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http://orphelia.gobics.de/

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on August 23,2022. A metagenomic open reading frame (ORF) finding tool for the prediction of protein coding genes in short, environmental DNA sequences with unknown phylogenetic origin. The resource is based on a two-stage machine learning approach that uses linear discriminants to extract features from the ORFs. An artificial neural network then combines the features and computes a gene probability for each ORF fragment.

Proper citation: Orphelia (RRID:SCR_000119) Copy   

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http://www.gobics.de/fabian/treephyler.php

A software tool for fast taxonomic profiling of metagenomes.

Proper citation: Treephyler (RRID:SCR_000109) Copy   

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http://www.bioextract.org/GuestLogin

An open, web-based system designed to aid researchers in the analysis of genomic data by providing a platform for the creation of bioinformatic workflows. Scientific workflows are created within the system by recording tasks performed by the user. These tasks may include querying multiple, distributed data sources, saving query results as searchable data extracts, and executing local and web-accessible analytic tools. The series of recorded tasks can then be saved as a reproducible, sharable workflow available for subsequent execution with the original or modified inputs and parameter settings. Integrated data resources include interfaces to the National Center for Biotechnology Information (NCBI) nucleotide and protein databases, the European Molecular Biology Laboratory (EMBL-Bank) non-redundant nucleotide database, the Universal Protein Resource (UniProt), and the UniProt Reference Clusters (UniRef) database. The system offers access to numerous preinstalled, curated analytic tools and also provides researchers with the option of selecting computational tools from a large list of web services including the European Molecular Biology Open Software Suite (EMBOSS), BioMoby, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The system further allows users to integrate local command line tools residing on their own computers through a client-side Java applet.

Proper citation: BioExtract (RRID:SCR_005397) Copy   

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http://llama.mshri.on.ca/synergizer/translate/

The Synergizer database is a growing repository of gene and protein identifier synonym relationships. This tool facilitates the conversion of identifiers from one naming scheme (a.k.a namespace) to another. The Synergizer is a service for translating between sets of biological identifiers. It can, for example, translate Ensembl Gene IDs to Entrez Gene IDs, or IPI IDs to HGNC gene symbols, and much more. Unlike some other tools for this purpose, The Synergizer is simple and easy to learn. The Synergizer works via a web interface (for users who are not programmers) or through a web service (for programmatic access).

Proper citation: Synergizer (RRID:SCR_005308) Copy   

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http://users-birc.au.dk/biopv/php/fabox/

Tools for splitting, joining and otherwise manipulating FASTA format sequence files. The first tools in the toolbox is for manipulating fasta headers, cropping alignments and doing some sequence comparison allowing users to combine the description of data (often in excel spreadsheets) with the actual data (often DNA sequences). Also, producing correct input files for a range of programs seems to be problematic for the average user. Hence, some converters in some of the services have been included as well as some stand-alone converters. The converters are not necessarily meant to provide the final input file, but you''ll get a valid input file for Arlequin, MrBayes etc. - that you may further edit so it suit your needs. This means that you may need to combine several of the tools to finish your handling - but it keeps it relatively simple to use. Please note that FaBox is written in PHP and ONLY RUNS ON A WEBSERVER.

Proper citation: FaBox (RRID:SCR_005350) Copy   

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http://www.jcvi.org/cgi-bin/tigrfams/index.cgi

Consists curated multiple sequence alignments, Hidden Markov Models (HMMs) for protein sequence classification, and associated information designed to support automated annotation of (mostly prokaryotic) proteins. Starting with release 10.0, TIGRFAMs models use HMMER3, which provides excellent search speed as well as exquisite search sensitivity. See the "TIGRFAMs Complete Listing" page to review the accession, protein name, model type, and EC number (if assigned) of all models. TIGRFAMs is a member database in InterPro. The HMM libraries and supporting files are available to download and use for free from our FTP site.

