RefSeq Genomes Research Articles

Enterobacterhormaechei subsp. hoffmannii subsp. nov., Enterobacter hormaechei subsp. xiangfangensis comb. nov., Enterobacter roggenkampii sp. nov., and Enterobacter muelleri is a later heterotypic synonym of Enterobacter asburiae based on computational analysis of sequenced Enterobacter genomes.

Background: The predominant species in clinical Enterobacter isolates is E. hormaechei. Many articles, clinicians, and GenBank submissions misname these strains as E. cloacae. The lack of sequenced type strains or named species/subspecies for some clades in the E. cloacae complex complicate the issue. Methods: The genomes of the type strains for Enterobacter hormaecheisubsp. oharae, E. hormaechei subsp. steigerwaltii, and E. xiangfangensis, and two strains from Hoffmann clusters III and IV of the E. cloacae complex were sequenced. These genomes, the E. hormaecheisubsp. hormaechei type strain, and other available Enterobacter type strains were analysed in conjunction with all extant Enterobacter genomes in NCBI's RefSeq using Average Nucleotide Identity (ANI). Results: There were five recognizable subspecies of E. hormaechei: E. hormaechei subsp. hoffmannii subsp. nov., E. hormaechei subsp. xiangfangensis comb. nov., and the three previously known subspecies. One of the strains sequenced from the E. cloacae complex was not a novel E. hormaechei subspecies but rather a member of a clade of a novel species: E. roggenkampii sp. nov.. E. muelleri was determined to be a later heterotypic synonym of E. asburiae which should take precedence. Conclusion: The phylogeny of the Enterobacter genus, particularly the cloacae complex, was re-evaluated based on the type strain genome sequences and all other available Enterobacter genomes in RefSeq.

Enterobacter hormaechei subsp. hoffmannii subsp. nov., Enterobacter hormaechei subsp. xiangfangensis comb. nov., Enterobacter roggenkampii sp. nov., and Enterobacter muelleri is a later heterotypic synonym of Enterobacter asburiae based on computational analysis of sequenced Enterobacter genomes.

Background: The predominant species in clinical Enterobacter isolates is E. hormaechei. Many articles, clinicians, and GenBank submissions misname these strains as E. cloacae. The lack of sequenced type strains or named species/subspecies for some clades in the E. cloacae complex complicate the issue. Methods: The genomes of the type strains for Enterobacter hormaechei subsp. oharae, E. hormaechei subsp. steigerwaltii, and E. xiangfangensis, and two strains from Hoffmann clusters III and IV of the E. cloacae complex were sequenced. These genomes, the E. hormaechei subsp. hormaechei type strain, and other available Enterobacter type strains were analysed in conjunction with all extant Enterobacter genomes in NCBI’s RefSeq using Average Nucleotide Identity (ANI). Results: There were five recognizable subspecies of E. hormaechei: E. hormaechei subsp. hoffmannii subsp. nov., E. hormaechei subsp. xiangfangensis comb. nov., and the three previously known subspecies. One of the strains sequenced from the E. cloacae complex was not a novel E. hormaechei subspecies but rather a member of a clade of a novel species: E. roggenkampii sp. nov.. E. muelleri was determined to be a later heterotypic synonym of E. asburiae which should take precedence. Conclusion: The phylogeny of the Enterobacter genus, particularly the cloacae complex, was re-evaluated based on the type strain genome sequences and all other available Enterobacter genomes in RefSeq.

Enterobacterhormaechei subsp. hoffmannii subsp. nov., Enterobacter hormaechei subsp. xiangfangensis comb. nov., Enterobacter roggenkampii sp. nov., and Enterobacter muelleri is a later heterotypic synonym of Enterobacter asburiae based on computational analysis of sequenced Enterobacter genomes.

Background: The predominant species in clinical Enterobacter isolates is E. hormaechei. Many articles, clinicians, and GenBank submissions misname these strains as E. cloacae. The lack of sequenced type strains or named species/subspecies for some clades in the E. cloacae complex complicate the issue. Methods: The genomes of the type strains for Enterobacter hormaecheisubsp. oharae, E. hormaechei subsp. steigerwaltii, and E. xiangfangensis, and two strains from Hoffmann clusters III and IV of the E. cloacae complex were sequenced. These genomes, the E. hormaecheisubsp. hormaechei type strain, and other available Enterobacter type strains were analysed in conjunction with all extant Enterobacter genomes in NCBI's RefSeq using Average Nucleotide Identity (ANI). Results: There were five recognizable subspecies of E. hormaechei: E. hormaechei subsp. hoffmannii subsp. nov., E. hormaechei subsp. xiangfangensis comb. nov., and the three previously known subspecies. One of the strains sequenced from the E. cloacae complex was not a novel E. hormaechei subspecies but rather a member of a clade of a novel species: E. roggenkampii sp. nov.. E. muelleri was determined to be a later heterotypic synonym of E. asburiae which should take precedence. Conclusion: The phylogeny of the Enterobacter genus, particularly the cloacae complex, was re-evaluated based on the type strain genome sequences and all other available Enterobacter genomes in RefSeq.

