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NCBI viral genomes resource.

ABSTRACT: 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.

SUBMITTER: Brister JR

PROVIDER: S-EPMC4383986 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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NCBI viral genomes resource.

Brister J Rodney JR Ako-Adjei Danso D Bao Yiming Y Blinkova Olga O

Nucleic acids research 20141126 Database issue

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 ...[more]

PMID: 25428358

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Project description:Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information, however, the development of powerful, highly effective tools to automatically detect central biomedical concepts such as diseases is conditional on the availability of annotated corpora. This paper presents the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community. Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH®) or Online Mendelian Inheritance in Man (OMIM®). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. In this setting, a high inter-annotator agreement was observed. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency. The public release of the NCBI disease corpus contains 6892 disease mentions, which are mapped to 790 unique disease concepts. Of these, 88% link to a MeSH identifier, while the rest contain an OMIM identifier. We were able to link 91% of the mentions to a single disease concept, while the rest are described as a combination of concepts. In order to help researchers use the corpus to design and test disease identification methods, we have prepared the corpus as training, testing and development sets. To demonstrate its utility, we conducted a benchmarking experiment where we compared three different knowledge-based disease normalization methods with a best performance in F-measure of 63.7%. These results show that the NCBI disease corpus has the potential to significantly improve the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks. The NCBI disease corpus, guidelines and other associated resources are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Dogan/DISEASE/.

Dataset Information

NCBI viral genomes resource.

Publications

NCBI viral genomes resource.

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