Proper citation: TIGRFAMS (RRID:SCR_005493) Copy   

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http://www.ncbi.nlm.nih.gov/gtr/

Central location for voluntary submission of genetic test information by providers including the test''s purpose, methodology, validity, evidence of the test''s usefulness, and laboratory contacts and credentials. GTR aims to advance the public health and research into the genetic basis of health and disease. GTR is accepting registration of clinical tests for Mendelian disorders, complex tests and arrays, and pharmacogenetic tests. These tests may include multiple methods and may include multiple major method categories such as biochemical, cytogenetic, and molecular tests. GTR is not currently accepting registration of tests for somatic disorders, research tests or direct-to-consumer tests.

Proper citation: Genetic Testing Registry (RRID:SCR_005565) Copy   

•   Source: SciCrunch Registry

http://llama.mshri.on.ca/

The Roth Laboratory is designing and interpreting large-scale experiments to understand pathway structure and its relationship to phenotype and human disease. Software for research focused on a specific research goal is available. Current experimental interests: * Exploiting parallel sequencing technology to phenotype all pairwise gene deletion combinations in S. cerevisiae, with initial application to genes involved in transcription. * Generation of S. cerevisiae strains carrying dozens of chosen targeted deletions, with initial application to delete all ABC transporters imparting multidrug resistance. * Targeted insertion of gene sets encoding entire human pathways into S. cerevisiae, with initial application to genes involved in drug metabolism. Current computational interests: * Systematic analysis of genetic interaction to reveal redundant systems and order of action in genetic pathways * Integrating large-scale studies - including phenotype, genetic epistasis, protein-protein and transcription-regulatory interactions and sequence patterns - to quantitatively assign function to genes and guide experimentation and disease association studies. * Alternative splicing and its relationship to protein interaction networks.

Proper citation: Roth Laboratory (RRID:SCR_005711) Copy   

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http://genemania.org/

Data analysis service to predict the function of your favorite genes and gene sets. Indexing 1,421 association networks containing 266,984,699 interactions mapped to 155,238 genes from 7 organisms. GeneMANIA interaction networks are available for download in plain text format. GeneMANIA finds other genes that are related to a set of input genes, using a very large set of functional association data. Association data include protein and genetic interactions, pathways, co-expression, co-localization and protein domain similarity. You can use GeneMANIA to find new members of a pathway or complex, find additional genes you may have missed in your screen or find new genes with a specific function, such as protein kinases. Your question is defined by the set of genes you input. If members of your gene list make up a protein complex, GeneMANIA will return more potential members of the protein complex. If you enter a gene list, GeneMANIA will return connections between your genes, within the selected datasets. GeneMANIA suggests annotations for genes based on Gene Ontology term enrichment of highly interacting genes with the gene of interest. GeneMANIA is also a gene recommendation system. GeneMANIA is also accessible via a Cytoscape plugin, designed for power users. Platform: Online tool, Windows compatible, Mac OS X compatible, Linux compatible, Unix compatible

Proper citation: GeneMANIA (RRID:SCR_005709) Copy   

•   Source: SciCrunch Registry

http://www.molgen.de

The research of the group concentrates on the molecular biology of Gram-positive bacteria, with Bacillus subtilis and Lactococcus lactis as the main model organisms. A number of important (human) pathogens are also investigated: Bacillus cereus, Streptococcus pneumoniae and Enterococcus faecalis. The nature of the research is both fundamental and application-oriented. Transcript- and protein profiling by high-throughput technologies such as DNA microarrays and proteomics tools are being used. The very large data sets generated are analyzed by employing existing and novel bioinformatics tools. Major lines of research are in the field of functional genomics of these organisms, using systems- and synthetic biology approaches.

Proper citation: MolGen (RRID:SCR_005700) Copy   

•   Source: SciCrunch Registry

http://agbase.msstate.edu/cgi-bin/tools/goprofiler_select.pl

Service that provides a summary of GO annotations available for each species. The user provides a taxon id and GOProfiler displays the number of GO associations and the number of annotated proteins for that species. The results are listed by evidence code and a separate list of unannotated proteins is also provided.

