GFam: a platform for automatic annotation of gene families

We have developed GFam, a platform for automatic annotation of gene/protein families. GFam provides a framework for genome initiatives and model organism resources to build domain-based families, derive meaningful functional labels and offers a seamless approach to propagate functional annotation across periodic genome updates. GFam is a hybrid approach that uses a greedy algorithm to chain component domains from InterPro annotation provided by its 12 member resources followed by a sequence-based connected component analysis of un-annotated sequence regions to derive consensus domain architecture for each sequence and subsequently generate families based on common architectures. Our integrated approach increases sequence coverage by 7.2 percentage points and residue coverage by 14.6 percentage points higher than the coverage relative to the best single-constituent database within InterPro for the proteome of Arabidopsis. The true power of GFam lies in maximizing annotation provided by the different InterPro data sources that offer resource-specific coverage for different regions of a sequence. GFam’s capability to capture higher sequence and residue coverage can be useful for genome annotation, comparative genomics and functional studies. GFam is a general-purpose software and can be used for any collection of protein sequences. The software is open source and can be obtained from http://www.paccanarolab.org/software/gfam/.     

The predictive power of the CluSTr database

SUMMARY: The CluSTr database employs a fully automatic single-linkage hierarchical clustering method based on a similarity matrix. In order to compute the matrix, first all-against-all pair-wise comparisons between protein sequences are computed using the Smith-Waterman algorithm. The statistical significance of the similarity scores is then assessed using a Monte Carlo analysis, yielding Z-values, which are used to populate the matrix. This paper describes automated annotation experiments that quantify the predictive power and hence the biological relevance of the CluSTr data. The experiments utilized the UniProt data-mining framework to derive annotation predictions using combinations of InterPro and CluSTr. We show that this combination of data sources greatly increases the precision of predictions made by the data-mining framework, compared with the use of InterPro data alone. We conclude that the CluSTr approach to clustering proteins makes a valuable contribution to traditional protein classifications. AVAILABILITY: http://www.ebi.ac.uk/clustr/.     

Functional information in SWISS-PROT: the basis for large-scale characterisation of protein sequences

With the rapid growth of sequence databases, there is an increasing need for reliable functional characterisation and annotation of newly predicted proteins. To cope with such large data volumes, faster and more effective means of protein sequence characterisation and annotation are required. One promising approach is automatic large-scale functional characterisation and annotation, which is generated with limited human interaction. However, such an approach is heavily dependent on reliable data sources. The SWISS-PROT protein sequence database plays an essential role here owing to its high level of functional information.     

Automated genome sequence analysis and annotation.

MOTIVATION: Large-scale genome projects generate a rapidly increasing number of sequences, most of them biochemically uncharacterized. Research in bioinformatics contributes to the development of methods for the computational characterization of these sequences. However, the installation and application of these methods require experience and are time consuming. RESULTS: We present here an automatic system for preliminary functional annotation of protein sequences that has been applied to the analysis of sets of sequences from complete genomes, both to refine overall performance and to make new discoveries comparable to those made by human experts. The GeneQuiz system includes a Web-based browser that allows examination of the evidence leading to an automatic annotation and offers additional information, views of the results, and links to biological databases that complement the automatic analysis. System structure and operating principles concerning the use of multiple sequence databases, underlying sequence analysis tools, lexical analyses of database annotations and decision criteria for functional assignments are detailed. The system makes automatic quality assessments of results based on prior experience with the underlying sequence analysis tools; overall error rates in functional assignment are estimated at 2.5-5% for cases annotated with highest reliability ('clear' cases). Sources of over-interpretation of results are discussed with proposals for improvement. A conservative definition for reporting 'new findings' that takes account of database maturity is presented along with examples of possible kinds of discoveries (new function, family and superfamily) made by the system. System performance in relation to sequence database coverage, database dynamics and database search methods is analysed, demonstrating the inherent advantages of an integrated automatic approach using multiple databases and search methods applied in an objective and repeatable manner. AVAILABILITY: The GeneQuiz system is publicly available for analysis of protein sequences through a Web server at http://www.sander.ebi.ac. uk/gqsrv/submit     

RiceGAAS: an automated annotation system and database for rice genome sequence

An extensive effort of the International Rice Genome Sequencing Project (IRGSP) has resulted in rapid accumulation of genome sequence, and >137 Mb has already been made available to the public domain as of August 2001. This requires a high-throughput annotation scheme to extract biologically useful and timely information from the sequence data on a regular basis. A new automated annotation system and database called Rice Genome Automated Annotation System (RiceGAAS) has been developed to execute a reliable and up-to-date analysis of the genome sequence as well as to store and retrieve the results of annotation. The system has the following functional features: (i) collection of rice genome sequences from GenBank; (ii) execution of gene prediction and homology search programs; (iii) integration of results from various analyses and automatic interpretation of coding regions; (iv) re-execution of analysis, integration and automatic interpretation with the latest entries in reference databases; (v) integrated visualization of the stored data using web-based graphical view. RiceGAAS also has a data submission mechanism that allows public users to perform fully automated annotation of their own sequences. The system can be accessed at http://RiceGAAS.dna.affrc.go.jp/.     

