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http://disulfind.dsi.unifi.it/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023, Software for predicting the disulfide bonding state of cysteines and their disulfide connectivity, starting from a protein sequence alone and may be useful in other genomic annotation tasks.

Proper citation: DISULFIND (RRID:SCR_016072) Copy   

•   Source: SciCrunch Registry

https://www.ebi.ac.uk/about/vertebrate-genomics/software/exonerate

Software package for sequence alignment of pairwise sequence comparison. Exonerate can be used to align sequences using many alignment models, exhaustive dynamic programming, or a variety of heuristics., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Exonerate (RRID:SCR_016088) Copy   

•   Source: SciCrunch Registry

http://biopp.univ-montp2.fr/wiki/index.php/Main_Page

Software providing a set of ready-to-use C++ libraries as re-usable tools to visualize, edit, print and output data for bioinformatics. It uses sequence analysis, phylogenetics, molecular evolution and population genetics to help to write programs., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: Bio++ (RRID:SCR_016055) Copy   

•   Source: SciCrunch Registry

http://baderlab.org/Software/EnrichmentMap

Source code of a Cytoscape plugin for functional enrichment visualization. It organizes gene-sets, such as pathways and Gene Ontology terms, into a network to reveal which mutually overlapping gene-sets cluster together.

Proper citation: EnrichmentMap (RRID:SCR_016052) Copy   

•   Source: SciCrunch Registry

https://github.com/Oshlack/necklace/wiki

Software that combines reference and assembled transcriptomes for RNA-Seq analysis. It replaces many manual steps in the pipeline of RNA-Seq analyses involving species with incomplete genome or annotations.

Proper citation: Necklace (RRID:SCR_016103) Copy   

•   Source: SciCrunch Registry

http://zzlab.net/blink/index.html

Software for next level of genome wide association studies with both individuals and markers in millions. The method releases the requirement that causative genes are evenly distributed on genome and consequently boosts statistical power.

Proper citation: BLINK (RRID:SCR_016288) Copy   

•   Source: SciCrunch Registry

https://github.com/sblanck/smagexp

Software toolkit for transcriptomics data meta-analysis. It integrates metaMA and metaRNAseq packages into Galaxy, carries out meta-analysis of gene expression data, handles microarray data from Gene Expression Omnibus (GEO) database, and more.

Proper citation: SMAGEXP (RRID:SCR_016360) Copy   

•   Source: SciCrunch Registry

https://omictools.com/fluxmodecalculator-tool

Software for performing flux mode analysis in stoichiometric models. FluxModeCalculator enables large-scale elementary flux mode (EFM) computation and uses the OpenMP API to optimally exploit processor architectures with multiple cores., THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: FluxModeCalculator (RRID:SCR_016290) Copy   

•   Source: SciCrunch Registry

https://lifebit.ai/

Platform for computing management for data analysis on the cloud from the Lifebit company. Allows the computational analyses to be permanently linked to live analyses pipelines.

Proper citation: Lifebit Deploit (RRID:SCR_016428) Copy   

•   Source: SciCrunch Registry

http://mib.helsinki.fi/

Software package for advanced image processing, analysis, segmentation and visualization of multi-dimensional (2D-4D) light and electron microscopy datasets.

Proper citation: Microscopy Image Browser (RRID:SCR_016560) Copy   

•   Source: SciCrunch Registry

http://bioconductor.org/packages/release/bioc/html/MetaCyto.html

Software tool for automated meta-analysis of mass and flow cytometry data. Provides functions for preprocessing, automated gating and meta-analysis of cytometry data and collection of cytometry data from the ImmPort database.

Proper citation: MetaCyto (RRID:SCR_016415) Copy   

•   Source: SciCrunch Registry

https://www.genome.jp/tools/dinies/

Web server for predicting unknown drug-target interaction networks from various types of biological data in the framework of supervised network inference.

Proper citation: DINIES (RRID:SCR_016505) Copy   

•   Source: SciCrunch Registry

https://github.com/WGS-TB/MentaLiST

Software for a MLST (multi-locus sequence typing) caller, based on a k-mer counting algorithm and written in the Julia language. Designed and implemented to handle large typing schemes.

Proper citation: MentaLiST (RRID:SCR_016469) Copy   

•   Source: SciCrunch Registry

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

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

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

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://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