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http://www.scbi.uma.es/ingebiol/session/new/seqtrimnext

A customizable and distributed pre-processing software for NGS (Next Generation Sequencing) biological data.The old version for Sanger sequences, Seqtrim, has been discontinued.

Proper citation: SeqtrimNEXT (RRID:SCR_011845) Copy   

•   Source: SciCrunch Registry

http://amphoranet.pitgroup.org/

Webserver implementation of the AMPHORA2 workflow for phylogenetic analysis of metagenomic shotgun sequencing data. It is capable of assigning a probability-weighted taxonomic group for each phylogenetic marker gene found in the input metagenomic sample.

Proper citation: AmphoraNet (RRID:SCR_005009) Copy   

•   Source: SciCrunch Registry

http://mltreemap.org/

Data analysis service that analyzes DNA sequences and determines their most likely phylogenetic origin. Its main use is in metagenomics projects, where DNA is isolated directly from natural environments and sequenced (the organisms from which the DNA originates are often entirely undescribed). It will search such sequences for suitable marker genes, and will use maximum likelihood analysis to place them in the ''''Tree of Life''''. This placement is more reliable than simply assessing the closest relative of a sequence using BLAST. More importantly, MLTreeMap decides not only who is the closest relative of your query sequence, but also how deep in the tree of life it probably branched off. Additionally, MLTreeMap searches the sequences for genes, which are coding for key enzymes of important functional pathways, such as RuBisCo, methane monooxygenase or nitrogenase. In case of a positive hit, MLTreeMap uses maximum likelihood analysis to place them in the respective ''''gene-family tree''''.

Proper citation: MLTreeMap (RRID:SCR_004792) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/biosamples/

Database that aggregates sample information for reference samples (e.g. Coriell Cell lines) and samples for which data exist in one of the EBI''''s assay databases such as ArrayExpress, the European Nucleotide Archive or PRoteomics Identificates DatabasE. It provides links to assays for specific samples, and accepts direct submissions of sample information. The goals of the BioSample Database include: # recording and linking of sample information consistently within EBI databases such as ENA, ArrayExpress and PRIDE; # minimizing data entry efforts for EBI database submitters by enabling submitting sample descriptions once and referencing them later in data submissions to assay databases and # supporting cross database queries by sample characteristics. The database includes a growing set of reference samples, such as cell lines, which are repeatedly used in experiments and can be easily referenced from any database by their accession numbers. Accession numbers for the reference samples will be exchanged with a similar database at NCBI. The samples in the database can be queried by their attributes, such as sample types, disease names or sample providers. A simple tab-delimited format facilitates submissions of sample information to the database, initially via email to biosamples (at) ebi.ac.uk. Current data sources: * European Nucleotide Archive (424,811 samples) * PRIDE (17,001 samples) * ArrayExpress (1,187,884 samples) * ENCODE cell lines (119 samples) * CORIELL cell lines (27,002 samples) * Thousand Genome (2,628 samples) * HapMap (1,417 samples) * IMSR (248,660 samples)

Proper citation: BioSample Database at EBI (RRID:SCR_004856) Copy   

•   Source: SciCrunch Registry

http://www.ncbi.nlm.nih.gov/Structure/ibis/ibis.cgi

A web server and database that organizes, analyzes and predicts interactions between proteins and other biomolecules. For a given protein sequence or structure query, it reports protein-protein, protein-small molecule, protein nucleic acids and protein-ion interactions observed in experimentally-determined structural biological assemblies. It also infers/predicts interacting partners and binding sites by homology, by inspecting the protein complexes formed by close homologs of a given query. To ensure biological relevance of inferred binding sites, the IBIS algorithm clusters binding sites formed by homologs based on binding site sequence and structure conservation.

