BMC GenomicsBioinformaticsPeer-reviewed journal article
Discovery of large genomic inversions using pooled clone sequencing
Marzieh Eslami RasekhiD, Giorgia Chiatante, Mattia Miroballo, Joyce Tang, Mario VenturaiD, Chris T Amemiya, Evan E. EichleriD, Francesca AntonacciiD, Can AlkaniD
Abstract
MotivationThere are many different forms of genomic structural variation that can be broadly classified into two groups as copy number variation (CNV) and balanced rearrangements. Although many algorithms are now available in the literature that aim to characterize CNVs, discovery of balanced rearrangements (inversions and translocations) remains an open problem. This is mainly because the breakpoints of such events typically lie within segmental duplications and common repeats, which reduce the mappability of short reads. The 1000 Genomes Project spearheaded the development of several methods to identify inversions, however, they are limited to relatively short inversions, and there are currently no available algorithms to discover large inversions using high throughput sequencing technologies (HTS).\n\nResultsHere we propose to use a sequencing method (Kitzman et al., 2011) originally developed to improve haplotype phasing to characterize large genomic inversions. This method, called pooled clone sequencing, merges the advantages of clone based sequencing approaches with the speed and cost efficiency of HTS technologies. Using data generated with pooled clone sequencing method, we developed a novel algorithm, dipSeq, to discover large inversions (>500 Kbp). We show the power of dipSeq first on simulated data, and then apply it to the genome of a HapMap individual (NA12878). We were able to accurately discover all previously known and experimentally validated large inversions in the same genome. We also identified a novel inversion, and confirmed using fluorescent in situ hybridization.\n\nAvailabilityImplementation of the dipSeq algorithm is available at https://github.com/BilkentCompGen/dipseq\n\nContactcalkan@cs.bilkent.edu.tr, francesca.antonacci@uniba.it
- organism: human
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: variant effect prediction
- approach: deep learning
- approach: statistical model
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