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Argania spinosa plays an important socioeconomic and ecologic role in different contrast situations like the arid zone located in the midwestern Morocco. This tree is used as a food and feed and as a resource for both pharmaceutical and cosmetic compounds. In the last decades, this genetic resource has faced significant threats from climate change, environmental factors, and human overexploitation. Argane tree genetics and genomics knowledge will help in counteracting these challenges. Studies have been undertaken to determine the Argane tree’s genetic diversity and depict ecological and sustainability concerns using morphological, biological, biochemical, and standardized molecular marker techniques. The development of affordable next-generation sequencing technologies allowed the sequencing and assembly of the A. spinosa genome using a hybrid de novo assembly approach combining short (Illumina) and long (PacBio) sequencing reads. The final draft genome comprises 75,327 scaffolds totaling 671 Mb. The 158,848 bp full genome of A. spinosa chloroplast (cp) was also assembled and annotated. This Argania spinosa reference genome constitutes a first initiative of the International Argania Genome Consortium toward a global integrative omics approach to bring insight into the biology and evolution of the Argane tree. Metabolic profiling studies on Argania fruit extracts have been conducted to extend the limit of biochemical detection. Prospective research will focus on transcriptome analysis for the sake of improving the prediction-based structural annotation of the genome and to understand the regulation of the genome expression. These genomics studies will facilitate efficient conservation of this endangered endemic tree and may enable genome-assisted cultivar breeding for genetic improvement and adaptation.

Background: The Argane tree ( Argania spinosa L. Skeels) is an endemic tree of mid-western Morocco that plays an important socioeconomic and ecologic role for a dense human population in an arid zone. Several studies confirmed the importance of this species as a food and feed source and as a resource for both pharmaceutical and cosmetic compounds. Unfortunately, the argane tree ecosystem is facing significant threats from environmental changes (global warming, over-population) and over-exploitation. Limited research has been conducted, however, on argane tree genetics and genomics, which hinders its conservation and genetic improvement. Methods: Here, we present a draft genome assembly of A. spinosa. A reliable reference genome of A. spinosa was created using a hybrid de novo assembly approach combining short and long sequencing reads. Results: In total, 144 Gb Illumina HiSeq reads and 7.6 Gb PacBio reads were produced and assembled. The final draft genome comprises 75 327 scaffolds totaling 671 Mb with an N50 of 49 916 kb. The draft assembly is close to the genome size estimated by k-mers distribution and covers 89% of complete and 4.3 % of partial Arabidopsis orthologous groups in BUSCO. Conclusion: The A. spinosa genome will be useful for assessing biodiversity leading to efficient conservation of this endangered endemic tree. Furthermore, the genome may enable genome-assisted cultivar breeding, and provide a better understanding of important metabolic pathways and their underlying genes for both cosmetic and pharmacological.

Recent technological advances have opened unprecedented opportunities for large-scale sequencing and analysis of populations of pathogenic species in disease outbreaks, as well as for large-scale diversity studies aimed at expanding our knowledge across the whole domain of prokaryotes. To meet the challenge of timely interpretation of structure, function and meaning of this vast genetic information, a comprehensive approach to automatic genome annotation is critically needed. In collaboration with Georgia Tech, NCBI has developed a new approach to genome annotation that combines alignment based methods with methods of predicting protein-coding and RNA genes and other functional elements directly from sequence. A new gene finding tool, GeneMarkS+, uses the combined evidence of protein and RNA placement by homology as an initial map of annotation to generate and modify ab initio gene predictions across the whole genome. Thus, the new NCBI's Prokaryotic Genome Annotation Pipeline (PGAP) relies more on sequence similarity when confident comparative data are available, while it relies more on statistical predictions in the absence of external evidence. The pipeline provides a framework for generation and analysis of annotation on the full breadth of prokaryotic taxonomy. For additional information on PGAP see https://www.ncbi.nlm.nih.gov/genome/annotation_prok/ and the NCBI Handbook, https://www.ncbi.nlm.nih.gov/books/NBK174280/.

