MRUniNovo: an efficient tool for de novo peptide sequencing utilizing the Hadoop distributed computing framework
peer-reviewed · Bioinformatics · 2017
| Date | 2017-03-15 |
| Type | peer-reviewed |
| Venue | Bioinformatics |
| Publisher | Oxford University Press (OUP) |
| Contribution | algorithm |
| DOI | 10.1093/bioinformatics/btw721 |
| Citations (OpenAlex) | 15 |
| Venue 2-year citedness | 5.93 |
Abstract
Summary: Tandem mass spectrometry-based de novo peptide sequencing is a complex and time-consuming process. The current algorithms for de novo peptide sequencing cannot rapidly and thoroughly process large mass spectrometry datasets. In this paper, we propose MRUniNovo, a novel tool for parallel de novo peptide sequencing. MRUniNovo parallelizes UniNovo based on the Hadoop compute platform. Our experimental results demonstrate that MRUniNovo significantly reduces the computation time of de novo peptide sequencing without sacrificing the correctness and accuracy of the results, and thus can process very large datasets that UniNovo cannot. Availability and Implementation: MRUniNovo is an open source software tool implemented in java. The source code and the parameter settings are available at http://bioinfo.hupo.org.cn/MRUniNovo/index.php. Supplementary information: Supplementary data are available at Bioinformatics online.
Methods and tools
- MRUniNovo: Hadoop-distributed implementation of UniNovo for efficient de novo peptide sequencing.