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A platform composed of three modules: the Database, the Search Engine, and rSNPs, for the computational identification of transcription factor binding sites (TFBSs) in multiple genomes, that combines TRANSFAC and JASPAR data with the search power of profile hidden Markov models (HMMs). The Database contains putative TFBSs found in the upstream sequences of genes from the human, mouse and D.melanogaster genomes. For each gene, they scanned the region from 10,000 base pairs upstream of the transcript start to 50 base pairs downstream of the coding sequence start against all their models. Therefore, the database contains putative binding sites in the gene promoter and in the initial introns and non-coding exons. Information displayed for each putative binding site includes the transcription factor name, its position (absolute on the chromosome, or relative to the gene), the score of the prediction, and the region of the gene the site belongs to. If the selected gene has homologs in any of the other two organisms, the program optionally displays the putative TFBSs in the homologs. The Search Engine allows the identification, visualization and selection of putative TFBSs occurring in the promoter or other regions of a gene from the human, mouse, D.melanogaster, C.elegans or S.cerevisiae genomes. In addition, it allows the user to upload a sequence to query and to build a model by supplying a multiple sequence alignment of binding sites for a transcription factor of interest. rSNPs MAPPER is designed to identify Single Nucleotide Polymorphisms (SNPs) that may have an effect on the presence of one or more TFBSs.
Proper citation: MAPPER - Multi-genome Analysis of Positions and Patterns of Elements of Regulation (RRID:SCR_003077) Copy
http://www.ncbi.nlm.nih.gov/homologene
Automated system for constructing putative homology groups from complete gene sets of wide range of eukaryotic species. Databse that provides system for automatic detection of homologs, including paralogs and orthologs, among annotated genes of sequenced eukaryotic genomes. HomoloGene processing uses proteins from input organisms to compare and sequence homologs, mapping back to corresponding DNA sequences. Reports include homology and phenotype information drawn from Online Mendelian Inheritance in Man, Mouse Genome Informatics, Zebrafish Information Network, Saccharomyces Genome Database and FlyBase.
Proper citation: HomoloGene (RRID:SCR_002924) Copy
http://www.biocomputing.it/fidea/
A web server for the functional interpretation of differential expression analysis. It can: * Calculate overrepresentation statistics using KEGG, Interpro, Gene Ontology Molecular Function, Gene Ontology Biological Process, Gene Ontology Cellular Component and GoSlim classifications; * Analyze down-regulated and up-regulated DE genes separately or together as a single set; * Provide interactive graphs and tables that can be modified on the fly according to user defined parameters; the user can set a fold change filter and interactively see the effects on the gene set under examination; * Output publication-ready plot of the graph; * Compare the results of several experiments in any combination.
Proper citation: FIDEA (RRID:SCR_004187) Copy
https://netbio.bgu.ac.il/labwebsite/software/responsenet/
WebServer that identifies high-probability signaling and regulatory paths that connect input data sets. The input includes two weighted lists of condition-related proteins and genes, such as a set of disease-associated proteins and a set of differentially expressed disease genes, and a molecular interaction network (i.e., interactome). The output is a sparse, high-probability interactome sub-network connecting the two sets that is biased toward signaling pathways. This sub-network exposes additional proteins that are potentially involved in the studied condition and their likely modes of action. Computationally, it is formulated as a minimum-cost flow optimization problem that is solved using linear programming.
Proper citation: ResponseNet (RRID:SCR_003176) Copy
http://gpcr.biocomp.unibo.it/bacello/
A predictor for the subcellular localization of proteins in eukaryotes that is based on a decision tree of several support vector machines (SVMs). It classifies up to four localizations for Fungi and Metazoan proteins and five localizations for Plant ones. BaCelLo's predictions are balanced among different classes and all the localizations are considered as equiprobable.
Proper citation: BaCelLo (RRID:SCR_011965) Copy
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Welcome to the Neuroscience Information Framework Project, designed to serve the biomedical research community. NIF maintains the largest searchable collection of neuroscience data, the largest catalog of biomedical resources, and the largest ontology for neuroscience on the web. We welcome all feedback and suggestions and are actively looking for resource providers to make their resources accessible through NIF. Learn about the tools available to help you share your data and discover a dynamic inventory of Web-based neuroscience resources.
