InvBFM: finding genomic inversions from high-throughput sequence data based on feature mining

BMC Genomics
Zhongjia WuJingyang Gao

Abstract

Genomic inversion is one type of structural variations (SVs) and is known to play an important biological role. An established problem in sequence data analysis is calling inversions from high-throughput sequence data. It is more difficult to detect inversions because they are surrounded by duplication or other types of SVs in the inversion areas. Existing inversion detection tools are mainly based on three approaches: paired-end reads, split-mapped reads, and assembly. However, existing tools suffer from unsatisfying precision or sensitivity (eg: only 50~60% sensitivity) and it needs to be improved. In this paper, we present a new inversion calling method called InvBFM. InvBFM calls inversions based on feature mining. InvBFM first gathers the results of existing inversion detection tools as candidates for inversions. It then extracts features from the inversions. Finally, it calls the true inversions by a trained support vector machine (SVM) classifier. Our results on real sequence data from the 1000 Genomes Project show that by combining feature mining and a machine learning model, InvBFM outperforms existing tools. InvBFM is written in Python and Shell and is available for download at https://github.com/wzj1234/InvBFM.

References

Apr 21, 2012·Briefings in Bioinformatics·Helga ThorvaldsdóttirJill P Mesirov
Jun 28, 2014·Genome Biology·Ryan M LayerIra M Hall
May 23, 2015·Briefings in Functional Genomics·Marta PuigMario Cáceres
Oct 4, 2015·Nature·UNKNOWN 1000 Genomes Project ConsortiumGonçalo R Abecasis
Jan 17, 2016·BMC Genomics·Hemang ParikhMarc Salit
May 27, 2016·Nigerian Medical Journal : Journal of the Nigeria Medical Association·Arun MuthuvelChandralekha Subbian

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Methods Mentioned

BETA
PCA

Software Mentioned

InvBFM
Pindel
LumpyEP
shold Tool
Thre
Lumpy express tool
Lumpy
vcf
Delly
pysam

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