Atropos: specific, sensitive, and speedy trimming of sequencing reads
- Published
- Accepted
- Subject Areas
- Bioinformatics, Genomics, Computational Science
- Keywords
- NGS, Sequencing, Read, Trimming, Preprocessing, Adapter, Cutadapt, Illumina
- Copyright
- © 2017 Didion et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Atropos: specific, sensitive, and speedy trimming of sequencing reads. PeerJ Preprints 5:e2452v4 https://doi.org/10.7287/peerj.preprints.2452v4
Abstract
A key step in the transformation of raw sequencing reads into biological insights is the trimming of adapter sequences and low-quality bases. Read trimming has been shown to increase the quality and reliability while decreasing the computational requirements of downstream analyses. Many read trimming software tools are available; however, no tool simultaneously provides the accuracy, computational efficiency, and feature set required to handle the types and volumes of data generated in modern sequencing-based experiments. Here we introduce Atropos and show that it trims reads with high sensitivity and specificity while maintaining leading-edge speed. Compared to other state-of-the-art read trimming tools, Atropos achieves significant increases in trimming accuracy while remaining competitive in execution times. Furthermore, Atropos maintains high accuracy even when trimming data with elevated rates of sequencing errors. The accuracy, high performance, and broad feature set offered by Atropos makes it an appropriate choice for the pre-processing of Illumina, ABI SOLiD, and other current-generation short-read sequencing datasets. Availability. Atropos is open source and free software written in Python (3.3+) and available at https://github.com/jdidion/atropos.
Author Comment
This is a submission to PeerJ for review.
Supplemental Information
Memory usage of trimming tools on simulated datasets
Maximum memory usage, in MB, of jobs executed on our cluster for trimming tools run on simulated datasets with error rates of A) 0.2\%, B) 0.6\%, and C) 1.2\%. Note that this memory usage includes the overhead of the Singularity container and is thus an overestimate.
CPU Utilization of trimming tools
Average total CPU usage of each trimming tool run on A) simulated data, B) WGBS data, and C) mRNA-Seq data.
Mapping execution times
Execution time of A) bwa-meth on WGBS reads, and B) STAR on mRNA-Seq reads, for reads trimmed by each tool as well as the untrimmed reads.
Descriptions of software used in the benchmark workflow
Performance of trimming tools on desktop machine
Min/max execution time and average CPU usage for trimming of simulated datasets on a desktop with 4 parallel threads.
Performance of trimming tools on cluster node
Min/max execution time and average CPU usage for trimming of simulated datasets on a cluster node with 4, 8, or 16 parallel threads.
Memory usage of jobs run on cluster for trimming simulated datasets
Performance of trimming tools on WGBS dataset
Min/max execution time and average CPU usage for trimming of WGBS data on a cluster node with 4, 8, or 16 parallel threads.
Performance of trimming tools on RNA-Seq dataset
Min/max execution time and average CPU usage for trimming of mRNA-Seq data on a cluster node with 4, 8, or 16 parallel threads.