Research Article

A repetitive sequence assembler based on next-generation sequencing

Published: July 25, 2016
Genet. Mol. Res. 15(3): gmr8790 DOI: 10.4238/gmr.15038790

Abstract

Repetitive sequences of variable length are common in almost all eukaryotic genomes, and most of them are presumed to have important biomedical functions and can cause genomic instability. Next-generation sequencing (NGS) technologies provide the possibility of identifying capturing these repetitive sequences directly from the NGS data. In this study, we assessed the performances in identifying capturing repeats of leading assemblers, such as Velvet, SOAPdenovo, SGA, MSR-CA, Bambus2, ALLPATHS-LG, and AByss using three real NGS datasets. Our results indicated that most of them performed poorly in capturing the repeats. Consequently, we proposed a repetitive sequence assembler, named NGSReper, for capturing repeats from NGS data. Simulated datasets were used to validate the feasibility of NGSReper. The results indicate that the completeness of capturing repeat is up to 99%. Cross validation was performed in three real NGS datasets, and extensive comparisons indicate that NGSReper performed best in terms of completeness and accuracy in capturing repeats. In conclusion, NGSReper is an appropriate and suitable tool for capturing repeats directly from NGS data.

Repetitive sequences of variable length are common in almost all eukaryotic genomes, and most of them are presumed to have important biomedical functions and can cause genomic instability. Next-generation sequencing (NGS) technologies provide the possibility of identifying capturing these repetitive sequences directly from the NGS data. In this study, we assessed the performances in identifying capturing repeats of leading assemblers, such as Velvet, SOAPdenovo, SGA, MSR-CA, Bambus2, ALLPATHS-LG, and AByss using three real NGS datasets. Our results indicated that most of them performed poorly in capturing the repeats. Consequently, we proposed a repetitive sequence assembler, named NGSReper, for capturing repeats from NGS data. Simulated datasets were used to validate the feasibility of NGSReper. The results indicate that the completeness of capturing repeat is up to 99%. Cross validation was performed in three real NGS datasets, and extensive comparisons indicate that NGSReper performed best in terms of completeness and accuracy in capturing repeats. In conclusion, NGSReper is an appropriate and suitable tool for capturing repeats directly from NGS data.

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