HUSCAP logo Hokkaido Univ. logo

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

Resampling Nucleotide Sequences with Closest-Neighbor Trimming and Its Comparison to Other Methods

This item is licensed under:Creative Commons Attribution 3.0 Unported

Files in This Item:
journal.pone.0057684.pdf646.93 kBPDFView/Open
Please use this identifier to cite or link to this item:

Title: Resampling Nucleotide Sequences with Closest-Neighbor Trimming and Its Comparison to Other Methods
Authors: Yonezawa, Kouki Browse this author
Igarashi, Manabu Browse this author →KAKEN DB
Ueno, Keisuke Browse this author →KAKEN DB
Takada, Ayato Browse this author →KAKEN DB
Ito, Kimihito Browse this author →KAKEN DB
Issue Date: 27-Feb-2013
Publisher: Public Library Science
Journal Title: PLoS One
Volume: 8
Issue: 2
Start Page: e57684
Publisher DOI: 10.1371/journal.pone.0057684
Abstract: A large number of nucleotide sequences of various pathogens are available in public databases. The growth of the datasets has resulted in an enormous increase in computational costs. Moreover, due to differences in surveillance activities, the number of sequences found in databases varies from one country to another and from year to year. Therefore, it is important to study resampling methods to reduce the sampling bias. A novel algorithm-called the closest-neighbor trimming method-that resamples a given number of sequences from a large nucleotide sequence dataset was proposed. The performance of the proposed algorithm was compared with other algorithms by using the nucleotide sequences of human H3N2 influenza viruses. We compared the closest-neighbor trimming method with the naive hierarchical clustering algorithm and k-medoids clustering algorithm. Genetic information accumulated in public databases contains sampling bias. The closest-neighbor trimming method can thin out densely sampled sequences from a given dataset. Since nucleotide sequences are among the most widely used materials for life sciences, we anticipate that our algorithm to various datasets will result in reducing sampling bias.
Type: article
Appears in Collections:人獣共通感染症国際共同研究所 (International Institute 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_1.0 )

MathJax is now OFF:


 - Hokkaido University