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http://griffin.cbrc.jp/

Griffin (G-protein-receptor interacting feature finding instrument) is a high-throughput system to predict GPCR - G-protein coupling selectively with the input of GPCR sequence and ligand molecular weight. This system consists of two parts: 1) HMM section using family specific multiple alignment of GPCRs, 2) SVM section using physico-chemical feature vectors in GPCR sequence. G-protein coupled receptors (GPCR), which is composed of seven transmembrane helices, play a role as interface of signal transduction. The external stimulation for GPCR, induce the coupling with G-protein (Gi/o, Gq/11, Gs, G12/13) followed by different kinds of signal transduction to inner cell. About half of distributed drugs are intending to control this GPCR - G-protein binding system, and therefore this system is important research target for the development of effective drug. For this purpose, it is necessary to monitor, effectively and comprehensively, of the activation of G-protein by identifying ligand combined with GPCR. Since, at present, it is difficult to construct such biochemical experiment system, if the answers for experimental results can be prepared beforehand by using bioinformatics techniques, large progress is brought to G-protein related drug design. Previous works for predicting GPCR-G protein coupling selectivity are using sequence pattern search, statistical models, and HMM representations showed high sensitivity of predictions. However, there are still no works that can predict with both high sensitivity and specificity. In this work we extracted comprehensively the physico-chemical parameters of each part of ligand, GPCR and G-protein, and choose the parameters which have strong correlation with the coupling selectivity of G-protein. These parameters were put as a feature vector, used for GPCR classification based on SVM.

Proper citation: G protein receptor interaction feature finding instrument (RRID:SCR_008343) Copy   

•   Source: SciCrunch Registry

https://CRAN.R-project.org/package=gma

Software package to perform Granger mediation analysis for time series. Includes single level GMA model and two-level GMA model, for time series with hierarchically nested structure.

Proper citation: GMA (RRID:SCR_009212) Copy   

•   Source: SciCrunch Registry

http://clipserve.clip.ubc.ca/topfind

An integrated knowledgebase focused on protein termini, their formation by proteases and functional implications. It contains information about the processing and the processing state of proteins and functional implications thereof derived from research literature, contributions by the scientific community and biological databases. It lists more than 120,000 N- and C-termini and almost 10,000 cleavages. TopFIND is a resource for comprehensive coverage of protein N- and C-termini discovered by all available in silico, in vitro as well as in vivo methodologies. It makes use of existing knowledge by seamless integration of data from UniProt and MEROPS and provides access to new data from community submission and manual literature curating. It renders modifications of protein termini, such as acetylation and citrulination, easily accessible and searchable and provides the means to identify and analyse extend and distribution of terminal modifications across a protein. The data is presented to the user with a strong emphasis on the relation to curated background information and underlying evidence that led to the observation of a terminus, its modification or proteolytic cleavage. In brief the protein information, its domain structure, protein termini, terminus modifications and proteolytic processing of and by other proteins is listed. All information is accompanied by metadata like its original source, method of identification, confidence measurement or related publication. A positional cross correlation evaluation matches termini and cleavage sites with protein features (such as amino acid variants) and domains to highlight potential effects and dependencies in a unique way. Also, a network view of all proteins showing their functional dependency as protease, substrate or protease inhibitor tied in with protein interactions is provided for the easy evaluation of network wide effects. A powerful yet user friendly filtering mechanism allows the presented data to be filtered based on parameters like methodology used, in vivo relevance, confidence or data source (e.g. limited to a single laboratory or publication). This provides means to assess physiological relevant data and to deduce functional information and hypotheses relevant to the bench scientist. TopFIND PROVIDES: * Integration of protein termini with proteolytic processing and protein features * Displays proteases and substrates within their protease web including detailed evidence information * Fully supports the Human Proteome Project through search by chromosome location CONTRIBUTE * Submit your N- or C-termini datasets * Contribute information on protein cleavages * Provide detailed experimental description, sample information and raw data

Proper citation: TopFIND (RRID:SCR_008918) Copy   

•   Source: SciCrunch Registry

http://go.princeton.edu/cgi-bin/GOTermFinder

The Generic GO Term Finder finds the significant GO terms shared among a list of genes from an organism, displaying the results in a table and as a graph (showing the terms and their ancestry). The user may optionally provide background information or a custom gene association file or filter evidence codes. This tool is capable of batch processing multiple queries at once. GO::TermFinder comprises a set of object-oriented Perl modules GO::TermFinder can be used on any system on which Perl can be run, either as a command line application, in single or batch mode, or as a web-based CGI script. This implementation, developed at the Lewis-Sigler Institute at Princeton, depends on the GO-TermFinder software written by Gavin Sherlock and Shuai Weng at Stanford University and the GO:View module written by Shuai Weng. It is made publicly available through the GMOD project. The full source code and documentation for GO:TermFinder are freely available from http://search.cpan.org/dist/GO-TermFinder/. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: Generic GO Term Finder (RRID:SCR_008870) Copy   