MIPS bacterial genomes functional annotation benchmark dataset

MOTIVATION: Any development of new methods for automatic functional annotation of proteins according to their sequences requires high-quality data (as benchmark) as well as tedious preparatory work to generate sequence parameters required as input data for the machine learning methods. Different program settings and incompatible protocols make a comparison of the analyzed methods difficult. RESULTS: The MIPS Bacterial Functional Annotation Benchmark dataset (MIPS-BFAB) is a new, high-quality resource comprising four bacterial genomes manually annotated according to the MIPS functional catalogue (FunCat). These resources include precalculated sequence parameters, such as sequence similarity scores, InterPro domain composition and other parameters that could be used to develop and benchmark methods for functional annotation of bacterial protein sequences. These data are provided in XML format and can be used by scientists who are not necessarily experts in genome annotation. AVAILABILITY: BFAB is available at http://mips.gsf.de/proj/bfab     

BioPD: a web-based information center for bioactive peptides

Bioactive peptide database (BioPD) is a web-based knowledge base that contains more than 1100 protein sequences from human, mouse and rat, which are putative or are known to be bioactive peptides. In addition to peptide sequences and the annotation, the database also contains gene sequences with annotation, protein interaction and disease data related to the peptides. Each entry has as many references as possible to support the information represented. BioPD consists of six parts: PROTEIN, GENE, DISEASE, LINKS, INTERACTION, and REFERENCE. The database is searchable through keyword, gene and protein name, receptor name, etc. The links to PDB, InterPro, Pfam, OMIM, etc. are provided in each entry. Thus BioPD is formed as an information center for the bioactive peptide and serves as a gateway for exploration of bioactive peptides. The database can be accessed at http://biopd.bjmu.edu.cn.     

In Silico Characterization of Proteins: UniProt,InterPro and Integr8

Nucleic acid sequences from genome sequencing projects are submitted as raw data, from which biologists attempt to elucidate the function of the predicted gene products. The protein sequences are stored in public databases, such as the UniProt Knowledgebase (UniProtKB), where curators try to add predicted and experimental functional information. Protein function prediction can be done using sequence similarity searches, but an alternative approach is to use protein signatures, which classify proteins into families and domains. The major protein signature databases are available through the integrated InterPro database, which provides a classification of UniProtKB sequences. As well as characterization of proteins through protein families, many researchers are interested in analyzing the complete set of proteins from a genome (i.e. the proteome), and there are databases and resources that provide non-redundant proteome sets and analyses of proteins from organisms with completely sequenced genomes. This article reviews the tools and resources available on the web for single and large-scale protein characterization and whole proteome analysis.     

CluSTr: a database of clusters of SWISS-PROT+TrEMBL proteins

The CluSTr (Clusters of SWISS-PROT and TrEMBL proteins) database offers an automatic classification of SWISS-PROT and TrEMBL proteins into groups of related proteins. The clustering is based on analysis of all pairwise comparisons between protein sequences. Analysis has been carried out for different levels of protein similarity, yielding a hierarchical organisation of clusters. The database provides links to InterPro, which integrates information on protein families, domains and functional sites from PROSITE, PRINTS, Pfam and ProDom. Links to the InterPro graphical interface allow users to see at a glance whether proteins from the cluster share particular functional sites. CluSTr also provides cross-references to HSSP and PDB. The database is available for querying and browsing at http://www.ebi.ac.uk/clustr.     

The role SWISS-PROT and TrEMBL play in the genome research environment

SWISS-PROT, a curated protein sequence data bank, contains not only sequence data but also annotation relevant to a particular sequence. The annotation added to each entry is done by a team of biologists and comes, primarily, from articles in journals reporting the actual sequencing and sometimes characterisation. Review articles and collaboration with external experts also play a role along with the use of secondary databases like PROSITE and Pfam in addition to a variety of feature prediction methods. Annotation added by these methods is checked for relevance and likelihood to a particular sequence. The onset of genome sequencing has led to a dramatic increase in sequence data to be included in SWISS-PROT. This has led to the production of TrEMBL (Translation of the EMBL database). TrEMBL consists of entries in a SWISS-PROT format that are derived from the translation of all coding sequences in the EMBL nucleotide sequence database, that are not in SWISS-PROT. Unlike SWISS-PROT entries those in TrEMBL are awaiting manual annotation. However, rather than just representing basic sequence and source information, steps have been taken to add features and annotation automatically. In taking these steps it is hoped that TrEMBL entries are enhanced with some indication as to what a protein is, could or may be.     