Proper citation: IBIS: Inferred Biomolecular Interactions Server (RRID:SCR_004886) Copy   

•   Source: SciCrunch Registry

http://solgenomics.net/

A clade oriented, community curated database containing genomic, genetic, phenotypic and taxonomic information for plant genomes. Genomic information is presented in a comparative format and tied to important plant model species such as Arabidopsis. SGN provides tools such as: BLAST searches, the SolCyc biochemical pathways database, a CAPS experiment designer, an intron detection tool, an advanced Alignment Analyzer, and a browser for phylogenetic trees. The SGN code and database are developed as an open source project, and is based on database schemas developed by the GMOD project and SGN-specific extensions.

Proper citation: SGN (RRID:SCR_004933) Copy   

•   Source: SciCrunch Registry

http://katahdin.mssm.edu/kismeth/revpage.pl

A web-based tool for bisulfite sequencing analysis that was designed to be used with plants, since it considers potential cytosine methylation in any sequence context (CG, CHG, and CHH). It provides a tool for the design of bisulfite primers as well as several tools for the analysis of the bisulfite sequencing results. Kismeth is not limited to data from plants, as it can be used with data from any species.

Proper citation: Kismeth (RRID:SCR_005444) Copy   

•   Source: SciCrunch Registry

http://www.gene-regulation.com/pub/databases.html#transfac

Manually curated database of eukaryotic transcription factors, their genomic binding sites and DNA binding profiles. Used to predict potential transcription factor binding sites.

Proper citation: TRANSFAC (RRID:SCR_005620) Copy   

•   Source: SciCrunch Registry

http://indel.bioinfo.sdu.edu.cn/gridsphere/gridsphere

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. Indel Flanking Region Database is an online resource for indels and the flanking regions of proteins in SCOP superfamilies, including amino acid sequences, lengths, locations, secondary structure constitutions, hydrophilicity / hydrophobicity, domain information, 3D structures and so on. It aims at providing a comprehensive dataset for analyzing the qualities of amino acid insertion/deletions(indels), substitutions and the relationship between them. The indels were obtained through the pairwise alignment of homologous structures in SCOP superfamilies. The IndelFR database contains 2,925,017 indels with flanking regions extracted from 373,402 structural alignment pairs of 12,573 non-redundant domains from 1053 superfamilies. IndelFR has already been used for molecular evolution studies and may help to promote future functional studies of indels and their flanking regions.

Proper citation: IndelFR - Indel Flanking Region Database (RRID:SCR_006050) Copy   

•   Source: SciCrunch Registry

http://webclu.bio.wzw.tum.de/profcom/

Profiling of Complex Functionality (ProfCom) is a web-based tool for the functional interpretation of a gene list that was identified to be related by experiments. A trait which makes ProfCom a unique tool is an ability to profile enrichments of not only available Gene Ontology (GO) terms but also of complex function. A complex function is constructed as Boolean combination of available GO terms. The complex functions inferred by ProfCom are more specific in comparison to single terms and describe more accurately the functional role of genes. Platform: Online tool

Proper citation: ProfCom - Profiling of complex functionality (RRID:SCR_005797) Copy   

•   Source: SciCrunch Registry

http://estbioinfo.stat.ub.es/apli/serbgov131/index.php

SerbGO is a web-based tool intended to assist researchers determine which microarray tools for gene expression analysis which make use of the GO ontologies are best suited to their projects. SerbGO is a bidirectional application. The user can ask for some features by checking on the Query Form to get the appropriate tools for their interests. The user can also compare tools to check which features are implemented in each one. Platform: Online tool

Proper citation: SerbGO (RRID:SCR_005798) Copy   

•   Source: SciCrunch Registry

http://gopubmed.org/web/gopubmed/

A web server which allows users to explore PubMed search results with the Gene Ontology, a hierarchically structured vocabulary for molecular biology. GoPubMed submits a user''''s keywords to PubMed, retrieves the abstracts, detects Gene Ontology terms in the abstracts, displays the subset of Gene Ontology relevant to the original query, and allows the user to browse through the ontology displaying associated papers and their GO annotation. Platform: Online tool

Proper citation: GoPubMed (RRID:SCR_005823) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/expressionprofiler/

THIS RESOURCE IS NO LONGER IN SERVCE, documented September 2, 2016. The EP:GO browser is built into EBI's Expression Profiler, a set of tools for clustering, analysis and visualization of gene expression and other genomic data. With it, you can search for GO terms and identify gene associations for a node, with or without associated subnodes, for the organism of your choice.

