136
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      fastp: an ultra-fast all-in-one FASTQ preprocessor

      research-article
      1 , 2 , 1 , 1 , 2
      Bioinformatics
      Oxford University Press

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Motivation

          Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient.

          Results

          We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools.

          Availability and implementation

          The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.

          Related collections

          Most cited references14

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Fast gapped-read alignment with Bowtie 2.

              As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
                Bookmark

                Author and article information

                Journal
                Bioinformatics
                Bioinformatics
                bioinformatics
                Bioinformatics
                Oxford University Press
                1367-4803
                1367-4811
                01 September 2018
                08 September 2018
                08 September 2018
                : 34
                : 17
                : i884-i890
                Affiliations
                [1 ]Department of Bioinformatics, HaploX Biotechnology, Shenzhen, China
                [2 ]Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
                Author notes
                To whom correspondence should be addressed. E-mail: chen@ 123456haplox.com
                Article
                bty560
                10.1093/bioinformatics/bty560
                6129281
                30423086
                8e1b1e57-d7f2-49a2-8987-35cac1abe6af
                © The Author(s) 2018. Published by Oxford University Press.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

                History
                Page count
                Pages: 7
                Funding
                Funded by: Special Funds for Future Industries of Shenzhen
                Award ID: JSGG20160229123927512
                Funded by: National Science Foundation of China 10.13039/501100001809
                Award ID: 61472411
                Categories
                Eccb 2018: European Conference on Computational Biology Proceedings
                Data

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

                Comments

                Comment on this article