Functional annotation prediction: all for one and one for all

In an era of rapid genome sequencing and high-throughput technology, automatic function prediction for a novel sequence is of utter importance in bioinformatics. While automatic annotation methods based on local alignment searches can be simple and straightforward, they suffer from several drawbacks, including relatively low sensitivity and assignment of incorrect annotations that are not associated with the region of similarity. ProtoNet is a hierarchical organization of the protein sequences in the UniProt database. Although the hierarchy is constructed in an unsupervised automatic manner, it has been shown to be coherent with several biological data sources. We extend the ProtoNet system in order to assign functional annotations automatically. By leveraging on the scaffold of the hierarchical classification, the method is able to overcome some frequent annotation pitfalls.     

PRINTS and PRINTS-S shed light on protein ancestry

The PRINTS database houses a collection of protein fingerprints. These may be used to make family and tentative functional assignments for uncharacterised sequences. The September 2001 release (version 32.0) includes 1600 fingerprints, encoding ~10 000 motifs, covering a range of globular and membrane proteins, modular polypeptides and so on. In addition to its continued steady growth, we report here its use as a source of annotation in the InterPro resource, and the use of its relational cousin, PRINTS-S, to model relationships between families, including those beyond the reach of conventional sequence analysis approaches. The database is accessible for BLAST, fingerprint and text searches at http://www.bioinf.man.ac.uk/dbbrowser/PRINTS/.     

A new set of bioinformatics tools for genome projects

A new tool called System for Automated Bacterial Integrated Annotation--SABIA (SABIA being a very well-known bird in Brazil) was developed for the assembly and annotation of bacterial genomes. This system performs automatic tasks of assembly analysis, ORFs identification/analysis, and extragenic region analyses. Genome assembly and contig automatic annotation data are also available in the same working environment. The system integrates several public domains and newly developed software programs capable of dealing with several types of databases, and it is portable to other operational systems. These programs interact with most of the well-known biological database/softwares, such as Glimmer, Genemark, the BLAST family programs, InterPro, COG, Kegg, PSORT, GO, tRNAScan and RBSFinder, and can also be used to identify metabolic pathways.     

Dasty and UniProt DAS: a perfect pair for protein feature visualization

In this study, we present two freely available and complementary Distributed Annotation System (DAS) resources: a DAS reference server that provides up-to-date sequence and annotation from UniProt, with additional feature links and database cross-references from InterPro and a DAS client implemented using Java and Macromedia Flash that is optimized for the display of protein features.     

Mercator: a fast and simple web server for genome scale functional annotation of plant sequence data

Next‐generation technologies generate an overwhelming amount of gene sequence data. Efficient annotation tools are required to make these data amenable to functional genomics analyses. The Mercator pipeline automatically assigns functional terms to protein or nucleotide sequences. It uses the MapMan ‘BIN’ ontology, which is tailored for functional annotation of plant ‘omics’ data. The classification procedure performs parallel sequence searches against reference databases, compiles the results and computes the most likely MapMan BINs for each query. In the current version, the pipeline relies on manually curated reference classifications originating from the three reference organisms (Arabidopsis, Chlamydomonas, rice), various other plant species that have a reviewed SwissProt annotation, and more than 2000 protein domain and family profiles at InterPro, CDD and KOG. Functional annotations predicted by Mercator achieve accuracies above 90% when benchmarked against manual annotation. In addition to mapping files for direct use in the visualization software MapMan, Mercator provides graphical overview charts, detailed annotation information in a convenient web browser interface and a MapMan‐to‐GO translation table to export results as GO terms. Mercator is available free of charge via http://mapman.gabipd.org/web/guest/app/Mercator .     

ProDom: automated clustering of homologous domains

The ProDom database is a comprehensive set of protein domain families automatically generated from the SWISS-PROT and TrEMBL sequence databases. An associated database, ProDom-CG, has been derived as a restriction of ProDom to completely sequenced genomes. The ProDom construction method is based on iterative PSI-BLAST searches and multiple alignments are generated for each domain family. The ProDom web server provides the user with a set of tools to visualise multiple alignments, phylogenetic trees and domain architectures of proteins, as well as a BLAST-based server to analyse new sequences for homologous domains. The comprehensive nature of ProDom makes it particularly useful to help sustain the growth of InterPro.     

Association algorithm to mine the rules that govern enzyme definition and to classify protein sequences

Background  

The number of sequences compiled in many genome projects is growing exponentially, but most of them have not been characterized experimentally. An automatic annotation scheme must be in an urgent need to reduce the gap between the amount of new sequences produced and reliable functional annotation. This work proposes rules for automatically classifying the fungus genes. The approach involves elucidating the enzyme classifying rule that is hidden in UniProt protein knowledgebase and then applying it for classification. The association algorithm, Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity.