RT Journal Article
SR Electronic
T1 Parallel and scalable workflow for the analysis of Oxford Nanopore direct RNA sequencing datasets
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 818336
DO 10.1101/818336
A1 Luca Cozzuto
A1 Huanle Liu
A1 Leszek P. Pryszcz
A1 Toni Hermoso Pulido
A1 Julia Ponomarenko
A1 Eva Maria Novoa
YR 2019
UL http://biorxiv.org/content/early/2019/10/28/818336.abstract
AB The direct RNA sequencing platform offered by Oxford Nanopore Technologies allows for direct measurement of RNA molecules without the need of conversion to complementary DNA, fragmentation or amplification. As such, it is virtually capable of detecting any given RNA modification present in the molecule that is being sequenced, as well as provide polyA tail length estimations at the level of individual RNA molecules. Although this technology has been publicly available since 2017, the complexity of the raw Nanopore data, together with the lack of systematic and reproducible pipelines, have greatly hindered the access of this technology to the general user. Here we address this problem by providing a fully benchmarked workflow for the analysis of direct RNA sequencing reads, termed MasterOfPores. The pipeline converts raw current intensities into multiple types of processed data, providing metrics of the quality of the run, quality-filtering, base-calling and mapping. The output of the pipeline can in turn be used to compute per-gene counts, RNA modifications, and prediction of polyA tail length and RNA isoforms. The software is written using the NextFlow framework for parallelization and portability, and relies on Linux containers such as Docker and Singularity for achieving better reproducibility. The MasterOfPores workflow can be executed on any Unix-compatible OS on a computer, cluster or cloud without the need of installing any additional software or dependencies, and is freely available in Github (https://github.com/biocorecrg/master_of_pores). This workflow will significantly simplify the analysis of nanopore direct RNA sequencing data by non-bioinformatics experts, thus boosting the understanding of the (epi)transcriptome with single molecule resolution.