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	SVmine improves structural variation detection by integrative mining of predictions from multiple algorithms
	Xia, Yuchao ; Liu, Yun ; Deng, Minghua ; Xi, Ruibin
	2017
关键词	COPY NUMBER VARIATION PAIRED-END HUMAN-DISEASE GENOME REARRANGEMENTS MECHANISMS SIMULATION ALIGNMENT VARIANTS IDENTIFY
英文摘要	Motivation: Structural variation (SV) is an important class of genomic variations in human genomes. A number of SV detection algorithms based on high-throughput sequencing data have been developed, but they have various and often limited level of sensitivity, specificity and breakpoint resolution. Furthermore, since overlaps between predictions of algorithms are low, SV detection based on multiple algorithms, an often-used strategy in real applications, has little effect in improving the performance of SV detection. Results: We develop a computational tool called SVmine for further mining of SV predictions from multiple tools to improve the performance of SV detection. SVmine refines SV predictions by performing local realignment and assess quality of SV predictions based on likelihoods of the realignments. The local realignment is performed against a set of sequences constructed from the reference sequence near the candidate SV by incorporating nearby single nucleotide variations, insertions and deletions. A sandwich alignment algorithm is further used to improve the accuracy of breakpoint positions. We evaluate SVmine on a set of simulated data and real data and find that SVmine has superior sensitivity, specificity and breakpoint estimation accuracy. We also find that SVmine can significantly improve overlaps of SV predictions from other algorithms.; National Key Basic Research Project of China [2015CB856000]; National Natural Science Foundation of China [11471022, 71532001]; Recruitment Program of Global Youth Experts of China; SCI(E); ARTICLE; 21; 3348-3354; 33
语种	英语
出处	SCI
出版者	BIOINFORMATICS
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/484548]
专题	数学科学学院 生命科学学院
推荐引用方式 GB/T 7714	Xia, Yuchao,Liu, Yun,Deng, Minghua,et al. SVmine improves structural variation detection by integrative mining of predictions from multiple algorithms. 2017-01-01.

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