage large datasets and to overcome sequencing errors.

Proper citation: CROP (RRID:SCR_006916) Copy   

•   Source: SciCrunch Registry

http://sourceforge.net/p/fastsemsim/home/Home/

A package that implements several semantic similarity measures. It is both a library and an end-user application, featuring an intuitive graphical user interface (GUI). It has been implemented with the aim of being fast, expandable, and easy to use. It allows the user to work with the most updated version of GO database and customizable annotation corpora. It provides a set of logically-organized classes that can be easily exploited to both integrate semantic similarity into different analysis pipelines and extend the library with new measures. Platform: Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: FastSemSim (RRID:SCR_006919) Copy   

•   Source: SciCrunch Registry

http://www.cjdats.org

A cooperative research program to explore the issues related to the complex system of offender treatment services. Nine research centers and a Coordinating Center were created in partnership with researchers, criminal justice professionals, and drug abuse treatment practitioners to form a national research infrastructure. The establishment of CJ-DATS is an outstanding example of cooperation among Federal agencies with the research community... We need to understand how to provide better drug treatment services for criminal justice offenders to alter their drug use and criminal behavior. - Dr. Nora Volkow, Director of NIDA. CJ-DATS PHASE I In 2002, NIDA launched the National Criminal Justice����������Drug Abuse Treatment Studies (CJ-DATS). CJ-DATS is a multisite research program aimed at improving the treatment of offenders with drug use disorders and integrating criminal justice and public health responses to drug involved offenders. From 2002 through 2008, CJ-DATS researchers from 9 research centers, a coordinating center, and NIDA worked together with federal, state, and local criminal justice partners to develop and test integrated approaches to the treatment of offenders with drug use disorders. The areas that were studied included: * Assessing Offender Problems * Measuring Progress in Treatment and Recovery * Linking Criminal Justice and Drug Abuse Treatment * Adolescent Interventions * HIV and Hepatitis Risk Reduction * Understanding Systems CJ-DATS PHASE II In 2008, CJ-DATS began to focus on the problems of implementing research-based practices drug treatment practices. This research concerns the organizational and systems processes involved in implementing valid, evidence-based practices to reduce drug use and drug-related recidivism for individuals in the criminal justice system. 12 CJ-DATS Research Centers are conducting implementation research in three primary domains: * Research to improve the implementation of evidence-based assessment processes for offenders with drug problems * Implementing effective treatment for drug-involved offenders * Implementing evidence-based interventions to improve an HIV continuum-of-care for offenders

Proper citation: Criminal Justice Drug Abuse Treatment Studies (RRID:SCR_006996) Copy   

•   Source: SciCrunch Registry

http://www.stanleyresearch.org/dnn/Default.aspx?tabid=203

The Stanley Medical Research Institute (SMRI) is a nonprofit organization supporting research on the causes of, and treatments for, schizophrenia and bipolar disorder. Since it began in 1989, SMRI has supported more than $300 million in research in over 30 countries around the world. It is the largest nongovernmental source of funds for research on these diseases in the United States. Schizophrenia and bipolar disorder are the most important psychiatric disorders in the United States, affecting more than 4 million people at any given time. Until recent years, little research had been done on these diseases, and the treatment of them was unsatisfactory. The neuroscience revolution has brought with it great opportunities for increased understanding of brain diseases such as schizophrenia and bipolar disorder. SMRI is on the leading edge of this exciting research. Approximately 75 percent of SMRI expenditures goes towards the development of new treatments for schizophrenia and bipolar disorder. The remaining funds are used for research on the causes of these diseases. SMRI has a close relationship with and is the supporting organization for the Treatment Advocacy Center (TAC). The Treatment Advocacy Center is a nonprofit organization dedicated to eliminating barriers to the timely and effective treatment of severe psychiatric disorders. TAC promotes laws, policies, and practices for the delivery of psychiatric care and supports the development of innovative treatments for and research into the causes of severe and persistent psychiatric disorders, such as schizophrenia and bipolar disorder.

Proper citation: Stanley Medical Research Institute (RRID:SCR_007047) Copy   

•   Source: SciCrunch Registry

http://ardb.cbcb.umd.edu

The goals of Antibiotic Resistance Genes Database (ARGB) are to provide a centralized compendium of information on antibiotic resistance, to facilitate the consistent annotation of resistance information in newly sequenced organisms, and also to facilitate the identification and characterization of new genes. ARGB contains six types of database groups: - Resistance Type: This database contains information, such as resistance profile, mechanism, requirement, epidemiology for each type. - Resistance Gene: This database contains information, such as resistance profile, resistance type, requirement, protein and DNA sequence for each gene.This database only includes NON-REDUNDANT, NON-VECTOR, COMPLETE genes. - Antibiotic: This database contains information, such as producer, action mechanism, resistance type, for each gene. - Resistance Gene(NonRD): This database contains the same information as Resistance Gene. It does NOT include NON-REDUNDANT, NON-VECTOR genes, but includes INCOMPLETE genes. - Resistance Gene(ALL): This database contains the same information as Resistance Gene. It includes all REDUNDANT, VECTOR AND INCOMPLETE genes. - Resistance Species: This database contains resistance profile and corresponding resistance genes for each species. Furthermore, ARDB also contians three types BLAST database: - Resistance Genes Complete: Contains only NON-REDUNDANT, NON-VECTOR, COMPLETE genes sequences. - Resistance Genes Non-redundant: Contains NON-REDUNDANT, NON-VECTOR, COMPLETE, INCOMPLETE genes sequences. - Resistance Genes All: Contains all REDUNDANT, VECTOR, COMPLETE, INCOMPLETE genes sequences. Lastly, ARDB provides four types of Analytical tools: - Normal BLAST: This function allows an user to input a DNA or protein sequence, and find similar DNA (Nucleotide BLAST) or protein (Protein BLAST) sequences using blastn, blastp, blastx, tblastn, tblastx - RPS BLAST: A web RPSBLAST (RPS BLAST) interface is provided to align a query sequence against the Position Specific Scoring Matrix (PSSM) for each type. Normally, this will give the same annotation information as using regular BLAST mentioned above. - Multiple Sequences BLAST (Genome Annotation): This function allows an user to annotate multiple (less than 5000) query sequences in FASTA format. - Mutation Resistance Identification: This function allows an user to identify mutations that will cause potential antibiotic resistance, for 12 genes (16S rRNA, 23S rRNA, gyrA, gyrB, parC, parE, rpoB, katG, pncA, embB, folP, dfr). ������ :Sponsors: ARDB is funded by Uniformed Services University of the Health Sciences, administered by the Henry Jackson Foundation. :

Proper citation: Antibiotic Resistance Genes Database (RRID:SCR_007040) Copy   

•   Source: SciCrunch Registry

http://xin.cz3.nus.edu.sg/group/drt/dart.asp

Database that provides comprehensive information about adverse effect targets of drugs described in the literature, including information about known drug adverse reaction targets, functions and properties. Moreover, proteins involved in adverse effect targets of chemicals not yet confirmed as adverse drug reaction (ADR) targets are also included as potential targets. Associated references are also included. This database gives physiological function of each target, binding drugs / agonists / antagonists / activators / inhibitors, IC(50) values of the inhibitors, corresponding adverse effects, and type of ADR induced by drug binding to a target. Cross-links to other databases are also introduced to facilitate the access of information about the sequence, 3-dimensional structure, function, and nomenclature of each target along with drug/ligand binding properties, and related literature. Each entry can be retrieved through multiple search methods including target name, target physiological function, adverse effect, ligand name, and biological pathways. A special page is provided for contribution of new or additional information. Function for ADR-target prediction by SVMDART: Submit protein primary sequence for ADR-related protein prediction.

Proper citation: DART - Drug Adverse Reaction Targets (RRID:SCR_007041) Copy   

•   Source: SciCrunch Registry

http://seer.cancer.gov/

SEER collects cancer incidence data from population-based cancer registries covering approximately 47.9 percent of the U.S. population. The SEER registries collect data on patient demographics, primary tumor site, tumor morphology, stage at diagnosis, and first course of treatment, and they follow up with patients for vital status.There are two data products available: SEER Research and SEER Research Plus. This was motivated because of concerns about the increasing risk of re-identifiability of individuals. The Research Plus databases require more rigorous process for access that includes user authentication through Institutional Account or multiple-step request process for Non-Institutional users.

