PRINSEQ++, a multi-threaded tool for fast and efficient quality control and preprocessing of sequencing datasets
- Published
- Accepted
- Subject Areas
- Bioinformatics, Genomics, Computational Science
- Keywords
- Bioinformatics, Computational Biology, Software Engineering, Distributed and Parallel Computing
- Copyright
- © 2019 Cantu 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
- 2019. PRINSEQ++, a multi-threaded tool for fast and efficient quality control and preprocessing of sequencing datasets. PeerJ Preprints 7:e27553v1 https://doi.org/10.7287/peerj.preprints.27553v1
Abstract
PRINSEQ++ is a C++ implementation of the very popular software prinseq-lite for quality control and preprocessing of sequencing datasets. PRINSEQ++ can run multi-threaded processes, which makes it more than 10 times faster than the original version. It can read from, and write to, compressed files, drastically reducing the use of hard-drive. PRINSEQ++ can filter, trim and reformat sequences by a variety of options to improve downstream analysis. PRINSEQ++ is freely available on GitHub (https://github.com/Adrian-Cantu/PRINSEQ-plus-plus) and runs on all Unix-like systems.
Author Comment
This is a preprint submission to PeerJ Preprints.
Supplemental Information
Raw data for timing experiment
Each row is a timing measurement for some input size/ number of threads combination
Summary statistics used to plot figure 1
Each rows indicates the average time, standard deviation, standard error, and .95 confidence interval for the timing measurements for each input size/number of threads combination. This data is derived from sup_table1
Code to plot figure1
jupyter notebook of the code used to plot figure1. This is also available in the github repository