Argania spinosa (Sapotaceae), an important endemic Moroccan oil tree, is a primary source of argan oil, which has numerous dietary and medicinal proprieties. The plant species occupies the mid-western part of Morocco and provides great environmental and socioeconomic benefits. The complete chloroplast (cp) genome of A. spinosa was sequenced, assembled, and analyzed in comparison with those of two Sapotaceae members. The A. spinosa cp genome is 158,848 bp long, with an average GC content of 36.8%. The cp genome exhibits a typical quadripartite and circular structure consisting of a pair of inverted regions (IR) of 25,945 bp in length separating small single-copy (SSC) and large single-copy (LSC) regions of 18,591 and 88,367 bp, respectively. The annotation of A. spinosa cp genome predicted 130 genes, including 85 protein-coding genes (CDS), 8 ribosomal RNA (rRNA) genes, and 37 transfer RNA (tRNA) genes. A total of 44 long repeats and 88 simple sequence repeats (SSR) divided into mononucleotides (76), dinucleotides (7), trinucleotides (3), tetranucleotides (1), and hexanucleotides (1) were identified in the A. spinosa cp genome. Phylogenetic analyses using the maximum likelihood (ML) method were performed based on 69 protein-coding genes from 11 species of Ericales. The results confirmed the close position of A. spinosa to the Sideroxylon genus, supporting the revisiting of its taxonomic status. The complete chloroplast genome sequence will be valuable for further studies on the conservation and breeding of this medicinally and culinary important species and also contribute to clarifying the phylogenetic position of the species within Sapotaceae.

The NCBI Assembly database (www.ncbi.nlm.nih.gov/assembly/) provides stable accessioning and data tracking for genome assembly data. The model underlying the database can accommodate a range of assembly structures, including sets of unordered contig or scaffold sequences, bacterial genomes consisting of a single complete chromosome, or complex structures such as a human genome with modeled allelic variation. The database provides an assembly accession and version to unambiguously identify the set of sequences that make up a particular version of an assembly, and tracks changes to updated genome assemblies. The Assembly database reports metadata such as assembly names, simple statistical reports of the assembly (number of contigs and scaffolds, contiguity metrics such as contig N50, total sequence length and total gap length) as well as the assembly update history. The Assembly database also tracks the relationship between an assembly submitted to the International Nucleotide Sequence Database Consortium (INSDC) and the assembly represented in the NCBI RefSeq project. Users can find assemblies of interest by querying the Assembly Resource directly or by browsing available assemblies for a particular organism. Links in the Assembly Resource allow users to easily download sequence and annotations for current versions of genome assemblies from the NCBI genomes FTP site.

The RefSeq project at the National Center for Biotechnology Information (NCBI) maintains and curates a publicly available database of annotated genomic, transcript, and protein sequence records (http://www.ncbi.nlm.nih.gov/refseq/). The RefSeq project leverages the data submitted to the International Nucleotide Sequence Database Collaboration (INSDC) against a combination of computation, manual curation, and collaboration to produce a standard set of stable, non-redundant reference sequences. The RefSeq project augments these reference sequences with current knowledge including publications, functional features and informative nomenclature. The database currently represents sequences from more than 55 000 organisms (>4800 viruses, >40 000 prokaryotes and >10 000 eukaryotes; RefSeq release 71), ranging from a single record to complete genomes. This paper summarizes the current status of the viral, prokaryotic, and eukaryotic branches of the RefSeq project, reports on improvements to data access and details efforts to further expand the taxonomic representation of the collection. We also highlight diverse functional curation initiatives that support multiple uses of RefSeq data including taxonomic validation, genome annotation, comparative genomics, and clinical testing. We summarize our approach to utilizing available RNA-Seq and other data types in our manual curation process for vertebrate, plant, and other species, and describe a new direction for prokaryotic genomes and protein name management.

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.

The number of viral genome sequences in the public databases is increasing dramatically, and these sequences are playing an important role in virus classification. Pairwise sequence comparison is a sequence-based virus classification method. A program using this method calculates the pairwise identities of virus sequences within a virus family and displays their distribution, and visual analysis helps to determine demarcations at different taxonomic levels such as strain, species, genus and subfamily. Subsequent comparison of new sequences against existing ones allows viruses from which the new sequences were derived to be classified. Although this method cannot be used as the only criterion for virus classification in some cases, it is a quantitative method and has many advantages over conventional virus classification methods. It has been applied to several virus families, and there is an increasing interest in using this method for other virus families/groups. The Pairwise Sequence Comparison (PASC) classification tool was created at the National Center for Biotechnology Information. The tool's database stores pairwise identities for complete genomes/segments of 56 virus families/groups. Data in the system are updated every day to reflect changes in virus taxonomy and additions of new virus sequences to the public database. The web interface of the tool ( http://www.ncbi.nlm.nih.gov/sutils/pasc/ ) makes it easy to navigate and perform analyses. Multiple new viral genome sequences can be tested simultaneously with this system to suggest the taxonomic position of virus isolates in a specific family. PASC eliminates potential discrepancies in the results caused by different algorithms and/or different data used by researchers.

Virus Variation (http://www.ncbi.nlm.nih.gov/genomes/VirusVariation/) is a comprehensive, web-based resource designed to support the retrieval and display of large virus sequence datasets. The resource includes a value added database, a specialized search interface and a suite of sequence data displays. Virus-specific sequence annotation and database loading pipelines produce consistent protein and gene annotation and capture sequence descriptors from sequence records then map these metadata to a controlled vocabulary. The database supports a metadata driven, web-based search interface where sequences can be selected using a variety of biological and clinical criteria. Retrieved sequences can then be downloaded in a variety of formats or analyzed using a suite of tools and displays. Over the past 2 years, the pre-existing influenza and Dengue virus resources have been combined into a single construct and West Nile virus added to the resultant resource. A number of improvements were incorporated into the sequence annotation and database loading pipelines, and the virus-specific search interfaces were updated to support more advanced functions. Several new features have also been added to the sequence download options, and a new multiple sequence alignment viewer has been incorporated into the resource tool set. Together these enhancements should support enhanced usability and the inclusion of new viruses in the future.

Background Microbial genomes at the National Center for Biotechnology Information (NCBI) represent a large collection of more than 35,000 assemblies. There are several complexities associated with the data: a great variation in sampling density since human pathogens are densely sampled while other bacteria are less represented; different protein families occur in annotations with different frequencies; and the quality of genome annotation varies greatly. In order to extract useful information from these sophisticated data, the analysis needs to be performed at multiple levels of phylogenomic resolution and protein similarity, with an adequate sampling strategy. Results Protein clustering is used to construct meaningful and stable groups of similar proteins to be used for analysis and functional annotation. Our approach is to create protein clusters at three levels. First, tight clusters in groups of closely-related genomes (species-level clades) are constructed using a combined approach that takes into account both sequence similarity and genome context. Second, clustroids of conservative in-clade clusters are organized into seed global clusters. Finally, global protein clusters are built around the the seed clusters. We propose filtering strategies that allow limiting the protein set included in global clustering. The in-clade clustering procedure, subsequent selection of clustroids and organization into seed global clusters provides a robust representation and high rate of compression. Seed protein clusters are further extended by adding related proteins. Extended seed clusters include a significant part of the data and represent all major known cell machinery. The remaining part, coming from either non-conservative (unique) or rapidly evolving proteins, from rare genomes, or resulting from low-quality annotation, does not group together well. Processing these proteins requires significant computational resources and results in a large number of questionable clusters. Conclusion The developed filtering strategies allow to identify and exclude such peripheral proteins limiting the protein dataset in global clustering. Overall, the proposed methodology allows the relevant data at different levels of details to be obtained and data redundancy eliminated while keeping biologically interesting variations. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1112-8) contains supplementary material, which is available to authorized users.

The National Center for Biotechnology Information (NCBI), as a primary public repository of genomic sequence data, collects and maintains enormous amounts of heterogeneous data. Data for genomes, genes, gene expressions, gene variation, gene families, proteins, and protein domains are integrated with the analytical, search, and retrieval resources through the NCBI website, text-based search and retrieval system, provides a fast and easy way to navigate across diverse biological databases.Comparative genome analysis tools lead to further understanding of evolution processes quickening the pace of discovery. Recent technological innovations have ignited an explosion in genome sequencing that has fundamentally changed our understanding of the biology of living organisms. This huge increase in DNA sequence data presents new challenges for the information management system and the visualization tools. New strategies have been designed to bring an order to this genome sequence shockwave and improve the usability of associated data.

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/

The National Center for Biotechnology Information (NCBI), as a primary public repository of genomic sequence data, collects and maintains enormous amounts of heterogeneous data. Data for genomes, genes, gene expressions, gene variation, gene families, proteins, and protein domains are integrated with the analytical, search, and retrieval resources through the NCBI website, text-based search and retrieval system, provides a fast and easy way to navigate across diverse biological databases.Comparative genome analysis tools lead to further understanding of evolution processes quickening the pace of discovery. Recent technological innovations have ignited an explosion in genome sequencing that has fundamentally changed our understanding of the biology of living organisms. This huge increase in DNA sequence data presents new challenges for the information management system and the visualization tools. New strategies have been designed to bring an order to this genome sequence shockwave and improve the usability of associated data.

Many genomes are incorrectly identified at GenBank. We developed a plan to find and correct misidentified genomes using genomic comparison statistics together with a scaffold of reliably identified genomes from type. A workshop was organized with broad representation from the bacterial taxonomic community to review the proposal, the GenBank Microbial Genomic Taxonomy Workshop, Bethesda MD, May 12-13, 2015.

PAirwise Sequence Comparison (PASC) is a tool that uses genome sequence similarity to help with virus classification. The PASC tool at NCBI uses two methods: local alignment based on BLAST and global alignment based on Needleman-Wunsch algorithm. It works for complete genomes of viruses of several families/groups, and for the family of Filoviridae, it currently includes 52 complete genomes available in GenBank. It has been shown that BLAST-based alignment approach works better for filoviruses, and therefore is recommended for establishing taxon demarcation criteria. When more genome sequences with high divergence become available, these demarcations will most likely become more precise. The tool can compare new genome sequences of filoviruses with the ones already in the database, and propose their taxonomic classification.

As the volume and complexity of data sets archived at NCBI grow rapidly, so does the need to gather and organize the associated metadata. Although metadata has been collected for some archival databases, previously, there was no centralized approach at NCBI for collecting this information and using it across databases. The BioProject database was recently established to facilitate organization and classification of project data submitted to NCBI, EBI and DDBJ databases. It captures descriptive information about research projects that result in high volume submissions to archival databases, ties together related data across multiple archives and serves as a central portal by which to inform users of data availability. Concomitantly, the BioSample database is being developed to capture descriptive information about the biological samples investigated in projects. BioProject and BioSample records link to corresponding data stored in archival repositories. Submissions are supported by a web-based Submission Portal that guides users through a series of forms for input of rich metadata describing their projects and samples. Together, these databases offer improved ways for users to query, locate, integrate and interpret the masses of data held in NCBI's archival repositories. The BioProject and BioSample databases are available at http://www.ncbi.nlm.nih.gov/bioproject and http://www.ncbi.nlm.nih.gov/biosample, respectively.

The National Center for Biotechnology Information (NCBI) Reference Sequence (RefSeq) database is a collection of genomic, transcript and protein sequence records. These records are selected and curated from public sequence archives and represent a significant reduction in redundancy compared to the volume of data archived by the International Nucleotide Sequence Database Collaboration. The database includes over 16,00 organisms, 2.4 × 0(6) genomic records, 13 × 10(6) proteins and 2 × 10(6) RNA records spanning prokaryotes, eukaryotes and viruses (RefSeq release 49, September 2011). The RefSeq database is maintained by a combined approach of automated analyses, collaboration and manual curation to generate an up-to-date representation of the sequence, its features, names and cross-links to related sources of information. We report here on recent growth, the status of curating the human RefSeq data set, more extensive feature annotation and current policy for eukaryotic genome annotation via the NCBI annotation pipeline. More information about the resource is available online (see http://www.ncbi.nlm.nih.gov/RefSeq/).

We describe a new program called cryptic splice finder (CSF) that can reliably identify cryptic splice sites (css), so providing a useful tool to help investigate splicing mutations in genetic disease. We report that many css are not entirely dormant and are often already active at low levels in normal genes prior to their enhancement in genetic disease. We also report a fascinating correlation between the positions of css and introns, whereby css within the exons of one species frequently match the exact position of introns in equivalent genes from another species. These results strongly indicate that many introns were inserted into css during evolution and they also imply that the splicing information that lies outside some introns can be independently recognized by the splicing machinery and was in place prior to intron insertion. This indicates that non-intronic splicing information had a key role in shaping the split structure of eukaryote genes.

The Genomic Standards Consortium (GSC) is an open-membership community that was founded in 2005 to work towards the development, implementation and harmonization of standards in the field of genomics. Starting with the defined task of establishing a minimal set of descriptions the GSC has evolved into an active standards-setting body that currently has 18 ongoing projects, with additional projects regularly proposed from within and outside the GSC. Here we describe our recently enacted policy for proposing new activities that are intended to be taken on by the GSC, along with the template for proposing such new activities. Copyright (C) retained by original authors

This report summarizes the proceedings of the 9th workshop of the Genomic Standards Consortium (GSC), held at the J. Craig Venter Institute, Rockville, MD, USA. It was the first GSC workshop to have open registration and attracted over 90 participants. This workshop featured sessions that provided overviews of the full range of ongoing GSC projects. It included sessions on Standards in Genomic Sciences, the open access journal of the GSC, building standards for genome annotation, the M5 platform for next-generation collaborative computational infrastructures, building ties with the biodiversity research community and two discussion panels with government and industry participants. Progress was made on all fronts, and major outcomes included the completion of the MIENS specification for publication and the formation of the Biodiversity working group.