GET_PHYLOMARKERS, a Software Package to Select Optimal Orthologous Clusters for Phylogenomics and Inferring Pan-Genome Phylogenies, Used for a Critical Geno-Taxonomic Revision of the Genus Stenotrophomonas.

The massive accumulation of genome-sequences in public databases promoted the proliferation of genome-level phylogenetic analyses in many areas of biological research. However, due to diverse evolutionary and genetic processes, many loci have undesirable properties for phylogenetic reconstruction. These, if undetected, can result in erroneous or biased estimates, particularly when estimating species trees from concatenated datasets. To deal with these problems, we developed GET_PHYLOMARKERS, a pipeline designed to identify high-quality markers to estimate robust genome phylogenies from the orthologous clusters, or the pan-genome matrix (PGM), computed by GET_HOMOLOGUES. In the first context, a set of sequential filters are applied to exclude recombinant alignments and those producing anomalous or poorly resolved trees. Multiple sequence alignments and maximum likelihood (ML) phylogenies are computed in parallel on multi-core computers. A ML species tree is estimated from the concatenated set of top-ranking alignments at the DNA or protein levels, using either FastTree or IQ-TREE (IQT). The latter is used by default due to its superior performance revealed in an extensive benchmark analysis. In addition, parsimony and ML phylogenies can be estimated from the PGM. We demonstrate the practical utility of the software by analyzing 170 Stenotrophomonas genome sequences available in RefSeq and 10 new complete genomes of Mexican environmental S. maltophilia complex (Smc) isolates reported herein. A combination of core-genome and PGM analyses was used to revise the molecular systematics of the genus. An unsupervised learning approach that uses a goodness of clustering statistic identified 20 groups within the Smc at a core-genome average nucleotide identity (cgANIb) of 95.9% that are perfectly consistent with strongly supported clades on the core- and pan-genome trees. In addition, we identified 16 misclassified RefSeq genome sequences, 14 of them labeled as S. maltophilia, demonstrating the broad utility of the software for phylogenomics and geno-taxonomic studies. The code, a detailed manual and tutorials are freely available for Linux/UNIX servers under the GNU GPLv3 license at https://github.com/vinuesa/get_phylomarkers. A docker image bundling GET_PHYLOMARKERS with GET_HOMOLOGUES is available at https://hub.docker.com/r/csicunam/get_homologues/, which can be easily run on any platform.

Transcriptomic changes in CHO cells after adaptation to suspension growth in protein-free medium analysed by a species-specific microarray

Chinese Hamster Ovary (CHO) cells are the preferred cell line for production of biopharmaceuticals. These cells are capable to grow without serum supplementation, but drastic changes in their phenotype occur during adaptation to protein-free growth, which typically include the change to a suspension phenotype with reduced growth rate. A possible approach to understand this transformation, with the intention to counteract the reduction in growth by targeted supplementation of protein-free media, is gene expression profiling. The increasing availability of genome-scale data for CHO now facilitates quests for a better understanding of metabolic pathways and gene networks. So far, systematic large-scale expression profiling in CHO cells by microarray was limited due to lack of publicly available array designs and limitations of alternative approaches. Based on the recent release of CHO and Chinese Hamster genome sequences, including an annotated RefSeq genome, we have constructed a publicly available microarray design for effective genome-scale expression profiling. The design employed microarray probes optimized for uniformity, sensitivity, and specificity, with probe properties computed using the latest thermodynamic models. We validated the platform in an analysis of gene expression changes in response to serum-free adaptation. The observed effects on the lipid metabolism as well as on nucleotide synthesis were used to successfully select media supplements that were able to increase growth rate.

