Microbial virus genome annotation—Mustering the troops to fight the sequence onslaught

Brister, J. Rodney; Mercier, Philippe Le; Hu, James C.

doi:10.1016/j.virol.2012.09.027

Cited by 12 publications

(9 citation statements)

References 22 publications

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“…Despite the presence of many thousands of completely sequenced virus genomes in NCBI sequence databases, the vast majority of viruses still remain to be characterized (3). Even so, the large number of sequences available creates considerable difficulties in navigating through all the data in search of biological meaning (4). The Prokaryotic Virus Orthologous Groups (pVOGs, formerly Phage Orthologous Groups, POGs (5–7)) resource aids researchers by providing clusters of orthologous genes in complete genomes of viruses that infect bacteria or archaea, using the microbial COG framework (8,9)—one of the oldest, most accurate, and most-often-used methods to computationally identify orthologs (10).…”

Section: Introductionmentioning

confidence: 99%

Prokaryotic Virus Orthologous Groups (pVOGs): a resource for comparative genomics and protein family annotation

2016

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Viruses are the most abundant and diverse biological entities on earth, and while most of this diversity remains completely unexplored, advances in genome sequencing have provided unprecedented glimpses into the virosphere. The Prokaryotic Virus Orthologous Groups (pVOGs, formerly called Phage Orthologous Groups, POGs) resource has aided in this task over the past decade by using automated methods to keep pace with the rapid increase in genomic data. The uses of pVOGs include functional annotation of viral proteins, identification of genes and viruses in uncharacterized DNA samples, phylogenetic analysis, large-scale comparative genomics projects, and more. The pVOGs database represents a comprehensive set of orthologous gene families shared across multiple complete genomes of viruses that infect bacterial or archaeal hosts (viruses of eukaryotes will be added at a future date). The pVOGs are constructed within the Clusters of Orthologous Groups (COGs) framework that is widely used for orthology identification in prokaryotes. Since the previous release of the POGs, the size has tripled to nearly 3000 genomes and 300 000 proteins, and the number of conserved orthologous groups doubled to 9518. User-friendly webpages are available, including multiple sequence alignments and HMM profiles for each VOG. These changes provide major improvements to the pVOGs database, at a time of rapid advances in virus genomics. The pVOGs database is hosted jointly at the University of Iowa at http://dmk-brain.ecn.uiowa.edu/pVOGs and the NCBI at ftp://ftp.ncbi.nlm.nih.gov/pub/kristensen/pVOGs/home.html.

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Section: Introductionmentioning

confidence: 99%

Prokaryotic Virus Orthologous Groups (pVOGs): a resource for comparative genomics and protein family annotation

2016

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“…In some cases, sequence homologies allow the transfer of annotation from experimentally defined to poorly characterized genomes ( 11 – 13 ). Yet, often genomes are annotated by purely ab initio processes ( 27 – 29 ). Given the difficulty of implementing a purely well annotated representation of viral genome sequences, the viral RefSeq model has evolved into a more flexible approach that includes both reference and representative sequences.…”

Section: Adapting the Refseq Data Model To Virusesmentioning

confidence: 99%

“…Yet, biology and taxonomic criteria vary among viral species, and the one RefSeq per species model does not always sufficiently capture important sequence variants. This phenomenon is underscored in viral systems that undergo horizontal gene transfer where the genetic diversity within an otherwise closely related group of viruses cannot be captured with a single reference genome ( 29 – 30 ). Moreover, some viral communities are developing well defined subspecies classification such as the genotyping schemes for hepatitis B virus and hepatitis C virus ( 31 – 33 ).…”

Section: Adapting the Refseq Data Model To Virusesmentioning

confidence: 99%

NCBI Viral Genomes Resource

Brister

Ako-adjei

Bào

et al. 2014

Nucleic Acids Research

550

539

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Recent technological innovations have ignited an explosion in virus genome sequencing that promises to fundamentally alter our understanding of viral biology and profoundly impact public health policy. Yet, any potential benefits from the billowing cloud of next generation sequence data hinge upon well implemented reference resources that facilitate the identification of sequences, aid in the assembly of sequence reads and provide reference annotation sources. The NCBI Viral Genomes Resource is a reference resource designed to bring order to this sequence shockwave and improve usability of viral sequence data. The resource can be accessed at http://www.ncbi.nlm.nih.gov/genome/viruses/ and catalogs all publicly available virus genome sequences and curates reference genome sequences. As the number of genome sequences has grown, so too have the difficulties in annotating and maintaining reference sequences. The rapid expansion of the viral sequence universe has forced a recalibration of the data model to better provide extant sequence representation and enhanced reference sequence products to serve the needs of the various viral communities. This, in turn, has placed increased emphasis on leveraging the knowledge of individual scientific communities to identify important viral sequences and develop well annotated reference virus genome sets.

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“…In biomedical research, this strategy can be divided into two principal types: microtasks and megatasks [19]. Microtasks are useful to achieve many simple tasks that together produce a quality resource, for example, genome annotation [20, 21], drug indication curation [22], extraction of gene expression signatures [23], and human gene-disease annotation [24], as well as many other examples in recent years [25]. Megatasks address more challenging problems and are set as a competition between teams or individual experts, for example, the reconstruction of the topology of biological networks, or the imputation of missing data by the development of novel algorithms [26].…”

Section: Educational and Other Efforts To Involve The Communitymentioning

confidence: 99%

Developing a framework for digital objects in the Big Data to Knowledge (BD2K) commons: Report from the Commons Framework Pilots workshop

Jagodnik

Koplev

Jenkins

et al. 2017

Journal of Biomedical Informatics

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The volume and diversity of data in biomedical research has been rapidly increasing in recent years. While such data hold significant promise for accelerating discovery, their use entails many challenges including: the need for adequate computational infrastructure, secure processes for data sharing and access, tools that allow researchers to find and integrate diverse datasets, and standardized methods of analysis. These are just some elements of a complex ecosystem that needs to be built to support the rapid accumulation of these data. The NIH Big Data to Knowledge (BD2K) initiative aims to facilitate digitally enabled biomedical research. Within the BD2K framework, the Commons initiative is intended to establish a virtual environment that will facilitate the use, interoperability, and discoverability of shared digital objects used for research. The BD2K Commons Framework Pilots Working Group (CFPWG) was established to clarify goals and work on pilot projects that would address existing gaps toward realizing the vision of the BD2K Commons. This report reviews highlights from a two-day meeting involving the BD2K CFPWG to provide insights on trends and considerations in advancing Big Data science for biomedical research in the United States.

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Microbial virus genome annotation—Mustering the troops to fight the sequence onslaught

Cited by 12 publications

References 22 publications

Prokaryotic Virus Orthologous Groups (pVOGs): a resource for comparative genomics and protein family annotation

Prokaryotic Virus Orthologous Groups (pVOGs): a resource for comparative genomics and protein family annotation

NCBI Viral Genomes Resource

Developing a framework for digital objects in the Big Data to Knowledge (BD2K) commons: Report from the Commons Framework Pilots workshop

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