m<sup>6</sup>A peak calling accounting for varying sequencing bias across regions and samples

N<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup>-Methyladenosine (m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup>A) is the most abundant type of mRNA methylation and is most widely measured by methylated RNA immunoprecipitation sequencing (MeRIP-seq). In MeRIP-seq, an immunoprecipitation (IP) sample and a pairing control (input) sample are sequenced for each biological sample. Methylated regions are identified as peaks showing increased counts in the IP sample versus the input. We report that technical bias in sequencing can vary substantially in the IP and input samples depending on the local sequence context. Current sequencing depth-based normalization does not appropriately account for the varying technical bias along the transcriptome and leads to inaccurate identification of m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup>A regions. We describe a method to estimate a local size factor that reflects the RNA sequence context and show that peak calling using these region-specific size factors identifies more accurate peak regions.

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Published
2024-12-03
DOI
https://doi.org/10.1109/bibm62325.2024.10822578
Primary Topic
RNA modifications and cancer
Type
article
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article

m6A peak calling accounting for varying sequencing bias across regions and samples

Lanyu Zhang, Zhaohui Qin, Zhijin Wu, Zhenxing Guo
RNA modifications and cancer
article

m6A peak calling accounting for varying sequencing bias across regions and samples

Lanyu Zhang, Zhaohui Qin, Zhijin Wu, Zhenxing Guo
article en

Abstract

N<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup>-Methyladenosine (m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup>A) is the most abundant type of mRNA methylation and is most widely measured by methylated RNA immunoprecipitation sequencing (MeRIP-seq). In MeRIP-seq, an immunoprecipitation (IP) sample and a pairing control (input) sample are sequenced for each biological sample. Methylated regions are identified as peaks showing increased counts in the IP sample versus the input. We report that technical bias in sequencing can vary substantially in the IP and input samples depending on the local sequence context. Current sequencing depth-based normalization does not appropriately account for the varying technical bias along the transcriptome and leads to inaccurate identification of m<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup>A regions. We describe a method to estimate a local size factor that reflects the RNA sequence context and show that peak calling using these region-specific size factors identifies more accurate peak regions.

Emory University (US), Brown University (US), Chinese University of Hong Kong, Shenzhen (CN)
Zero hunger
Openalex Percentile: Top 78%
RNA modifications and cancer
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m<sup>6</sup>A peak calling accounting for varying sequencing bias across regions and samples — Lanyu Zhang, Zhaohui Qin, et al. · (2024) | TGRS Research Map | TGRS