Viral Phylogenomics Using an Alignment-Free Method: A Three-Step Approach to Determine Optimal Length of k-mer

The development of rapid, economical genome sequencing has shed new light on the classification of viruses. As of October 2016, the National Center for Biotechnology Information (NCBI) database contained >2 million viral genome sequences and a reference set of ~4000 viral genome sequences that cover a wide range of known viral families. Whole-genome sequences can be used to improve viral classification and provide insight into the viral “tree of life”. However, due to the lack of evolutionary conservation amongst diverse viruses, it is not feasible to build a viral tree of life using traditional phylogenetic methods based on conserved proteins. In this study, we used an alignment-free method that uses k-mers as genomic features for a large-scale comparison of complete viral genomes available in RefSeq. To determine the optimal feature length, k (an essential step in constructing a meaningful dendrogram), we designed a comprehensive strategy that combines three approaches: (1) cumulative relative entropy, (2) average number of common features among genomes, and (3) the Shannon diversity index. This strategy was used to determine k for all 3,905 complete viral genomes in RefSeq. The resulting dendrogram shows consistency with the viral taxonomy of the ICTV and the Baltimore classification of viruses.

Mash: fast genome and metagenome distance estimation using MinHash.

Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections. Mash reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated. We demonstrate several use cases, including the clustering of all 54,118 NCBI RefSeq genomes in 33 CPU h; real-time database search using assembled or unassembled Illumina, Pacific Biosciences, and Oxford Nanopore data; and the scalable clustering of hundreds of metagenomic samples by composition. Mash is freely released under a BSD license (https://github.com/marbl/mash).Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0997-x) contains supplementary material, which is available to authorized users.

From data repositories to submission portals: rethinking the role of domain-specific databases in CollecTF.

Domain-specific databases are essential resources for the biomedical community, leveraging expert knowledge to curate published literature and provide access to referenced data and knowledge. The limited scope of these databases, however, poses important challenges on their infrastructure, visibility, funding and usefulness to the broader scientific community. CollecTF is a community-oriented database documenting experimentally validated transcription factor (TF)-binding sites in the Bacteria domain. In its quest to become a community resource for the annotation of transcriptional regulatory elements in bacterial genomes, CollecTF aims to move away from the conventional data-repository paradigm of domain-specific databases. Through the adoption of well-established ontologies, identifiers and collaborations, CollecTF has progressively become also a portal for the annotation and submission of information on transcriptional regulatory elements to major biological sequence resources (RefSeq, UniProtKB and the Gene Ontology Consortium). This fundamental change in database conception capitalizes on the domain-specific knowledge of contributing communities to provide high-quality annotations, while leveraging the availability of stable information hubs to promote long-term access and provide high-visibility to the data. As a submission portal, CollecTF generates TF-binding site information through direct annotation of RefSeq genome records, definition of TF-based regulatory networks in UniProtKB entries and submission of functional annotations to the Gene Ontology. As a database, CollecTF provides enhanced search and browsing, targeted data exports, binding motif analysis tools and integration with motif discovery and search platforms. This innovative approach will allow CollecTF to focus its limited resources on the generation of high-quality information and the provision of specialized access to the data.Database URL: http://www.collectf.org/

Mouse genome annotation by the RefSeq project.

Complete and accurate annotation of the mouse genome is critical to the advancement of research conducted on this important model organism. The National Center for Biotechnology Information (NCBI) develops and maintains many useful resources to assist the mouse research community. In particular, the reference sequence (RefSeq) database provides high-quality annotation of multiple mouse genome assemblies using a combinatorial approach that leverages computation, manual curation, and collaboration. Implementation of this conservative and rigorous approach, which focuses on representation of only full-length and non-redundant data, produces high-quality annotation products. RefSeq records explicitly link sequences to current knowledge in a timely manner, updating public records regularly and rapidly in response to nomenclature updates, addition of new relevant publications, collaborator discussion, and user feedback. Whole genome re-annotation is also conducted at least every 12–18 months, and often more frequently in response to assembly updates or availability of informative data. This article highlights key features and advantages of RefSeq genome annotation products and presents an overview of NCBI processes to generate these data. Further discussion of NCBI’s resources highlights useful features and the best methods for accessing our data.

Update on RefSeq microbial genomes resources.

NCBI RefSeq genome collection http://www.ncbi.nlm.nih.gov/genome represents all three major domains of life: Eukarya, Bacteria and Archaea as well as Viruses. Prokaryotic genome sequences are the most rapidly growing part of the collection. During the year of 2014 more than 10 000 microbial genome assemblies have been publicly released bringing the total number of prokaryotic genomes close to 30 000. We continue to improve the quality and usability of the microbial genome resources by providing easy access to the data and the results of the pre-computed analysis, and improving analysis and visualization tools. A number of improvements have been incorporated into the Prokaryotic Genome Annotation Pipeline. Several new features have been added to RefSeq prokaryotic genomes data processing pipeline including the calculation of genome groups (clades) and the optimization of protein clusters generation using pan-genome approach.

