Dealing with the Data Deluge – New Strategies in Prokaryotic Genome Analysis

Recent technological innovations have ignited an explosion in microbial genome sequencing that has fundamentally changed our understanding of biology of microbes and profoundly impacted public health policy. This huge increase in DNA sequence data presents new challenges for the annotation, analysis, and visualization bioinformatics tools. New strategies have been designed to bring an order to this genome sequence shockwave and improve the usability of associated data. Genomes are organized in a hierarchical distance tree using single-copy ribosomal protein marker distances for distance calculation. Protein distance measures dissimilarity between markers of the same type and the subsequent genomic distance averages over the majority of marker-distances, ignoring the outliers. More than 30,000 genomes from public archives have been organized in a marker distance tree resulting in 6,438 species-level clades representing 7,597 taxonomic species. This computational infrastructure provides a foundation for prokaryotic gene and genome analysis, allowing easy access to pre-calculated genome groups at various distance levels. One of the most challenging problems in the current data deluge is the presentation of the relevant data at an appropriate resolution for each application, eliminating data redundancy but keeping biologically interesting variations

FLAN: a web server for influenza virus genome annotation

FLAN (short for FLu ANnotation), the NCBI web server for genome annotation of influenza virus (http://www.ncbi.nlm.nih.gov/genomes/FLU/Database/annotation.cgi) is a tool for user-provided influenza A virus or influenza B virus sequences. It can validate and predict protein sequences encoded by an input flu sequence. The input sequence is BLASTed against a database containing influenza sequences to determine the virus type (A or B), segment (1 through 8) and subtype for the hemagglutinin and neuraminidase segments of influenza A virus. For each segment/subtype of the viruses, a set of sample protein sequences is maintained. The input sequence is then aligned against the corresponding protein set with a ‘Protein to nucleotide alignment tool’ (ProSplign). The translated product from the best alignment to the sample protein sequence is used as the predicted protein encoded by the input sequence. The output can be a feature table that can be used for sequence submission to GenBank (by Sequin or tbl2asn), a GenBank flat file, or the predicted protein sequences in FASTA format. A message showing the length of the input sequence, the predicted virus type, segment and subtype for the hemagglutinin and neuraminidase segments of Influenza A virus will also be displayed

Virus variation resources at the National Center for Biotechnology Information: dengue virus

<p>Abstract</p> <p>Background</p> <p>There is an increasing number of complete and incomplete virus genome sequences available in public databases. This large body of sequence data harbors information about epidemiology, phylogeny, and virulence. Several specialized databases, such as the NCBI Influenza Virus Resource or the Los Alamos HIV database, offer sophisticated query interfaces along with integrated exploratory data analysis tools for individual virus species to facilitate extracting this information. Thus far, there has not been a comprehensive database for dengue virus, a significant public health threat.</p> <p>Results</p> <p>We have created an integrated web resource for dengue virus. The technology developed for the NCBI Influenza Virus Resource has been extended to process non-segmented dengue virus genomes. In order to allow efficient processing of the dengue genome, which is large in comparison with individual influenza segments, we developed an offline pre-alignment procedure which generates a multiple sequence alignment of all dengue sequences. The pre-calculated alignment is then used to rapidly create alignments of sequence subsets in response to user queries. This improvement in technology will also facilitate the incorporation of additional virus species in the future. The set of virus-specific databases at NCBI, which will be referred to as Virus Variation Resources (VVR), allow users to build complex queries against virus-specific databases and then apply exploratory data analysis tools to the results. The metadata is automatically collected where possible, and extended with data extracted from the literature.</p> <p>Conclusion</p> <p>The NCBI Dengue Virus Resource integrates dengue sequence information with relevant metadata (sample collection time and location, disease severity, serotype, sequenced genome region) and facilitates retrieval and preliminary analysis of dengue sequences using integrated web analysis and visualization tools.</p

The National Center for Biotechnology Information's Protein Clusters Database

Rapid increases in DNA sequencing capabilities have led to a vast increase in the data generated from prokaryotic genomic studies, which has been a boon to scientists studying micro-organism evolution and to those who wish to understand the biological underpinnings of microbial systems. The NCBI Protein Clusters Database (ProtClustDB) has been created to efficiently maintain and keep the deluge of data up to date. ProtClustDB contains both curated and uncurated clusters of proteins grouped by sequence similarity. The May 2008 release contains a total of 285 386 clusters derived from over 1.7 million proteins encoded by 3806 nt sequences from the RefSeq collection of complete chromosomes and plasmids from four major groups: prokaryotes, bacteriophages and the mitochondrial and chloroplast organelles. There are 7180 clusters containing 376 513 proteins with curated gene and protein functional annotation. PubMed identifiers and external cross references are collected for all clusters and provide additional information resources. A suite of web tools is available to explore more detailed information, such as multiple alignments, phylogenetic trees and genomic neighborhoods. ProtClustDB provides an efficient method to aggregate gene and protein annotation for researchers and is available at http://www.ncbi.nlm.nih.gov/sites/entrez?db=proteinclusters

The COG database: an updated version includes eukaryotes

<p>Abstract</p> <p>Background</p> <p>The availability of multiple, essentially complete genome sequences of prokaryotes and eukaryotes spurred both the demand and the opportunity for the construction of an evolutionary classification of genes from these genomes. Such a classification system based on orthologous relationships between genes appears to be a natural framework for comparative genomics and should facilitate both functional annotation of genomes and large-scale evolutionary studies.</p> <p>Results</p> <p>We describe here a major update of the previously developed system for delineation of Clusters of Orthologous Groups of proteins (COGs) from the sequenced genomes of prokaryotes and unicellular eukaryotes and the construction of clusters of predicted orthologs for 7 eukaryotic genomes, which we named KOGs after eukaryotic orthologous groups. The COG collection currently consists of 138,458 proteins, which form 4873 COGs and comprise 75% of the 185,505 (predicted) proteins encoded in 66 genomes of unicellular organisms. The eukaryotic orthologous groups (KOGs) include proteins from 7 eukaryotic genomes: three animals (the nematode <it>Caenorhabditis elegans</it>, the fruit fly <it>Drosophila melanogaster </it>and <it>Homo sapiens</it>), one plant, <it>Arabidopsis thaliana</it>, two fungi (<it>Saccharomyces cerevisiae </it>and <it>Schizosaccharomyces pombe</it>), and the intracellular microsporidian parasite <it>Encephalitozoon cuniculi</it>. The current KOG set consists of 4852 clusters of orthologs, which include 59,838 proteins, or ~54% of the analyzed eukaryotic 110,655 gene products. Compared to the coverage of the prokaryotic genomes with COGs, a considerably smaller fraction of eukaryotic genes could be included into the KOGs; addition of new eukaryotic genomes is expected to result in substantial increase in the coverage of eukaryotic genomes with KOGs. Examination of the phyletic patterns of KOGs reveals a conserved core represented in all analyzed species and consisting of ~20% of the KOG set. This conserved portion of the KOG set is much greater than the ubiquitous portion of the COG set (~1% of the COGs). In part, this difference is probably due to the small number of included eukaryotic genomes, but it could also reflect the relative compactness of eukaryotes as a clade and the greater evolutionary stability of eukaryotic genomes.</p> <p>Conclusion</p> <p>The updated collection of orthologous protein sets for prokaryotes and eukaryotes is expected to be a useful platform for functional annotation of newly sequenced genomes, including those of complex eukaryotes, and genome-wide evolutionary studies.</p