Proper citation: Surveillance Epidemiology and End Results (RRID:SCR_006902) Copy   

•   Source: SciCrunch Registry

http://geneontology.org/docs/tools-overview/

Collection of tools developed by GO Consortium and by third parties. Tools are listed by category or alphabetically and continue to be improved and expanded.

Proper citation: Gene Ontology Tools (RRID:SCR_006941) Copy   

•   Source: SciCrunch Registry

https://www.siumed.edu/pharm

Pharmacology research teams and faculty study mechanisms of disease, develop and test novel drug therapies and seek to understand and prevent drug interactions. They also educate and train medical students, graduate students, residents and fellows in field of pharmacology.

Proper citation: Southern Illinois University School of Medicine Department of Pharmacology (RRID:SCR_006940) Copy   

•   Source: SciCrunch Registry

http://gtrnadb.ucsc.edu

This genomic tRNA database contains tRNA gene predictions made by the program tRNAscan-SE (Lowe & Eddy, Nucl Acids Res 25: 955-964, 1997) on complete or nearly complete genomes. Unless otherwise noted, all annotation is automated, and has not been inspected for agreement with published literature. Transfer RNAs (tRNAs) represent the single largest, best-understood class of non-protein coding RNA genes found in all living organisms. By far, the major source of new tRNAs is computational identification of genes within newly sequenced genomes. To organize the rapidly growing collection and enable systematic analyses, we created the Genomic tRNA Database (GtRNAdb). The web resource provides overview statistics of tRNA genes within each analyzed genome, including information by isotype and genetic locus, easily downloadable primary sequences, graphical secondary structures and multiple sequence alignments. Direct links for each gene to UCSC eukaryotic and microbial genome browsers provide graphical display of tRNA genes in the context of all other local genetic information. The database can be searched by primary sequence similarity, tRNA characteristics or phylogenetic group. Inevitably with automated sequence analysis, we find exceptions to general identification rules, isoacceptor type predictions (esp. due to variable post-transcriptional anticodon modification), and questionable tRNA identifications (due to pseudogenes, SINES, or other tRNA-derived elements). We attempt to document all cases we come across, and welcome feedback on new or unrecognized discrepancies.

Proper citation: GtRNAdb - Genomic tRNA Database (RRID:SCR_006939) Copy   

•   Source: SciCrunch Registry

http://www.europhenome.org

Open source software system for capturing, storing and analyzing raw phenotyping data from SOPs contained in EMPReSS, it provides access to raw and annotated mouse phenotyping data generated from primary pipelines such as EMPReSSlim and secondary procedures from specialist centers. Mutants of interest can be identified by searching the gene or the predicted phenotype. You can also access phenotype data from the EMPReSSlim Pipeline for inbred mouse strains. Initially EuroPhenome was developed within the EUMORPHIA programme to capture and store pilot phenotyping data obtained on four background strains (C57BL/6J, C3H/HeBFeJ, BALB/cByJ and 129/SvPas). EUMORPHIA (European Union Mouse Research for Public Health and Industrial Applications) was a large project comprising of 18 research centers in 8 European countries, with the main focus of the project being the development of novel approaches in phenotyping, mutagenesis and informatics to improve the characterization of mouse models for understanding human molecular physiology and pathology. The current version of EuroPhenome is capturing data from the EUMODIC project as well as the WTSI MGP, HMGU GMC pipeline and the CMHD. EUMODIC is undertaking a primary phenotype assessment of up to 500 mouse mutant lines derived from ES cells developed in the EUCOMM project as well as other lines. Lines showing an interesting phenotype will be subject to a more in depth assessment. EUMODIC is building upon the comprehensive database of standardized phenotyping protocols, called EMPReSS, developed by the EUMORPHIA project. EUMODIC has developed a selection of these screens, called EMPReSSslim, to enable comprehensive, high throughput, primary phenotyping of large numbers of mice. Phenovariants are annotated using a automated pipeline, which assigns a MP term if the mutant data is statistically different to the baseline data. This data is shown in the Phenomap and the mine for a mutant tool. Please note that a statistically significant result and the subsequent MP annotation does not necessarily mean a true phenovariant. There are other factors that could cause this result that have not been accounted for in the analysis. It is the responsibility of the user to download the data and use their expert knowledge or further analysis to decide whether they agree or not. EuroPhenome is primarily based in the bioinformatics group at MRC Harwell. The development of EuroPhenome is in collaboration with the Helmholtz Zentrum Munchen, Germany, the Wellcome Trust Sanger Institute, UK and the Institut Clinique de la Souris, France.