Gene: a gene-centered information resource at NCBI.

The National Center for Biotechnology Information's (NCBI) Gene database (www.ncbi.nlm.nih.gov/gene) integrates gene-specific information from multiple data sources. NCBI Reference Sequence (RefSeq) genomes for viruses, prokaryotes and eukaryotes are the primary foundation for Gene records in that they form the critical association between sequence and a tracked gene upon which additional functional and descriptive content is anchored. Additional content is integrated based on the genomic location and RefSeq transcript and protein sequence data. The content of a Gene record represents the integration of curation and automated processing from RefSeq, collaborating model organism databases, consortia such as Gene Ontology, and other databases within NCBI. Records in Gene are assigned unique, tracked integers as identifiers. The content (citations, nomenclature, genomic location, gene products and their attributes, phenotypes, sequences, interactions, 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 Entrez Direct) and for bulk transfer by FTP.

RefSeq: an update on mammalian reference sequences

The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database is a collection of annotated genomic, transcript and protein sequence records derived from data in public sequence archives and from computation, curation and collaboration (http://www.ncbi.nlm.nih.gov/refseq/). We report here on growth of the mammalian and human subsets, changes to NCBI’s eukaryotic annotation pipeline and modifications affecting transcript and protein records. Recent changes to NCBI’s eukaryotic genome annotation pipeline provide higher throughput, and the addition of RNAseq data to the pipeline results in a significant expansion of the number of transcripts and novel exons annotated on mammalian RefSeq genomes. Recent annotation changes include reporting supporting evidence for transcript records, modification of exon feature annotation and the addition of a structured report of gene and sequence attributes of biological interest. We also describe a revised protein annotation policy for alternatively spliced transcripts with more divergent predicted proteins and we summarize the current status of the RefSeqGene project.

Improved systematic tRNA gene annotation allows new insights into the evolution of mitochondrial tRNA structures and into the mechanisms of mitochondrial genome rearrangements

Transfer RNAs (tRNAs) are present in all types of cells as well as in organelles. tRNAs of animal mitochondria show a low level of primary sequence conservation and exhibit ‘bizarre’ secondary structures, lacking complete domains of the common cloverleaf. Such sequences are hard to detect and hence frequently missed in computational analyses and mitochondrial genome annotation. Here, we introduce an automatic annotation procedure for mitochondrial tRNA genes in Metazoa based on sequence and structural information in manually curated covariance models. The method, applied to re-annotate 1876 available metazoan mitochondrial RefSeq genomes, allows to distinguish between remaining functional genes and degrading ‘pseudogenes’, even at early stages of divergence. The subsequent analysis of a comprehensive set of mitochondrial tRNA genes gives new insights into the evolution of structures of mitochondrial tRNA sequences as well as into the mechanisms of genome rearrangements. We find frequent losses of tRNA genes concentrated in basal Metazoa, frequent independent losses of individual parts of tRNA genes, particularly in Arthropoda, and wide-spread conserved overlaps of tRNAs in opposite reading direction. Direct evidence for several recent Tandem Duplication-Random Loss events is gained, demonstrating that this mechanism has an impact on the appearance of new mitochondrial gene orders.

Capturing the Perfect Reference Genome

Capturing the Perfect Reference Genome

Reprogramming the human cancer cell nucleus

Background The human cancer cell nucleus contains 46 or more chromosomes, each bearing portions of the human genome. During the initiation and progression of the neoplastic state, chromosome portions can be duplicated, deleted, translocated or inverted, and these lesions often aggravate the rate of progression and metastasis of the cells. During gene transcription, two or more chromosomes can form gene clusters at specific gene sites, and such clusters regulate the rate of gene transcription and replication. Gene clusters are often sensitive to the immediate effects of ligand microRNAs (miRNAs) and other transcribed ultra-conserved noncoding RNAs (T-UCRs). Recent studies have reported 481 species of T-UCRs within human neuroblastoma cells, mostly from intragenic exon and/or intron sequences within the Ref-seq genome, but 37% were found transcribed from noncoding intergenic sites in the neoplastic cell genome [1]. In 237 of the 481 T-UCRs, intra-nuclear functions were completely independent of those within coding and other nuclear RNAs, and were increased in neuroblastomas of an aggressive type. Most of the TUCRs could be found in linked regions of 4 major gene clusters, associated with the 4 nuclear processes of neoplastic proliferation, apoptosis, differentiation, and patient survival. Similar T-UCR RNA patterns in normal human fibroblast BJ cells were also observed [1]. Earlier observations had demonstrated a specific deficiency of let-7 RNA microRNA species within human lung and breast neoplasms, that was reversed by the addition of let-7 RNA species to the neoplastic cells in culture [2,3].

