VMD: a community annotation database for oomycetes and microbial genomes

The VBI Microbial Database (VMD) is a database system designed to host a range of microbial genome sequences. At present, the database contains genome sequence and annotation data of two plant pathogens Phytophthora sojae and Phytophthora ramorum. With the completion of the draft genome sequences of these pathogens in collaboration with the DOE Joint Genome Institute (JGI), we have created this resource to make the sequences publicly available. The genome sequences (95 MB for P.sojae and 65 MB for P.ramorum) were annotated with ∼19 000 and ∼16 000 gene models, respectively. We used two different statistical methods to validate these gene models, Fickett's and a log-likelihood method. Functional annotation of the gene models is based on results from BlastX and InterProScan screens. From the InterProScan results, we could assign putative functions to 17 694 genes in P.sojae and 14 700 genes in P.ramorum. We created an easy-to-use genome browser to view the genome sequence data, which opens to detailed annotation pages for each gene model. A community annotation interface is available for registered community members to add or edit annotations. There are ∼ 1600 gene models for P.sojae and ∼700 models for P.ramorum that have already been manually curated. A toolkit is provided as an additional resource for users to perform a variety of sequence analysis jobs. The database is publicly available at

InterProScan: protein domains identifier

InterProScan [E. M. Zdobnov and R. Apweiler (2001) Bioinformatics, 17, 847–848] is a tool that combines different protein signature recognition methods from the InterPro [N. J. Mulder, R. Apweiler, T. K. Attwood, A. Bairoch, A. Bateman, D. Binns, P. Bradley, P. Bork, P. Bucher, L. Cerutti et al. (2005) Nucleic Acids Res., 33, D201–D205] consortium member databases into one resource. At the time of writing there are 10 distinct publicly available databases in the application. Protein as well as DNA sequences can be analysed. A web-based version is accessible for academic and commercial organizations from the EBI (). In addition, a standalone Perl version and a SOAP Web Service [J. Snell, D. Tidwell and P. Kulchenko (2001) Programming Web Services with SOAP, 1st edn. O'Reilly Publishers, Sebastopol, CA, ] are also available to the users. Various output formats are supported and include text tables, XML documents, as well as various graphs to help interpret the results

Specialized Hidden Markov Model Databases for Microbial Genomics

As hidden Markov models (HMMs) become increasingly more important in the analysis of biological sequences, so too have databases of HMMs expanded in size, number and importance. While the standard paradigm a short while ago was the analysis of one or a few sequences at a time, it has now become standard procedure to submit an entire microbial genome. In the future, it will be common to submit large groups of completed genomes to run simultaneously against a dozen public databases and any number of internally developed targets. This paper looks at some of the readily available HMM (or HMM-like) algorithms and several publicly available HMM databases, and outlines methods by which the reader may develop custom HMM targets

The PAM domain, a multi-protein complex-associated module with an all-alpha-helix fold

BACKGROUND: Multimeric protein complexes have a role in many cellular pathways and are highly interconnected with various other proteins. The characterization of their domain composition and organization provides useful information on the specific role of each region of their sequence. RESULTS: We identified a new module, the PAM domain (PCI/PINT associated module), present in single subunits of well characterized multiprotein complexes, like the regulatory lid of the 26S proteasome, the COP-9 signalosome and the Sac3-Thp1 complex. This module is an around 200 residue long domain with a predicted TPR-like all-alpha-helical fold. CONCLUSIONS: The occurrence of the PAM domain in specific subunits of multimeric protein complexes, together with the role of other all-alpha-helical folds in protein-protein interactions, suggest a function for this domain in mediating transient binding to diverse target proteins

PCAS – a precomputed proteome annotation database resource

BACKGROUND: Many model proteomes or "complete" sets of proteins of given organisms are now publicly available. Much effort has been invested in computational annotation of those "draft" proteomes. Motif or domain based algorithms play a pivotal role in functional classification of proteins. Employing most available computational algorithms, mainly motif or domain recognition algorithms, we set up to develop an online proteome annotation system with integrated proteome annotation data to complement existing resources. RESULTS: We report here the development of PCAS (ProteinCentric Annotation System) as an online resource of pre-computed proteome annotation data. We applied most available motif or domain databases and their analysis methods, including hmmpfam search of HMMs in Pfam, SMART and TIGRFAM, RPS-PSIBLAST search of PSSMs in CDD, pfscan of PROSITE patterns and profiles, as well as PSI-BLAST search of SUPERFAMILY PSSMs. In addition, signal peptide and TM are predicted using SignalP and TMHMM respectively. We mapped SUPERFAMILY and COGs to InterPro, so the motif or domain databases are integrated through InterPro. PCAS displays table summaries of pre-computed data and a graphical presentation of motifs or domains relative to the protein. As of now, PCAS contains human IPI, mouse IPI, and rat IPI, A. thaliana, C. elegans, D. melanogaster, S. cerevisiae, and S. pombe proteome. PCAS is available at CONCLUSION: PCAS gives better annotation coverage for model proteomes by employing a wider collection of available algorithms. Besides presenting the most confident annotation data, PCAS also allows customized query so users can inspect statistically less significant boundary information as well. Therefore, besides providing general annotation information, PCAS could be used as a discovery platform. We plan to update PCAS twice a year. We will upgrade PCAS when new proteome annotation algorithms identified

