SVEM: A structural variant estimation method using multi-mapped reads on breakpoints

Tomohiko Ohtsuki, Naoki Nariai, Kaname Kojima, Takahiro Mimori, Yukuto Sato, Yosuke Kawai, Yumi Yamaguchi-Kabata, Testuo Shibuya, Masao Nagasaki

研究成果: Conference contribution

抄録

Recent development of next generation sequencing (NGS) technologies has led to the identification of structural variants (SVs) of genomic DNA existing in the human population. Several SV detection methods utilizing NGS data have been proposed. However, there are several difficulties in analysis of NGS data, particularly with regard to handling reads from duplicated loci or low-complexity sequences of the human genome. In this paper, we propose SVEM, a novel statistical method to detect SVs with a single nucleotide resolution that can utilize multi-mapped reads on breakpoints. SVEM estimates the amount of reads on breakpoints as parameters and mapping states as latent variables using the expectation maximization algorithm. This framework enables us to handle ambiguous mapping of reads without discarding information for SV detection. SVEM is applied to simulation data and real data, and it achieves better performance than existing methods in terms of precision and recall.

本文言語English
ホスト出版物のタイトルAlgorithms for Computational Biology - First International Conference, AlCoB 2014, Proceedings
出版社Springer Verlag
ページ208-219
ページ数12
ISBN(印刷版)9783319079523
DOI
出版ステータスPublished - 2014
イベント1st International Conference on Algorithms for Computational Biology, AlCoB 2014 - Tarragona, Spain
継続期間: 2014 7 12014 7 3

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
8542 LNBI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other1st International Conference on Algorithms for Computational Biology, AlCoB 2014
国/地域Spain
CityTarragona
Period14/7/114/7/3

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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