IUPHAR-DB: An Expert-Curated, Peer-Reviewed Database of Receptors and Ion Channels

AbstractThe International Union of Basic and Clinical Pharmacology database (IUPHAR-DB) integrates peer-reviewed pharmacological, chemical, genetic, functional and anatomical information on the 354 non-sensory G protein-coupled receptors (GPCRs), 71 ligand-gated ion channel subunits and 141 voltage-gated ion channel subunits encoded by the human, rat and mouse genomes. These genes represent the targets of about a third of currently approved drugs and are a major focus of drug discovery and development programs in the pharmaceutical industry. Individual gene pages provide a comprehensive description of the genes and their functions, with information on protein structure, ligands, expression patterns, signaling mechanisms, functional assays and biologically important receptor variants (e.g. single nucleotide polymorphisms and splice variants). The phenotypes resulting from altered gene expression (e.g. in genetically altered animals) and genetic mutations are described. Links are provided to bioinformatics resources such as NCBI RefSeq, OMIM, PubChem, human, rat and mouse genome databases. Recent developments include the addition of ligand-centered pages summarising information about unique ligand molecules in IUPHAR-DB. IUPHAR-DB represents a novel approach to biocuration because most data are provided through manual curation of published literature by a network of over 60 expert subcommittees coordinated by NC-IUPHAR. Data are referenced to the primary literature and linked to PubMed. The data are checked to ensure accuracy and consistency by the curators, added to the production server using custom-built submission tools and peer-reviewed by NC-IUPHAR, before being transferred to the public database. Data are reviewed and updated regularly (at least biennially). Other website features include comprehensive database search tools, online and downloadable gene lists and links to recent publications of interest to the field, such as reports on receptor-ligand pairings. The database is freely available at "http://www.iuphar-db.org":http://www.iuphar-db.org. Curators can be reached at curators [at] iuphar-db.org. We thank British Pharmacological Society, UNESCO (through the ICSU Grants Programme), Incyte, GlaxoSmithKline, Novartis, Servier and Wyeth for their support.

IUPHAR-DB: An Expert-Curated, Peer-Reviewed Database of Receptors and Ion Channels

The International Union of Basic and Clinical Pharmacology database (IUPHAR-DB) integrates peer-reviewed pharmacological, chemical, genetic, functional and anatomical information on the 354 non-sensory G protein-coupled receptors (GPCRs), 71 ligand-gated ion channel subunits and 141 voltage-gated ion channel subunits encoded by the human, rat and mouse genomes. These genes represent the targets of about a third of currently approved drugs and are a major focus of drug discovery and development programs in the pharmaceutical industry. Individual gene pages provide a comprehensive description of the genes and their functions, with information on protein structure, ligands, expression patterns, signaling mechanisms, functional assays and biologically important receptor variants (e.g. single nucleotide polymorphisms and splice variants). The phenotypes resulting from altered gene expression (e.g. in genetically altered animals) and genetic mutations are described. Links are provided to bioinformatics resources such as NCBI RefSeq, OMIM, PubChem, human, rat and mouse genome databases. Recent developments include the addition of ligand-centered pages summarising information about unique ligand molecules in IUPHAR-DB. IUPHAR-DB represents a novel approach to biocuration because most data are provided through manual curation of published literature by a network of over 60 expert subcommittees coordinated by NC-IUPHAR. Data are referenced to the primary literature and linked to PubMed. The data are checked to ensure accuracy and consistency by the curators, added to the production server using custom-built submission tools and peer-reviewed by NC-IUPHAR, before being transferred to the public database. Data are reviewed and updated regularly (at least biennially). Other website features include comprehensive database search tools, online and downloadable gene lists and links to recent publications of interest to the field, such as reports on receptor-ligand pairings. The database is freely available at "http://www.iuphar-db.org":http://www.iuphar-db.org. Curators can be reached at curators [at] iuphar-db.org. We thank British Pharmacological Society, UNESCO (through the ICSU Grants Programme), Incyte, GlaxoSmithKline, Novartis, Servier and Wyeth for their support.