Proper citation: Expression Profiler (RRID:SCR_005821) Copy   

•   Source: SciCrunch Registry

http://nmr.cmbi.ru.nl/NRG-CING/HTML/index.html

NRG-CING presents a complete validation report for all 9,000+ wwPDB NMR entries including remediated experimental data such as chemical shifts from BMRB and restraints from NRG . These CING reports are compiled from internal analyses and those by CCPN, DSSP, PROCHECK-NMR/Aqua, ShiftX, Talos+, Vasco, Wattos, and WHAT_CHECK. The NRG-CING website is a collection of CING reports that has been pre-calculated for all PDB files solved by NMR. (See website for more information on CING.) In case the underlying experimental data is available, these have been cleaned up and made syntactically and semantically correct and homogeneous. For many macromolecular NMR ensembles from the Protein Data Bank (PDB) the experiment-based restraint lists used in the structure calculation are accessible, while other experimental data, mainly chemical shift values, are often available from the BioMagResBank. Assessment of the quality of the structural result is paramount to their usage and a combined, integrated repository of both input data and structural results greatly facilitates such an analysis. In addition, the accuracy and precision of the coordinates in these macromolecular NMR ensembles can be improved by recalculations using the available experimental data and present-day software with improved protocols and force fields. Such efforts, however, generally fail on over half of all deposited structures due to the syntactic and semantic heterogeneity of the data and the wide variety of formats used for their deposition. We have combined the cleaned-up restraints information from the NMR Restraints Grid (NRG) database with available chemical shifts from the BioMagResBank in the weekly updated NRG-CING database. Eleven programs, in addition to CING itself, have been included in the NRG-CING production pipeline to arrive at validation reports that list for each entry the potential inconsistencies between the coordinates and the available restraint and chemical shift data. The longitudinal validation of this data yielded a set of indicators that can be used to judge the quality of every macromolecular structure solved with NMR. The cleaned up NMR experimental datasets and the validation reports are freely available.

Proper citation: NRG-CING (RRID:SCR_006079) Copy   

•   Source: SciCrunch Registry

http://www.uniprot.org/uniparc/

Database that contains publicly available protein sequences with stable and unique identifiers (UPI) which are never removed, changed or reassigned. UniParc tracks sequence changes in the source databases and archives the history of all changes. Information other than protein sequence must be retrieved from the UniParc source databases using the database cross-references.

Proper citation: UniParc (RRID:SCR_005818) Copy   

•   Source: SciCrunch Registry

http://202.120.189.88/drvis/

Dr.VIS collects and locates human disease-related viral integration sites. So far, about 600 sites covering 5 virus organisms and 11 human diseases are available. Integration sites in Dr.VIS are located against chromosome, cytoband, gene and refseq position as specific as possible. Viral-cellular junction sequences are extracted from papers and nucleotide databases, and linked to corresponding integration sites Graphic views summarizing distribution of viral integration sites are generated according to chromosome maps. Dr.VIS is built with a hope to facilitate research of human diseases and viruses. Dr.VIS provides curated knowledge of integration sites from chromosome region narrow to genomic position, as well as junction sequences if available. Dr.VIS is an open resource for free.