Entrez Gene (www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene) is NCBI's database for gene-specific information. It does not include all known or predicted genes; instead Entrez Gene focuses on the 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 curation and automated integration of data from NCBI's Reference Sequence project (RefSeq), from collaborating model organism databases, and from many other databases available from NCBI. Records 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 updated as new information becomes available. Entrez Gene is a step forward from NCBI's LocusLink, with both a major increase in taxonomic scope and improved access through the many tools associated with NCBI Entrez.

NCBI's Reference Sequence (RefSeq) database (http://www.ncbi.nlm.nih.gov/RefSeq/) is a curated non-redundant collection of sequences representing genomes, transcripts and proteins. RefSeq records integrate information from multiple sources and represent a current description of the sequence, the gene and sequence features. The database includes over 5300 organisms spanning prokaryotes, eukaryotes and viruses, with records for more than 5.5 x 10(6) proteins (RefSeq release 30). Feature annotation is applied by a combination of curation, collaboration, propagation from other sources and computation. We report here on the recent growth of the database, recent changes to feature annotations and record types for eukaryotic (primarily vertebrate) species and policies regarding species inclusion and genome annotation. In addition, we introduce RefSeqGene, a new initiative to support reporting variation data on a stable genomic coordinate system.

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.

This meeting report summarizes the proceedings of the fifth Genomic Standards Consortium (GSC) workshop held December 12–14, 2007, at the European Bioinformatics Institute (EBI), Cambridge, UK. This fifth workshop served as a milestone event in the evolution of the GSC (launched in September 2005); the key outcome of the workshop was the finalization of a stable version of the MIGS specification (v2.0) for publication. This accomplishment enables, and also in some cases necessitates, downstream activities, which are described in the multiauthor, consensus-driven articles in this special issue of OMICS produced as a direct result of the workshop. This report briefly summarizes the workshop and overviews the special issue. In particular, it aims to explain how the various GSC-led projects are working together to help this community achieve its stated mission of further standardizing the descriptions of genomes and metagenomes and implementing improved mechanisms of data exchange and integration to enable more accurate comparative analyses. Further information about the GSC and its range of activities can be found at http://gensc.org

The methodologies used to generate genome and metagenome annotations are diverse and vary between groups and laboratories. Descriptions of the annotation process are helpful in interpreting genome annotation data. Some groups have produced Standard Operating Procedures (SOPs) that describe the annotation process, but standards are lacking for structure and content of these descriptions. In addition, there is no central repository to store and disseminate procedures and protocols for genome annotation. We highlight the importance of SOPs for genome annotation and endorse an online repository of SOPs.

The Rice Annotation Project Database (RAP-DB) was created to provide the genome sequence assembly of the International Rice Genome Sequencing Project (IRGSP), manually curated annotation of the sequence, and other genomics information that could be useful for comprehensive understanding of the rice biology. Since the last publication of the RAP-DB, the IRGSP genome has been revised and reassembled. In addition, a large number of rice-expressed sequence tags have been released, and functional genomics resources have been produced worldwide. Thus, we have thoroughly updated our genome annotation by manual curation of all the functional descriptions of rice genes. The latest version of the RAP-DB contains a variety of annotation data as follows: clone positions, structures and functions of 31 439 genes validated by cDNAs, RNA genes detected by massively parallel signature sequencing (MPSS) technology and sequence similarity, flanking sequences of mutant lines, transposable elements, etc. Other annotation data such as Gnomon can be displayed along with those of RAP for comparison. We have also developed a new keyword search system to allow the user to access useful information. The RAP-DB is available at: http://rapdb.dna.affrc.go.jp/ and http://rapdb.lab.nig.ac.jp/.

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.