National center for biotechnology information viral genomes project.

The Viral Genomes Project aims to provide molecular standards for viral genomic research. The project has produced over 1,600 records for more than 1,200 different species. The National Center for Biotechnology Information (NCBI) provides access to this data through the Entrez search and retrieval engine and offers visualization of the sequence information at various levels of detail. Taxonomically organized displays, precomputed sequence comparison data, and direct access to analytical tools provide researchers with the ability to analyze and compare viral genomes and proteomes in a fast and convenient manner. The Viral Genomes Project is a collaborative effort between NCBI staff and many dedicated scientists worldwide. The URL for the database is http://www.ncbi.nlm.nih.gov/genomes/VIRUSES/viruses.html. As the number of viral records in the public sequence databases (GenBank, EMBL, and DDBJ) grows, retrieving a viral genomic sequence of interest with associated information is becoming increasingly complex. High redundancy in the databases is a common problem for all organisms; in the case of viruses, however, the large number of available strains, isolates, and mutants further exacerbates the problem. For example, a search of Entrez Nucleotide currently retrieves more than 95,500 records for Human immunodeficiency virus 1 (HIV-1) and more than 22,500 records for Hepatitis C virus (HCV) alone; the total number of viral nucleotide records exceeds 220,000. Among these are both partial and complete genomic sequences, including partial sequences marked as a complete genome by submitters. Historically, sequence databases were merely archives of sequences directly submitted by users. Although a stricter submission procedure has been applied in recent years and therefore the quality of sequence records has greatly improved, a significant number of records are still underannotated, and the information in the old sequence records is often outdated. Furthermore, viral genomes are remarkably variable, consisting of either single-stranded or double-stranded DNA or RNA in either linear or circular form and comprising one or more segments. This variability makes viral records especially prone to inaccuracies in molecular information annotation. To cope with these problems, NCBI has created the Viral Genomes Project as a part of the NCBI Genomes Project (19). Only complete or, occasionally, nearly complete viral genomic sequences missing only nontranslated portions (usually the ends of a genomic molecule) are being collected for this project, thereby greatly reducing redundancy. All available complete viral genomic sequences are being collected in order to faithfully represent the great genome variability found in many viruses. For example, 314 complete genome sequences of HIV-1 from various strains and isolates are included in the Entrez Genome collection. But only one sequence ({type:entrez-nucleotide,attrs:{text:NC_001802,term_id:9629357}}NC_001802) has been selected as a reference (RefSeq) to serve as a molecular standard. RefSeq records are manually curated to correct and update content in the original sequence records, which often involves consultations with the original submitters and/or other outside experts. The collection of preselected reference sequences greatly facilitates comparison of the genomes of different viruses. As of December 2003, the Viral Genomes Project contained 1,677 viral reference genomic sequences representing 1,223 virus species, which make a significant contribution to the NCBI RefSeq collection (13). Figure ​Figure11 shows the growth of the viral RefSeq collection during the past 3 years. FIG. 1. The growth of NCBI's Viral Genomes Project. The bars represent the numbers of new and all viral genome reference sequences in each quarter. While a number of databases provide information on viral sequences, most of them are limited to certain families or groups (reviewed in references 4, 3, 6, and 12; http://www.dpvweb.net/index.php). The most comprehensive and well-established viral database, ICTVdB, provides “searchable descriptions of virus isolates, species, genera, families, orders; images of many viruses; and links to genomic and protein databanks” (2). ICTVdB has been a primary resource for information about biological properties of viruses. It plays a major role in viral taxonomic classification, on which our project relied heavily. ICTVdB does present links to viral sequences, but these sequences are original records from public sequence databases and therefore may contain inaccurate or outdated information. The Viral Genomes Project described here is the first comprehensive resource that provides access to the curated set of complete viral genomes in an easily navigable way and offers a collection of tools and precomputed results which greatly facilitate viral genome analysis. These precomputed analyses and tools include the global alignment of genome neighbors, available in both text and graphical forms; viral protein clusters (VOGs), (putative) functional and evolutionary groups of viral proteins derived from RefSeq genomes (which eliminates redundancy) and classified by sequence similarity; convenient VOG displays, including those integrated with the Conserved Domain Database (CDD), an NCBI collection of conserved protein domains; and a BLAST search against a selected set of viral proteins. To start exploring the Viral Genomes resources, go to http://www.ncbi.nlm.nih.gov/genomes/VIRUSES/viruses.html.