Proper citation: GOProfiler (RRID:SCR_005683) Copy   

•   Source: SciCrunch Registry

http://www.pandora.cs.huji.ac.il/

With PANDORA, you can search for any non-uniform sets of proteins and detect subsets of proteins that share unique biological properties and the intersections of such sets. PANDORA supports GO annotations as well as additional keywords (from UniProt Knowledgebase, InterPro, ENZYME, SCOP etc). It is also integrated into the ProtoNet system, thus allowing testing of thousands of automatically generated protein families. Note that PANDORA replaces the ProtoGO browser developed by the same group. Platform: Online tool

Proper citation: Pandora - Protein ANnotation Diagram ORiented Analysis (RRID:SCR_005686) Copy   

•   Source: SciCrunch Registry

http://mcbc.usm.edu/gofetcher/

THIS RESOURCE IS NO LONGER IN SERVICE, documented on June 29, 2012. We developed a web application, GOfetcher, with a very comprehensive search facility for the GO project and a variety of output formats for the results. GOfetcher has three different levels for searching the GO: Quick Search, Advanced Search, and Upload Files for searching. The application includes a unique search option which generates gene information given a nucleotide or protein accession number which can then be used in generating gene ontology information. The output data in GOfetcher can be saved into several different formats; including spreadsheet, comma-separated values, and the Extensible Markup Language (XML) format. Platform: Online tool

Proper citation: GOfetcher (RRID:SCR_005681) Copy   

•   Source: SciCrunch Registry

http://www.compbio.dundee.ac.uk/downloads/oxbench/

A suite of programs aimed at developers of alignment methods rather than end-users to assess the accuracy of multiple sequence alignment methods. It includes a reference database of protein multiple sequence alignments that were generated by consideration of protein three-dimensional structure.

Proper citation: OXBench (RRID:SCR_005591) Copy   

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http://www.garban.org/garban/home.php

THIS RESOURCE IS NO LONGER IN SERVICE, documented on July 12, 2012. GARBAN is a tool for analysis and rapid functional annotation of data arising from cDNA microarrays and proteomics techniques. GARBAN has been implemented with bioinformatic tools to rapidly compare, classify, and graphically represent multiple sets of data (genes/ESTs, or proteins), with the specific aim of facilitating the identification of molecular markers in pathological and pharmacological studies. GARBAN has links to the major genomic and proteomic databases (Ensembl, GeneBank, UniProt Knowledgebase, InterPro, etc.), and follows the criteria of the Gene Ontology Consortium (GO) for ontological classifications. Source may be shared: e-mail garban (at) ceit.es. Platform: Online tool

Proper citation: GARBAN (RRID:SCR_005778) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/Tools/pfa/iprscan/

Software package for functional analysis of sequences by classifying them into families and predicting presence of domains and sites. Scans sequences against InterPro's signatures. Characterizes nucleotide or protein function by matching it with models from several different databases. Used in large scale analysis of whole proteomes, genomes and metagenomes. Available as Web based version and standalone Perl version and SOAP Web Service.

Proper citation: InterProScan (RRID:SCR_005829) Copy   

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http://www.ebi.ac.uk/webservices/whatizit/info.jsf

A text processing system that allows you to do textmining tasks on text. It is great at identifying molecular biology terms and linking them to publicly available databases. Whatizit is also a Medline abstracts retrieval/search engine. Instead of providing the text by Copy&Paste, you can launch a Medline search. The abstracts that match your search criteria are retrieved and processed by a pipeline of your choice. Whatizit is also available as 1) a webservice and as 2) a streamed servlet. The webservice allows you to enrich content within your website in a similar way as in the wikipedia. The streamed servlet allows you to process large amounts of text.