•   Source: SciCrunch Registry

http://plantgrn.noble.org/LegumeIP/

LegumeIP is an integrative database and bioinformatics platform for comparative genomics and transcriptomics to facilitate the study of gene function and genome evolution in legumes, and ultimately to generate molecular based breeding tools to improve quality of crop legumes. LegumeIP currently hosts large-scale genomics and transcriptomics data, including: * Genomic sequences of three model legumes, i.e. Medicago truncatula, Glycine max (soybean) and Lotus japonicus, including two reference plant species, Arabidopsis thaliana and Poplar trichocarpa, with the annotation based on UniProt TrEMBL, InterProScan, Gene Ontology and KEGG databases. LegumeIP covers a total 222,217 protein-coding gene sequences. * Large-scale gene expression data compiled from 104 array hybridizations from L. japonicas, 156 array hybridizations from M. truncatula gene atlas database, and 14 RNA-Seq-based gene expression profiles from G. max on different tissues including four common tissues: Nodule, Flower, Root and Leaf. * Systematic synteny analysis among M. truncatula, G. max, L. japonicus and A. thaliana. * Reconstruction of gene family and gene family-wide phylogenetic analysis across the five hosted species. LegumeIP features comprehensive search and visualization tools to enable the flexible query on gene annotation, gene family, synteny, relative abundance of gene expression.

Proper citation: LegumeIP (RRID:SCR_008906) Copy   

•   Source: SciCrunch Registry

http://hymenopteragenome.org/beebase/

Gene sequences and genomes of Bombus terrestris, Bombus impatiens, Apis mellifera and three of its pathogens, that are discoverable and analyzed via genome browsers, blast search, and apollo annotation tool. The genomes of two additional species, Apis dorsata and A. florea are currently under analysis and will soon be incorporated.BeeBase is an archive and will not be updated. The most up-to-date bee genome data is now available through the navigation bar on the HGD Home page.

Proper citation: BeeBase (RRID:SCR_008966) Copy   

•   Source: SciCrunch Registry

http://pages.stat.wisc.edu/~yandell/qtl/software/qtlbim/

Software library for QTL Bayesian Interval Mapping that provides a Bayesian model selection approach to map multiple interacting QTL. It works on experimentally inbred lines and performs a genome-wide search to locate multiple potential QTL. The package can handle continuous, binary and ordinal traits. (entry from Genetic Analysis Software)

Proper citation: R/QTLBIM (RRID:SCR_009375) Copy   

•   Source: SciCrunch Registry

https://github.com/lpantano/seqbuster

Software tool for processing and analysis of small RNAs datasets.Reveals ubiquitous miRNA modifications in human embryonic cells.

Proper citation: SeqBuster (RRID:SCR_009616) Copy   

•   Source: SciCrunch Registry

http://www.sph.umich.edu/csg/abecasis/MACH/download/

QTL analysis based on imputed dosages/posterior_probabilities.

Proper citation: MACH (RRID:SCR_009621) Copy   

•   Source: SciCrunch Registry

http://beast.bio.ed.ac.uk/

A cross-platform software program for Bayesian MCMC analysis of molecular sequences. It is entirely orientated towards rooted, time-measured phylogenies inferred using strict or relaxed molecular clock models. It can be used as a method of reconstructing phylogenies but is also a framework for testing evolutionary hypotheses without conditioning on a single tree topology. BEAST uses MCMC to average over tree space, so that each tree is weighted proportional to its posterior probability. We include a simple to use user-interface program for setting up standard analyses and a suit of programs for analysing the results.

Proper citation: BEAST (RRID:SCR_010228) Copy   

•   Source: SciCrunch Registry

http://weblogo.berkeley.edu

Web application to generate sequence logos, graphical representations of patterns within multiple sequence alignment. Designed to make generation of sequence logos easy. Sequence logo generator.

