Effects of GC Bias in Next-Generation-Sequencing Data on De Novo Genome Assembly

Yen-Chun Chen, Tsunglin Liu, Chun-Hui Yu, Tzen-Yuh Chiang and Chi-Chuan Hwang

PLOS ONE, 2013, vol. 8, issue 4, 1-20

Abstract: Next-generation-sequencing (NGS) has revolutionized the field of genome assembly because of its much higher data throughput and much lower cost compared with traditional Sanger sequencing. However, NGS poses new computational challenges to de novo genome assembly. Among the challenges, GC bias in NGS data is known to aggravate genome assembly. However, it is not clear to what extent GC bias affects genome assembly in general. In this work, we conduct a systematic analysis on the effects of GC bias on genome assembly. Our analyses reveal that GC bias only lowers assembly completeness when the degree of GC bias is above a threshold. At a strong GC bias, the assembly fragmentation due to GC bias can be explained by the low coverage of reads in the GC-poor or GC-rich regions of a genome. This effect is observed for all the assemblers under study. Increasing the total amount of NGS data thus rescues the assembly fragmentation because of GC bias. However, the amount of data needed for a full rescue depends on the distribution of GC contents. Both low and high coverage depths due to GC bias lower the accuracy of assembly. These pieces of information provide guidance toward a better de novo genome assembly in the presence of GC bias.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0062856

DOI: 10.1371/journal.pone.0062856

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