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Benchmarking of computational methods for m6A profiling with Nanopore direct RNA sequencing

标杆管理 工作流程 计算机科学 纳米孔测序 管道(软件) 精确性和召回率 仿形(计算机编程) 计算生物学 软件 数据挖掘 人工智能 机器学习 生物 基因 DNA测序 遗传学 数据库 营销 业务 操作系统 程序设计语言
作者
Simone Maestri,Mattia Furlan,Logan Mulroney,Lucia Coscujuela Tarrero,Camilla Ugolini,Fabio Dalla Pozza,Tommaso Leonardi,Ewan Birney,Francesco Nicassio,Mattia Pelizzola
出处
期刊:Briefings in Bioinformatics [Oxford University Press]
卷期号:25 (2)
标识
DOI:10.1093/bib/bbae001
摘要

Abstract N6-methyladenosine (m6A) is the most abundant internal eukaryotic mRNA modification, and is involved in the regulation of various biological processes. Direct Nanopore sequencing of native RNA (dRNA-seq) emerged as a leading approach for its identification. Several software were published for m6A detection and there is a strong need for independent studies benchmarking their performance on data from different species, and against various reference datasets. Moreover, a computational workflow is needed to streamline the execution of tools whose installation and execution remains complicated. We developed NanOlympicsMod, a Nextflow pipeline exploiting containerized technology for comparing 14 tools for m6A detection on dRNA-seq data. NanOlympicsMod was tested on dRNA-seq data generated from in vitro (un)modified synthetic oligos. The m6A hits returned by each tool were compared to the m6A position known by design of the oligos. In addition, NanOlympicsMod was used on dRNA-seq datasets from wild-type and m6A-depleted yeast, mouse and human, and each tool’s hits were compared to reference m6A sets generated by leading orthogonal methods. The performance of the tools markedly differed across datasets, and methods adopting different approaches showed different preferences in terms of precision and recall. Changing the stringency cut-offs allowed for tuning the precision-recall trade-off towards user preferences. Finally, we determined that precision and recall of tools are markedly influenced by sequencing depth, and that additional sequencing would likely reveal additional m6A sites. Thanks to the possibility of including novel tools, NanOlympicsMod will streamline the benchmarking of m6A detection tools on dRNA-seq data, improving future RNA modification characterization.

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