Proper citation: Europhenome Mouse Phenotyping Resource (RRID:SCR_006935) Copy   

•   Source: SciCrunch Registry

http://scalablebrainatlas.incf.org/

A web-based, interactive brain atlas viewer, containing a growing number of atlas templates for various species, including mouse, macaque and human. Standard features include fast brain region lookup, point and click to select a region and view its full 3D extent, mark a stereotaxic coordinate and view all regions in a hierarchy. Built-in extensions are the CoCoMac plugin, which provides a spatial display of Macaque connectivity, and a service to transform stereotaxic coordinates to and from the INCF Waxholm space for the mouse. Three dimensional renderings of brain regions are available through a Matlab interface (local installation of Matlab required). The SBA is designed to be customizable. External users can create plugins, hosted on their own servers, to interactively attach images or data to spatial atlas locations. This fully web-based display engine for brain atlases and topologies allows client websites to show brain region related data in a 3D interactive context. Currently available atlases are: * Macaque: The Paxinos Rhesus Monkey atlas (2000) * Macaque: Various templates available through Caret, registered to F99 space: Felleman and Van Essen (1991), Lewis and Van Essen (2000), Regional Map from K��tter and Wanke (2005), Paxinos Rhesus Monkey (2000) * Macaque: The NeuroMaps Macaque atlas (2008) * Mouse: The INCF Waxholm Space for the mouse (2011). Previous versions available. * Mouse: The Allen Mouse Brain volumetric atlas (ABA07) * Human: The LPBA40 parcellation, registered to SRI24 space A variety of services are being developed around the templates contained in the Scalable Brain Atlas. For example, you can include thumbnails of brain regions in your own webpage. Other applications include: * Analyze atlas templates in Matlab * List all regions belonging to the given template * List of supported atlas templates * Find region by coordinate * Color-coded PNG (bitmap) or SVG (vector) image of a brain atlas slice * Region thumbnail in 2D (slice) or 3D (stack of slices) The Scalable Brain Atlas is created by Rembrandt Bakker and Gleb Bezgin, under supervision of Rolf K��tter in the NeuroPhysiology and -Informatics group of the Donders Institute, Radboud UMC Nijmegen.

Proper citation: Scalable Brain Atlas (RRID:SCR_006934) Copy   

•   Source: SciCrunch Registry

http://autismkb.cbi.pku.edu.cn/

Genetic factors contribute significantly to ASD. AutismKB is an evidence-based knowledgebase of Autism spectrum disorder (ASD) genetics. The current version contains 2193 genes (99 syndromic autism related genes and 2135 non-syndromic autism related genes), 4617 Copy Number Variations (CNVs) and 158 linkage regions associated with ASD by one or more of the following six experimental methods: # Genome-Wide Association Studies (GWAS); # Genome-wide CNV studies; # Linkage analysis; # Low-scale genetic association studies; # Expression profiling; # Other low-scale gene studies. Based on a scoring and ranking system, 99 syndromic autism related genes and 383 non-syndromic autism related genes (434 genes in total) were designated as having high confidence. Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder with a prevalence of 1.0-2.6%. The three core symptoms of ASD are: # impairments in reciprocal social interaction; # communication impairments; # presence of restricted, repetitive and stereotyped patterns of behavior, interests and activities.