Influenza viruses are remarkably adept at surviving in the human population over a long timescale. The human influenza A virus continues to thrive even among populations with widespread access to vaccines, and continues to be a major cause of morbidity and mortality. The virus mutates from year to year, making the existing vaccines ineffective on a regular basis, and requiring that new strains be chosen for a new vaccine. Less-frequent major changes, known as antigenic shift, create new strains against which the human population has little protective immunity, thereby causing worldwide pandemics. The most recent pandemics include the 1918 'Spanish' flu, one of the most deadly outbreaks in recorded history, which killed 30-50 million people worldwide, the 1957 'Asian' flu, and the 1968 'Hong Kong' flu. Motivated by the need for a better understanding of influenza evolution, we have developed flexible protocols that make it possible to apply large-scale sequencing techniques to the highly variable influenza genome. Here we report the results of sequencing 209 complete genomes of the human influenza A virus, encompassing a total of 2,821,103 nucleotides. In addition to increasing markedly the number of publicly available, complete influenza virus genomes, we have discovered several anomalies in these first 209 genomes that demonstrate the dynamic nature of influenza transmission and evolution. This new, large-scale sequencing effort promises to provide a more comprehensive picture of the evolution of influenza viruses and of their pattern of transmission through human and animal populations. All data from this project are being deposited, without delay, in public archives.

This meeting report summarizes the proceedings of the “eGenomics: Cataloguing our Complete Genome Collection III” workshop held September 11–13, 2006, at the National Institute for Environmental eScience (NIEeS), Cambridge, United Kingdom. This 3rd workshop of the Genomic Standards Consortium was divided into two parts. The first half of the three-day workshop was dedicated to reviewing the genomic diversity of our current and future genome and metagenome collection, and exploring linkages to a series of existing projects through formal presentations. The second half was dedicated to strategic discussions. Outcomes of the workshop include a revised “Minimum Information about a Genome Sequence” (MIGS) specification (v1.1), consensus on a variety of features to be added to the Genome Catalogue (GCat), agreement by several researchers to adopt MIGS for imminent genome publications, and an agreement by the EBI and NCBI to input their genome collections into GCat for the purpose of quantifying the amount of optional data already available (e.g., for geographic location coordinates) and working towards a single, global list of all public genomes and metagenomes.

The Genotyping tool at the National Center for Biotechnology Information is a web-based program that identifies the genotype (or subtype) of recombinant or non-recombinant viral nucleotide sequences. It works by using BLAST to compare a query sequence to a set of reference sequences for known genotypes. Predefined reference genotypes exist for three major viral pathogens: human immunodeficiency virus 1 (HIV-1), hepatitis C virus (HCV) and hepatitis B virus (HBV). User-defined reference sequences can be used at the same time. The query sequence is broken into segments for comparison to the reference so that the mosaic organization of recombinant sequences could be revealed. The results are displayed graphically using color-coded genotypes. Therefore, the genotype(s) of any portion of the query can quickly be determined. The Genotyping tool can be found at: http://www.ncbi.nih.gov/projects/genotyping/formpage.cgi.

The goal of the NCBI Reference Sequence (RefSeq) project is to provide the single best non-redundant and comprehensive collection of naturally occurring biological molecules, representing the central dogma. Nucleotide and protein sequences are explicitly linked on a residue-by-residue basis in this collection. Ideally all molecule types will be available for each well-studied organism, but the initial database collection pragmatically includes only those molecules and organisms that are most readily identified. Thus different amounts of information are available for different organisms at any given time. Furthermore, for some organisms additional intermediate records are provided when the genome sequence is not yet finished. The collection is supplied by NCBI through three distinct pipelines in addition to collaborations with community groups. The collection is curated on an ongoing basis. Additional information about the NCBI RefSeq project is available at http://www.ncbi.nih.gov/RefSeq/.

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.

The database of Clusters of Orthologous Groups of proteins (COGs), which represents an attempt on a phylogenetic classification of the proteins encoded in complete genomes, currently consists of 2791 COGs including 45 350 proteins from 30 genomes of bacteria, archaea and the yeast Saccharomyces cerevisiae (http://www.ncbi.nlm.nih.gov/COG). In addition, a supplement to the COGs is available, in which proteins encoded in the genomes of two multicellular eukaryotes, the nematode Caenorhabditis elegans and the fruit fly Drosophila melanogaster, and shared with bacteria and/or archaea were included. The new features added to the COG database include information pages with structural and functional details on each COG and literature references, improvements of the COGNITOR program that is used to fit new proteins into the COGs, and classification of genomes and COGs constructed by using principal component analysis.

The source of the microbial genomic sequences in the RefSeq collection is the set of primary sequence records submitted to the International Nucleotide Sequence Database public archives. These can be accessed through the Entrez search and retrieval system at http://www.ncbi.nlm.nih.gov/genome. Next-generation sequencing has enabled researchers to perform genomic sequencing at rates that were unimaginable in the past. Microbial genomes can now be sequenced in a matter of hours, which has led to a significant increase in the number of assembled genomes deposited in the public archives. This huge increase in DNA sequence data presents new challenges for the annotation, analysis and visualization bioinformatics tools. New strategies have been developed for the annotation and representation of reference genomes and sequence variations derived from population studies and clinical outbreaks.

