HUSCAP logo Hokkaido Univ. logo

Hokkaido University Collection of Scholarly and Academic Papers >
Research Center for Zoonosis Control >
Peer-reviewed Journal Articles, etc >

ELM: enhanced lowest common ancestor based method for detecting a pathogenic virus from a large sequence dataset

This item is licensed under: Creative Commons Attribution 2.0 Generic

Files in This Item:
1471-2105-15-254.pdf1.17 MBPDFView/Open
Please use this identifier to cite or link to this item:

Title: ELM: enhanced lowest common ancestor based method for detecting a pathogenic virus from a large sequence dataset
Authors: Ueno, Keisuke Browse this author →KAKEN DB
Ishii, Akihiro Browse this author →KAKEN DB
Ito, Kimihito Browse this author →KAKEN DB
Keywords: Next generation sequencing
Virus discovery
Diagnostic virology
Taxonomic identification
Issue Date: 28-Jul-2014
Publisher: Biomed Central
Journal Title: BMC Bioinformatics
Volume: 15
Start Page: 254
Publisher DOI: 10.1186/1471-2105-15-254
Abstract: Background: Emerging viral diseases, most of which are caused by the transmission of viruses from animals to humans, pose a threat to public health. Discovering pathogenic viruses through surveillance is the key to preparedness for this potential threat. Next generation sequencing (NGS) helps us to identify viruses without the design of a specific PCR primer. The major task in NGS data analysis is taxonomic identification for vast numbers of sequences. However, taxonomic identification via a BLAST search against all the known sequences is a computational bottleneck. Description: Here we propose an enhanced lowest-common-ancestor based method (ELM) to effectively identify viruses from massive sequence data. To reduce the computational cost, ELM uses a customized database composed only of viral sequences for the BLAST search. At the same time, ELM adopts a novel criterion to suppress the rise in false positive assignments caused by the small database. As a result, identification by ELM is more than 1,000 times faster than the conventional methods without loss of accuracy. Conclusions: We anticipate that ELM will contribute to direct diagnosis of viral infections.
Type: article
Appears in Collections:人獣共通感染症リサーチセンター (Research Center for Zoonosis Control) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 伊藤 公人

Export metadata:

OAI-PMH ( junii2 , jpcoar )

MathJax is now OFF:


 - Hokkaido University