Proper citation: WEBLOGO (RRID:SCR_010236) Copy   

•   Source: SciCrunch Registry

http://pathema.jcvi.org/Pathema/index.html

Pathema is one of the eight Bioinformatics Resource Centers designed to serve as a core resource for the bio-defense and infectious disease research community. Pathema strives to support basic research and accelerate scientific progress for understanding, detecting, diagnosing and treating an established set of six target NIAID Category A-C pathogens: Category A priority pathogens; Bacillus anthracis and Clostridium botulinum, and Category B priority pathogens; Burkholderia mallei, Burkholderia pseudomallei, Clostridium perfringens and Entamoeba histolytica. Each target pathogen is represented in one of four distinct clade-specific Pathema web resources and underlying databases developed to target the specific data and analysis needs of each scientific community. All publicly available complete genome projects of phylogenetically related organisms are also represented, providing a comprehensive collection of organisms for comparative analyses. Pathema facilitates the scientific exploration of genomic and related data through its integration with web-based analysis tools, customized to obtain, display, and compute results relevant to ongoing pathogen research. Pathema serves the bio-defense and infectious disease research community by disseminating data resulting from pathogen genome sequencing projects and providing access to the results of inter-genomic comparisons for these organisms. The Pathema BRC contract ends in December 2009. At that time JCVI will cease maintenance of the Pathema web resource and data. The PATRIC team, located at the Virginia Bioinformatics Institute, created and maintains a consolidated BRC for all of the NIAID category A-C priority pathogenic bacteria. The EuPathDB team at the University of Pennsylvania will support all eukaryotic pathogens. Pathema transferred all data and software to PATRIC and EuPathDB for incorporation into their new Web-based bioinformatics resource.

Proper citation: Pathema (RRID:SCR_010585) Copy   

•   Source: SciCrunch Registry

http://www.bcgsc.ca/platform/bioinfo/software/abyss

Software providing de novo, parallel, paired-end sequence assembler that is designed for short reads. ABySS 1.0 originally showed that assembling human genome using short 50 bp sequencing reads was possible by aggregating half terabyte of compute memory needed over several computers using standardized message passing system. ABySS 2.0 is Resource Efficient Assembly of Large Genomes using Bloom Filter. ABySS 2.0 departs from MPI and instead implements algorithms that employ Bloom filter, probabilistic data structure, to represent de Bruijn graph and reduce memory requirements.

Proper citation: ABySS (RRID:SCR_010709) Copy   

•   Source: SciCrunch Registry

http://bioinf.wehi.edu.au/limma/

Software package for the analysis of gene expression microarray data, especially the use of linear models for analyzing designed experiments and the assessment of differential expression.

Proper citation: LIMMA (RRID:SCR_010943) Copy   

•   Source: SciCrunch Registry

http://mutpred.mutdb.org/

Web application tool developed to classify an amino acid substitution as disease-associated or neutral in human.

Proper citation: MutPred (RRID:SCR_010778) Copy   

•   Source: SciCrunch Registry

http://www.arrayanalysis.org/

Tools for microarray quality control and pre-processing.

Proper citation: ArrayAnalysis.org (RRID:SCR_010932) Copy   

•   Source: SciCrunch Registry

http://snpanalyzer.uthsc.edu/

A tool to predict whether a nonsynonymous single nucleotide polymorphism (nsSNP) has a phenotypic effect.

Proper citation: nsSNPAnalyzer (RRID:SCR_010780) Copy   

•   Source: SciCrunch Registry

http://icy.bioimageanalysis.org/

An open community platform for bioimage informatics providing the software resources to visualize, annotate and quantify bioimaging data. To bridge the gap between developers and users, it combines: a) an open-source image analysis software, offering a powerful and flexible environment for developers such as applied mathematicians to write algorithms fast and efficiently; b) a common set of tools to view and manipulate data, and a set of plugins to perform specific quantification or analysis on images; c) a community-based website centralizing all plugins and resources to facilitate their management and maximize their visibility towards users. Workspaces are virtual groups of plugins dedicated to a specific application or image processing domain. By downloading a workspace, ICY automatically installs all corresponding plugins. The workspaces are enabled, but the editing section is not ready yet. If you want to publish a plugin on this website, its code has to be GPL. Source code is available and provided in each application download.

Proper citation: icy (RRID:SCR_010587) Copy   

•   Source: SciCrunch Registry

http://www.geneious.com/

Software package for sequence alignment, assembly and analysis. Integrated and extendable desktop software platform for organization and analysis of sequence data. Bioinformatics software platform packed with molecular biology and sequence analysis tools.

Proper citation: Geneious (RRID:SCR_010519) Copy   

•   Source: SciCrunch Registry

http://www.molecularevolution.org/software/genomics/velvet

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Software package as de novo genomic assembler for short read sequencing technologies using de Bruijn graphs. Takes in short read sequences, removes errors, then produces high quality unique contigs, retrieves repeated areas between contigs. Can leverage very short reads in combination with read pairs to produce useful assemblies. Operating system Unix/Linux., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Velvet (RRID:SCR_010755) Copy   

•   Source: SciCrunch Registry