The rapid growth of sequence data from genome sequencing projects has produced a huge set of new protein sequences. These sequences are generally derived by translation of ORFs in the genomic sequence, with no experimental evidence for expression or function. Functional characterization is most often obtained by comparison with sequences already in public databases. When some of these sequence neighbors have known 3-D structure, this information may further assist assignment of biological function. For example, a biologist may check whether the residues involved in specific ligand binding in the known structure are conserved in the newly sequenced protein. Further examination of structure neighbors, as detected by structure–structure comparison, may identify additional binding sites and/or ligands. Current efforts in structural genomics aim to provide either an experimental structure or detailed theoretical model for every protein from fully sequenced genomes (Kim 1998; Brenner et al. 1999; Burley et al. 1999; Eisenstein et al. 2000; Mallick et al. 2000; Shapiro and Harris 2000; Skolnick et al. 2000). While these projects are in progress, however, much can be learned by simply mapping residues of the newly sequenced protein onto the known structures of homologs. This analysis requires relatively straightforward computational tools, and it can be performed systematically and the results updated as additional genomes are sequenced and new structures are determined. What is needed is an efficient method for sequence comparison and molecular-graphics software for display of the corresponding sequence-structure alignments. The results may then be incorporated into a retrieval service accessible to genome researchers. Here we describe an annotation service of this kind, on the basis of links between the Genome database of Entrez (Tatusova et al. 1999) and its 3-D-structure Database, MMDB (Wang et al. 2000a). Significant sequence similarity between genome proteins and proteins with known structure is detected by use of the BLAST algorithm (Altschul et al. 1997) and recorded as a genome-to-structure link. When a user identifies a protein of interest and follows this link, the corresponding sequence-to-structure alignment is displayed by use of Cn3D (Wang et al. 2000b), a molecular-graphics viewer that operates on a PC, MacIntosh, and other popular computers. As an example, we examine a hypothetical protein from Aeropyrum pernix K1 (Kawarabayasi et al. 1999), showing how Entrez's link to 3-D structure strongly supports identification as a phosphoenolpyruvate carboxykinase (PCK).

MOTIVATION: The large amount of genome sequence data now publicly available can be accessed through the National Center for Biotechnology Information (NCBI) Entrez search and retrieval system, making it possible to explore data of a breadth and scope exceeding traditional flatfile views. RESULTS: Here we report recent improvements for completely sequenced genomes from viruses, bacteria, and yeast. Flexible web based views, precomputed relationships, and immediate access to analytical tools provide scientists with a portal into the new insights to be gained from completed genome sequences. AVAILABILITY: Entrez Genomes can be accessed on the World Wide Web at http://www.ncbi.nlm.nih.gov/Entrez/Genome/ org.html.

Bacterial genomes at NCBI represent a large collection of strains with different levels of sequence and assembly quality as well as sampling density. Among these, there are densely-sampled sets of related genomes, usually human pathogens, whose organization and protein content could be directly analyzed within the concept of pan-genome. Even in groups of close genomes, protein families appear with very different frequencies, with "core proteins" at one end and "dispensable proteins" at another and "accessory proteins" in between. In order to organize genomes available in the NCBI repositories in related groups (species-level clades), we use a distance method based on a robust distance between sets of ribosomal proteins. The threshold is selected to have one species per clade in most of the cases, with some clades containing genomes from a few species. Within each clade, we then build trees based on similarity of protein content using hierarchical clustering with tight parameters. In order to identify protein families for genomes within a clade accurately and reliably, we use a combined approach taking into account both sequence similarity and genome context: First, proteins are clustered in tentative clusters using inclusive parameters. Then, within each of tentative clusters, local genome context and protein phylogenetic tree are used to separate paralogs. The combined approach allows defining core and conservative clusters for the pan-genome more accurately than by sequence-based clustering. For computational efficiency, protein redundancy and near-redundancy is eliminated, with one representative sequence from each near-redundant group used.

