July 19, 2019  |  

AgIn: Measuring the landscape of CpG methylation of individual repetitive elements.

Authors: Suzuki, Yuta and Korlach, Jonas and Turner, Stephen W and Tsukahara, Tatsuya and Taniguchi, Junko and Qu, Wei and Ichikawa, Kazuki and Yoshimura, Jun and Yurino, Hideaki and Takahashi, Yuji and Mitsui, Jun and Ishiura, Hiroyuki and Tsuji, Shoji and Takeda, Hiroyuki and Morishita, Shinichi

Determining the methylation state of regions with high copy numbers is challenging for second-generation sequencing, because the read length is insufficient to map reads uniquely, especially when repetitive regions are long and nearly identical to each other. Single-molecule real-time (SMRT) sequencing is a promising method for observing such regions, because it is not vulnerable to GC bias, it produces long read lengths, and its kinetic information is sensitive to DNA modifications.We propose a novel linear-time algorithm that combines the kinetic information for neighboring CpG sites and increases the confidence in identifying the methylation states of those sites. Using a practical read coverage of ~30-fold from an inbred strain medaka (Oryzias latipes), we observed that both the sensitivity and precision of our method on individual CpG sites were ~93.7%. We also observed a high correlation coefficient (R?=?0.884) between our method and bisulfite sequencing, and for 92.0% of CpG sites, methylation levels ranging over [0, 1] were in concordance within an acceptable difference 0.25. Using this method, we characterized the landscape of the methylation status of repetitive elements, such as LINEs, in the human genome, thereby revealing the strong correlation between CpG density and hypomethylation and detecting hypomethylation hot spots of LTRs and LINEs. We uncovered the methylation states for nearly identical active transposons, two novel LINE insertions of identity ~99% and length 6050 base pairs (bp) in the human genome, and 16 Tol2 elements of identity >99.8% and length 4682?bp in the medaka genome.AgIn (Aggregate on Intervals) is available at: https://github.com/hacone/AgIn CONTACT: [email protected], [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. © The Author(s) 2016. Published by Oxford University Press.

Journal: Bioinformatics
DOI: 10.1093/bioinformatics/btw360
Year: 2016

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