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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.
This is a submission to PeerJ for review.
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.