The Energy Sciences Network (ESnet) is the primary provider of network connectivity for the U.S. Department of Energy (DOE) Office of Science (SC), the single largest supporter of basic research in the physical sciences in the United States. In support of SC programs, ESnet regularly updates and refreshes its understanding of the networking requirements of the instruments, facilities, scientists, and science programs that it serves. This focus has helped ESnet be a highly successful enabler of scientific discovery for over 25 years. In November 2012, ESnet and the Office of Biological and Environmental Research (BER) of the DOE SC organized a review to characterize the networking requirements of the programs funded by the BER program office. Several key findings resulted from the review. Among them: 1) The scale of data sets available to science collaborations continues to increase exponentially. This has broad impact, both on the network and on the computational and storage systems connected to the network. 2) Many science collaborations require assistance to cope with the systems and network engineering challenges inherent in managing the rapid growth in data scale. 3) Several science domains operate distributed facilities that rely on high-performance networking for success. Key examples illustrated in this report include the Earth System Grid Federation (ESGF) and the Systems Biology Knowledgebase (KBase). This report expands on these points, and addresses others as well. The report contains a findings section as well as the text of the case studies discussed at the review.

From the beginning of the microbial genome sequencing era, researchers have shown a commendable commitment to phylogenetic diversity. The completion of one genome from each prokaryotic division or phylum is still a frequently articulated community goal. However, largely because of the interest in human pathogens and advances in sequencing technologies, there are also now a number of very closely related genomes whose organization and gene content can be directly compared. Studying genetic variability of pathogenic bacteria using whole-genome sequencing provides a way to understanding the mechanism of bacterial adaptation to rapid environmental changes and can be a source of useful information on virulence mechanisms. The bacterial genome datasets available in public archives represent a large collection of genome at different levels of sequence quality and assembly. A fast and reliable method of phylogenetic classification based on genome sequences provides a necessary foundation for a more detailed comparative analysis. NCBI has developed an approach of grouping bacterial organisms into phylogenetic clades using a genome dissimilarity measure based on the comparison of universally conserved markers. Special adjustments have been made to compensate for data inaccuracy and incompleteness. Tests performed on complete and draft genomes from phylum Proteobacteria demonstrated that the proposed robust genomic distance allows stable and reliable species-level clustering and can be used for forming phylogenetic clades. Since the tradeoff for the increased robustness of the method is its limited sensitivity at a very fine level, a phylogenomic refinement could be done within each constructed clade when file-level phylogenetic resolution of close genomes is necessary.

A vast and rich body of information has grown up as a result of the world's enthusiasm for 'omics technologies. Finding ways to describe and make available this information that maximise its usefulness has become a major effort across the 'omics world. At the heart of this effort is the Genomic Standards Consortium (GSC), an open-membership organization that drives community-based standardization activities, Here we provide a short history of the GSC, provide an overview of its range of current activities, and make a call for the scientific community to join forces to improve the quality and quantity of contextual information about our public collections of genomes, metagenomes, and marker gene sequences.

The present article proposes the adoption of a community-defined, uniform, generic description of the core attributes of biological databases, BioDBCore. The goals of these attributes are to provide a general overview of the database landscape, to encourage consistency and interoperability between resources; and to promote the use of semantic and syntactic standards. BioDBCore will make it easier for users to evaluate the scope and relevance of available resources. This new resource will increase the collective impact of the information present in biological databases.

The National Center for Biotechnology Information (NCBI), as a primary public repository of genomic sequence data, collects and maintains enormous amounts of heterogeneous data. Data for genomes, genes, gene expressions, gene variation, gene families, proteins, and protein domains are integrated with the analytical, search, and retrieval resources through the NCBI Web site. Entrez, a text-based search and retrieval system, provides a fast and easy way to navigate across diverse biological databases.Customized genomic BLAST enables sequence similarity searches against a special collection of organism-specific sequence data and viewing the resulting alignments within a genomic context using NCBI's genome browser, Map Viewer.Comparative genome analysis tools lead to further understanding of evolutionary processes, quickening the pace of discovery.

The National Center for Biotechnology Information (NCBI), as a primary public repository of genomic sequence data, collects and maintains enormous amounts of heterogeneous data. Data for genomes, genes, gene expressions, gene variation, gene families, proteins, and protein domains are integrated with the analytical, search, and retrieval resources through the NCBI Web site. Entrez, a text-based search and retrieval system, provides a fast and easy way to navigate across diverse biological databases.Customized genomic BLAST enables sequence similarity searches against a special collection of organism-specific sequence data and viewing the resulting alignments within a genomic context using NCBI's genome browser, Map Viewer.Comparative genome analysis tools lead to further understanding of evolutionary processes, quickening the pace of discovery.