Proper citation: Whatizit (RRID:SCR_005824) Copy   

•   Source: SciCrunch Registry

http://crdd.osdd.net/raghava/ccpdb/

ccPDB (Compilation and Creation of datasets from PDB) is designed to provide service to scientific community working in the field of function or structure annoation of proteins. This database of datasets is based on Protein Data Bank (PDB), where all datasets were derived from PDB. ccPDB have four modules; i) compilation of datasets, ii) creation of datasets, iii) web services and iv) Important links. * Compilation of Datasets: Datasets at ccPDB can be classified in two categories, i) datasets collected from literature and ii) datasets compiled from PDB. We are in process of collecting PDB datasetsfrom literature and maintaining at ccPDB. We are also requesting community to suggest datasets. In addition, we generate datasets from PDB, these datasets were generated using commonly used standard protocols like non-redundant chains, structures solved at high resolution. * Creation of datasets: This module developed for creating customized datasets where user can create a dataset using his/her conditions from PDB. This module will be useful for those users who wish to create a new dataset as per ones requirement. This module have six steps, which are described in help page. * Web Services: We integrated following web services in ccPDB; i) Analyze of PDB ID service allows user to submit their PDB on around 40 servers from single point, ii) BLAST search allows user to perform BLAST search of their protein against PDB, iii) Structural information service is designed for annotating a protein structure from PDB ID, iv) Search in PDB facilitate user in searching structures in PDB, v)Generate patterns service facility to generate different types of patterns required for machine learning techniques and vi) Download useful information allows user to download various types of information for a given set of proteins (PDB IDs). * Important Links: One of major objectives of this web site is to provide links to web servers related to functional annotation of proteins. In first phase we have collected and compiled these links in different categories. In future attempt will be made to collect as many links as possible.

Proper citation: ccPDB - Compilation and Creation of datasets from PDB (RRID:SCR_005870) Copy   

•   Source: SciCrunch Registry

http://www.vectorbase.org

Bioinformatics Resource Center for invertebrate vectors. Provides web-based resources to scientific community conducting basic and applied research on organisms considered potential agents of biowarfare or bioterrorism or causing emerging or re-emerging diseases.

Proper citation: VectorBase (RRID:SCR_005917) Copy   

•   Source: SciCrunch Registry

http://newt-omics.mpi-bn.mpg.de/index.php

Newt-omics is a database, which enables researchers to locate, retrieve and store data sets dedicated to the molecular characterization of newts. Newt-omics is a transcript-centered database, based on an Expressed Sequence Tag (EST) data set from the newt, covering ~50,000 Sanger sequenced transcripts and a set of high-density microarray data, generated from regenerating hearts. Newt-omics also contains a large set of peptides identified by mass spectrometry, which was used to validate 13,810 ESTs as true protein coding. Newt-omics is open to implement additional high-throughput data sets without changing the database structure. Via a user-friendly interface Newt-omics allows access to a huge set of molecular data without the need for prior bioinformatical expertise. The newt Notopthalmus viridescens is the master of regeneration. This organism is known for more than 200 years for its exceptional regenerative capabilities. Newts can completely replace lost appendages like limb and tail, lens and retina and parts of the central nervous system. Moreover, after cardiac injury newts can rebuild the functional myocardium with no scar formation. To date only very limited information from public databases is available. Newt-Omics aims to provide a comprehensive platform of expressed genes during tissue regeneration, including extensive annotations, expression data and experimentally verified peptide sequences with yet no homology to other publicly available gene sequences. The goal is to obtain a detailed understanding of the molecular processes underlying tissue regeneration in the newt, that may lead to the development of approaches, efficiently stimulating regenerative pathways in mammalians. * Number of contigs: 26594 * Number of est in contigs: 48537 * Number of transcripts with verified peptide: 5291 * Number of peptides: 15169

Proper citation: Newtomics (RRID:SCR_006073) Copy   

•   Source: SciCrunch Registry