TY - GEN
T1 - SVEM
T2 - 1st International Conference on Algorithms for Computational Biology, AlCoB 2014
AU - Ohtsuki, Tomohiko
AU - Nariai, Naoki
AU - Kojima, Kaname
AU - Mimori, Takahiro
AU - Sato, Yukuto
AU - Kawai, Yosuke
AU - Yamaguchi-Kabata, Yumi
AU - Shibuya, Testuo
AU - Nagasaki, Masao
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84903978328&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84903978328&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-07953-0_17
DO - 10.1007/978-3-319-07953-0_17
M3 - Conference contribution
AN - SCOPUS:84903978328
SN - 9783319079523
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 208
EP - 219
BT - Algorithms for Computational Biology - First International Conference, AlCoB 2014, Proceedings
PB - Springer Verlag
Y2 - 1 July 2014 through 3 July 2014
ER -