Minimotif miner 2nd release: a database and web system for motif search

Minimotif Miner (MnM) consists of a minimotif database and a web-based application that enables prediction of motif-based functions in user-supplied protein queries. We have revised MnM by expanding the database more than 10-fold to approximately 5000 motifs and standardized the motif function definitions. The web-application user interface has been redeveloped with new features including improved navigation, screencast-driven help, support for alias names and expanded SNP analysis. A sample analysis of prion shows how MnM 2 can be used. Weblink: http://mnm.engr.uconn.edu, weblink for version 1 is http://sms.engr.uconn.edu

The Universal Protein Resource (UniProt)

The Universal Protein Resource (UniProt) provides the scientific community with a single, centralized, authoritative resource for protein sequences and functional information. Formed by uniting the Swiss-Prot, TrEMBL and PIR protein database activities, the UniProt consortium produces three layers of protein sequence databases: the UniProt Archive (UniParc), the UniProt Knowledgebase (UniProt) and the UniProt Reference (UniRef) databases. The UniProt Knowledgebase is a comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase with extensive cross-references. This centrepiece consists of two sections: UniProt/Swiss-Prot, with fully, manually curated entries; and UniProt/TrEMBL, enriched with automated classification and annotation. During 2004, tens of thousands of Knowledgebase records got manually annotated or updated; we introduced a new comment line topic: TOXIC DOSE to store information on the acute toxicity of a toxin; the UniProt keyword list got augmented by additional keywords; we improved the documentation of the keywords and are continuously overhauling and standardizing the annotation of post-translational modifications. Furthermore, we introduced a new documentation file of the strains and their synonyms. Many new database cross-references were introduced and we started to make use of Digital Object Identifiers. We also achieved in collaboration with the Macromolecular Structure Database group at EBI an improved integration with structural databases by residue level mapping of sequences from the Protein Data Bank entries onto corresponding UniProt entries. For convenient sequence searches we provide the UniRef non-redundant sequence databases. The comprehensive UniParc database stores the complete body of publicly available protein sequence data. The UniProt databases can be accessed online (http://www.uniprot.org) or downloaded in several formats (ftp://ftp.uniprot.org/pub). New releases are published every two weeks

HMM Logos for visualization of protein families

BACKGROUND: Profile Hidden Markov Models (pHMMs) are a widely used tool for protein family research. Up to now, however, there exists no method to visualize all of their central aspects graphically in an intuitively understandable way. RESULTS: We present a visualization method that incorporates both emission and transition probabilities of the pHMM, thus extending sequence logos introduced by Schneider and Stephens. For each emitting state of the pHMM, we display a stack of letters. The stack height is determined by the deviation of the position's letter emission frequencies from the background frequencies. The stack width visualizes both the probability of reaching the state (the hitting probability) and the expected number of letters the state emits during a pass through the model (the state's expected contribution). A web interface offering online creation of HMM Logos and the corresponding source code can be found at the Logos web server of the Max Planck Institute for Molecular Genetics . CONCLUSIONS: We demonstrate that HMM Logos can be a useful tool for the biologist: We use them to highlight differences between two homologous subfamilies of GTPases, Rab and Ras, and we show that they are able to indicate structural elements of Ras

PhenoFam-gene set enrichment analysis through protein structural information

<p>Abstract</p> <p>Background</p> <p>With the current technological advances in high-throughput biology, the necessity to develop tools that help to analyse the massive amount of data being generated is evident. A powerful method of inspecting large-scale data sets is gene set enrichment analysis (GSEA) and investigation of protein structural features can guide determining the function of individual genes. However, a convenient tool that combines these two features to aid in high-throughput data analysis has not been developed yet. In order to fill this niche, we developed the user-friendly, web-based application, PhenoFam.</p> <p>Results</p> <p>PhenoFam performs gene set enrichment analysis by employing structural and functional information on families of protein domains as annotation terms. Our tool is designed to analyse complete sets of results from quantitative high-throughput studies (gene expression microarrays, functional RNAi screens, <it>etc</it>.) without prior pre-filtering or hits-selection steps. PhenoFam utilizes Ensembl databases to link a list of user-provided identifiers with protein features from the InterPro database, and assesses whether results associated with individual domains differ significantly from the overall population. To demonstrate the utility of PhenoFam we analysed a genome-wide RNA interference screen and discovered a novel function of plexins containing the cytoplasmic RasGAP domain. Furthermore, a PhenoFam analysis of breast cancer gene expression profiles revealed a link between breast carcinoma and altered expression of PX domain containing proteins.</p> <p>Conclusions</p> <p>PhenoFam provides a user-friendly, easily accessible web interface to perform GSEA based on high-throughput data sets and structural-functional protein information, and therefore aids in functional annotation of genes.</p

THoR: a tool for domain discovery and curation of multiple alignments

We describe a tool, THoR, that automatically creates and curates multiple sequence alignments representing protein domains. This exploits both PSI-BLAST and HMMER algorithms and provides an accurate and comprehensive alignment for any domain family. The entire process is designed for use via a web-browser, with simple links and cross-references to relevant information, to assist the assessment of biological significance. THoR has been benchmarked for accuracy using the SMART and pufferfish genome databases