NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins

NCBI's reference sequence (RefSeq) database () is a curated non-redundant collection of sequences representing genomes, transcripts and proteins. The database includes 3774 organisms spanning prokaryotes, eukaryotes and viruses, and has records for 2 879 860 proteins (RefSeq release 19). RefSeq records integrate information from multiple sources, when additional data are available from those sources and therefore represent a current description of the sequence and its features. Annotations include coding regions, conserved domains, tRNAs, sequence tagged sites (STS), variation, references, gene and protein product names, and database cross-references. Sequence is reviewed and features are added using a combined approach of collaboration and other input from the scientific community, prediction, propagation from GenBank and curation by NCBI staff. The format of all RefSeq records is validated, and an increasing number of tests are being applied to evaluate the quality of sequence and annotation, especially in the context of complete genomic sequence

Entrez Gene: gene-centered information at NCBI

Entrez Gene () is NCBI's database for gene-specific information. Entrez Gene includes records from genomes that have been completely sequenced, that have an active research community to contribute gene-specific information or that are scheduled for intense sequence analysis. The content of Entrez Gene represents the result of both curation and automated integration of data from NCBI's Reference Sequence project (RefSeq), from collaborating model organism databases and from other databases within NCBI. Records in Entrez Gene are assigned unique, stable and tracked integers as identifiers. The content (nomenclature, map location, gene products and their attributes, markers, phenotypes and links to citations, sequences, variation details, maps, expression, homologs, protein domains and external databases) is provided via interactive browsing through NCBI's Entrez system, via NCBI's Entrez programing utilities (E-Utilities), and for bulk transfer by ftp

Entrez Gene: gene-centered information at NCBI

Entrez Gene (http://www.ncbi.nlm.nih.gov/gene) is National Center for Biotechnology Information (NCBI)’s database for gene-specific information. Entrez Gene maintains records from genomes which have been completely sequenced, which have an active research community to submit gene-specific information, or which are scheduled for intense sequence analysis. The content represents the integration of curation and automated processing from NCBI’s Reference Sequence project (RefSeq), collaborating model organism databases, consortia such as Gene Ontology and other databases within NCBI. Records in Entrez Gene are assigned unique, stable and tracked integers as identifiers. The content (nomenclature, genomic location, gene products and their attributes, markers, phenotypes and links to citations, sequences, variation details, maps, expression, homologs, protein domains and external databases) is available via interactive browsing through NCBI’s Entrez system, via NCBI’s Entrez programming utilities (E-Utilities) and for bulk transfer by FTP

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

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

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 relationship of protein conservation and sequence length

BACKGROUND: In general, the length of a protein sequence is determined by its function and the wide variance in the lengths of an organism's proteins reflects the diversity of specific functional roles for these proteins. However, additional evolutionary forces that affect the length of a protein may be revealed by studying the length distributions of proteins evolving under weaker functional constraints. RESULTS: We performed sequence comparisons to distinguish highly conserved and poorly conserved proteins from the bacterium Escherichia coli, the archaeon Archaeoglobus fulgidus, and the eukaryotes Saccharomyces cerevisiae, Drosophila melanogaster, and Homo sapiens. For all organisms studied, the conserved and nonconserved proteins have strikingly different length distributions. The conserved proteins are, on average, longer than the poorly conserved ones, and the length distributions for the poorly conserved proteins have a relatively narrow peak, in contrast to the conserved proteins whose lengths spread over a wider range of values. For the two prokaryotes studied, the poorly conserved proteins approximate the minimal length distribution expected for a diverse range of structural folds. CONCLUSIONS: There is a relationship between protein conservation and sequence length. For all the organisms studied, there seems to be a significant evolutionary trend favoring shorter proteins in the absence of other, more specific functional constraints