Proper citation: Dr.VIS - Human Disease-Related Viral Integration Sites (RRID:SCR_005965) Copy   

•   Source: SciCrunch Registry

http://www.ebi.ac.uk/thornton-srv/databases/FunTree/

FunTree provides a range of data resources to detect the evolution of enzyme function within distant structurally related clusters within domain super families as determined by CATH. To access the resource enter a specific CATH superfamily code or search for a structure / sequence / function (either via a EC code or KEGG ligand / reaction ID, PDB ID or UniProtKB ID). Or browse the resource via superfamily / function / structure / metabolites & reactions via the menu on the left panel. FunTree is a new resource that brings together sequence, structure, phylogenetic, chemical and mechanistic information for structurally defined enzyme superfamilies. Gathering together this range of data into a single resource allows the investigation of how novel enzyme functions have evolved within a structurally defined superfamily as well as providing a means to analyse trends across many superfamilies. This is done not only within the context of an enzyme''''s sequence and structure but also the relationships of their reactions. Developed in tandem with the CATH database, it currently comprises 276 superfamilies covering 1800 (70%) of sequence assigned enzyme reactions. Central to the resource are phylogenetic trees generated from structurally informed multiple sequence alignments using both domain structural alignments supplemented with domain sequences and whole sequence alignments based on commonality of multi-domain architectures. These trees are decorated with functional annotations such as metabolite similarity as well as annotations from manually curated resources such the catalytic site atlas and MACiE for enzyme mechanisms.

Proper citation: FunTree (RRID:SCR_006014) Copy   

•   Source: SciCrunch Registry

http://www.hpppi.iicb.res.in/btox/

Database of Bacterial ExoToxins for Human is a database of sequences, structures, interaction networks and analytical results for 229 exotoxins, from 26 different human pathogenic bacterial genus. All toxins are classified into 24 different Toxin classes. The aim of DBETH is to provide a comprehensive database for human pathogenic bacterial exotoxins. DBETH also provides a platform to its users to identify potential exotoxin like sequences through Homology based as well as Non-homology based methods. In homology based approach the users can identify potential exotoxin like sequences either running BLASTp against the toxin sequences or by running HMMER against toxin domains identified by DBETH from human pathogenic bacterial exotoxins. In Non-homology based part DBETH uses a machine learning approach to identify potential exotoxins (Toxin Prediction by Support Vector Machine based approach).

Proper citation: DBETH - Database for Bacterial ExoToxins for Humans (RRID:SCR_005908) Copy   

•   Source: SciCrunch Registry

http://mint.bio.uniroma2.it/virusmint/

A virus protein interactions database that collects and annotates all the interactions between human and viral proteins and integrates this information in the human protein interaction network. It uses the PSI-MI standard and is fully integrated with the MINT database. You can search for any viral or human protein by entering either common names or database identifiers or display a complete viral interactome.

Proper citation: VirusMINT (RRID:SCR_005987) Copy   

•   Source: SciCrunch Registry

http://omicslab.genetics.ac.cn/GOEAST/

Gene Ontology Enrichment Analysis Software Toolkit (GOEAST) is a web based software toolkit providing easy to use, visualizable, comprehensive and unbiased Gene Ontology (GO) analysis for high-throughput experimental results, especially for results from microarray hybridization experiments. The main function of GOEAST is to identify significantly enriched GO terms among give lists of genes using accurate statistical methods. Compared with available GO analysis tools, GOEAST has the following unique features: * GOEAST supports analysis for data from various resources, such as expression data obtained using Affymetrix, illumina, Agilent or customized microarray platforms. GOEAST also supports non-microarray based experimental data. The web-based feature makes GOEAST very user friendly; users only have to provide a list of genes in correct formats. * GOEAST provides visualizable analysis results, by generating graphs exhibiting enriched GO terms as well as their relationships in the whole GO hierarchy. * Note that GOEAST generates separate graph for each of the three GO categories, namely biological process, molecular function and cellular component. * GOEAST allows comparison of results from multiple experiments (see Multi-GOEAST tool). The displayed color of each GO term node in graphs generated by Multi-GOEAST is the combination of different colors used in individual GOEAST analysis. Platform: Online tool

Proper citation: GOEAST - Gene Ontology Enrichment Analysis Software Toolkit (RRID:SCR_006580) Copy   

•   Source: SciCrunch Registry