J. Comput. Biol. - Detection of structural variants involving repetitive regions in the reference genome.

Tópicos

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Resumo

Next-generation sequencing techniques are now commonly used to characterize structural variations (SVs) in population genomics and elucidate their associations with phenotypes. Many of the computational tools developed for detecting structural variations work by mapping paired-end reads to a reference genome and identifying the discordant read-pairs whose mapped loci in the reference genome deviate from the expected insert size and orientation. However, repetitive regions in the reference genome represent a major challenge in SV detection, because the paired-end reads from these regions may be mapped to multiple loci in the reference genome, resulting in spuriously discordant read-pairs. To address this issue, we have developed an algorithmic approach for read mapping and SV detection based on the framework of A-Bruijn graphs. Instead of mapping reads to a linear sequence of the reference genome, we propose to map reads onto the A-Bruijn graph constructed from the reference genome in which all instances of the same repeat are collapsed into a single edge. As a result, any given read, either from repetitive regions or not, will be mapped to a unique location in the A-Bruijn graph, and each discordant read-pair in the A-Bruijn graph indicates a potentially true SV event. We also developed a simple clustering algorithm to derive valid clusters of these discordant read-pairs, each supporting a different SV event. Finally, we demonstrate the performance of this approach, compared to existing approaches, by identifying transposition events of insertion sequence (IS) elements, a class of simple mobile genetic elements (MGEs), in E. coli by using simulated and real paired-end sequence data acquired from E. coli mutation accumulation lines.

Resumo Limpo

nextgener sequenc techniqu now common use character structur variat svs popul genom elucid associ phenotyp mani comput tool develop detect structur variat work map pairedend read refer genom identifi discord readpair whose map loci refer genom deviat expect insert size orient howev repetit region refer genom repres major challeng sv detect pairedend read region may map multipl loci refer genom result spurious discord readpair address issu develop algorithm approach read map sv detect base framework abruijn graph instead map read linear sequenc refer genom propos map read onto abruijn graph construct refer genom instanc repeat collaps singl edg result given read either repetit region will map uniqu locat abruijn graph discord readpair abruijn graph indic potenti true sv event also develop simpl cluster algorithm deriv valid cluster discord readpair support differ sv event final demonstr perform approach compar exist approach identifi transposit event insert sequenc element class simpl mobil genet element mges e coli use simul real pairedend sequenc data acquir e coli mutat accumul line

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