Proper citation: AutismKB (RRID:SCR_006937) Copy   

•   Source: SciCrunch Registry

http://www.dkfz.de/en/mga/Groups/LIFEdb-Database.html

Database that integrates large-scale functional genomics assays and manual cDNA annotation with bioinformatics gene expression and protein analysis. LifeDB integrates data regarding full length cDNA clones and data on expression of encoded protein and their subcellular localization on mammalian cell line. LifeDB enables the scientific community to systematically search and select genes, proteins as well as cDNA of interest by specific database identifiers as well as gene name. It enables to visualize cDNA clone and subcellular location of proteins. It also links the results to external biological databases in order to provide a broader functional information. LifeDB also provides an annotation pipeline which facilitates an improved mapping of clones to known human reference transcripts from the RefSeq database and the Ensembl database. An advanced web interface enables the researchers to view the data in a more user friendly manner. Users can search using any one of the following search options available both in Search gene and cDNA clones and Search Sub-cellular locations of human proteins: By Keyword, By gene/transcript identifier, By plate name, By clone name, By cellular location. * The Search genes and cDNA clones results include: Gene Name, Ensemble ID, Genomic Region, Clone name, Plate name, Plate position, Classification class, Synonymous SNP''s, Non- synonymous SNP''s, Number of ambiguous positions, and Alignment with reference genes. * The Search sub-cellular locations of human proteins results include: Subcellular location, Gene Name, Ensemble ID, Clone name, True localization, Images, Start tag and End tag. Every result page has an option to download result data (excluding the microscopy images). On click of ''Download results as CSV-file'' link in the result page the user will be given a choice to open or save result data in form of a CSV (Comma Separated Values) file. Later the CSV file can be easily opened using Excel or OpenOffice.

Proper citation: LifeDB (RRID:SCR_006899) Copy   

•   Source: SciCrunch Registry

http://www.iobis.org/

Accepts and provides access to biogeographic data collected throughout the global oceans. The datasets are integrated so you can search them all seamlessly by species name, higher taxonomic level, geographic area, depth, and time; and then map and find environmental data related to the locations. Created by the Census of Marine Life, OBIS is now part of the Intergovernmental Oceanographic Commission (IOC) of UNESCO, under its International Oceanographic Data and Information Exchange (IODE) programme

Proper citation: OBIS (RRID:SCR_006933) Copy   

•   Source: SciCrunch Registry

http://www.cdc.gov/labstandards/diabetes_dasp.html

Program that develops materials and methods to improve measurements of autoantibodies that are predictive of type 1 diabetes. These are the most sensitive and meaningful measures for predicting this disease. Historically, autoantibody measures have been variable among laboratories; therefore, this program, in collaboration with the Immunology of Diabetes Society, was established. The goals of DASP are to improve laboratory methods, evaluate laboratory performance, support the development of sensitive and specific measurement technologies, and develop reference methods. Currently, 48 key laboratories from 19 countries participate in DASP.

Proper citation: Diabetes Autoantibody Standardization Program (RRID:SCR_006929) Copy   

•   Source: SciCrunch Registry

http://purl.bioontology.org/ontology/WB-PHENOTYPE

A structured controlled vocabulary of Caenorhabditis elegans phenotypes.

Proper citation: C. elegans Phenotype Vocabulary (RRID:SCR_006924) Copy   

•   Source: SciCrunch Registry

http://bric.unc.edu/ideagroup/free-softwares/ABSORB/

This software package implements an algorithm for effective groupwise registration. The required input is a set of 3D MR intensity images (in Analyze format with paired .hdr and .img files) with a text file (.txt) listing all header file (.hdr) names. The output is the set of registered images together with the corresponding dense deformation fields. This software has been tested on Windows XP (32-bit) and Linux (64-bit, kernel version 2.6.18-194.el5). The images should be pre-processed before applying ABSORB: * All brain MR images used as inputs to ABSORB should be in the same situation (e.g., skull-stripped or not, cerebellum removed or not, etc.). * The input images should be in Analyze format with paired header and image files. This software was developed in IDEA group in UNC-Chapel Hill.

Proper citation: ABSORB: Atlas Building by Self-Organized Registration and Bundling (RRID:SCR_007018) Copy   

•   Source: SciCrunch Registry

http://openccdb-dev-web.crbs.ucsd.edu/software/index.shtm

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 4th,2023. Software to support registering brain images to the stereotaxic coordinate system of a brain atlas. It was specifically designed to work with the large scale brain mosaics. When data are uploaded to the CCDB, users may launch Jibber, a custom tool for defining correspondence points between the image and an atlas overlay. Jibber automatically downsamples the data, so that users can define the warping and scaling parameters with good interactive performance on the smaller copy. Once the warping transformation is computed, the original image and the transformation matrix are sent to a cluster of computers for warping. The current version of Jetsam is running on a 30 Sun V20 nodes and the execution time is roughly about 20 minutes per GB. The warped images are then automatically registered with an image web server that supports spatial queries based on stereotaxic coordinates. These servers generate optimized downsampled images, which can be displayed by standard online clients regardless of the size of the original image.

Proper citation: Image Workflow (RRID:SCR_007017) Copy   

•   Source: SciCrunch Registry