Cited by 29,721Genomics and Phylogenetic StudiesAlgorithms and Data CompressionSingle-cell and spatial transcriptomics

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

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

Open access
Chen, Shifu, Zhou, Yanqing, Chen, Yaru, Gu, Jia·2018·Bioinformatics

Published version of a preprint

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.

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  1. Bianchi, 2015, Noninvasive prenatal testing and incidental detection of occult maternal malignancies, JAMA, 314, 162, 10.1001/jama.2015.7120 · DOI
  2. Chen, 2017, AfterQC: automatic filtering, trimming, error removing and quality control for fastq data, BMC Bioinformatics, 18, 80, 10.1186/s12859-017-1469-3 · DOI
  3. Chen, 2018, SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data, Gigascience, 7, 1, 10.1093/gigascience/gix120 · DOI
  4. Chiang, 2015, SpeedSeq: ultra-fast personal genome analysis and interpretation, Nat. Methods, 12, 966, 10.1038/nmeth.3505 · DOI
  5. Esposito, 2017, The emerging role of “Liquid Biopsies,” circulating tumor cells, and circulating cell-free tumor dna in lung cancer diagnosis and identification of resistance mutations, Curr. Oncol. Rep., 19, 10.1007/s11912-017-0564-y · DOI
  6. Newman, 2016, Integrated digital error suppression for improved detection of circulating tumor DNA, Nat. Biotechnol., 34, 547, 10.1038/nbt.3520 · DOI
  7. Smith, 2017, UMI-tools: modelling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy, Genome Res., 27, 491, 10.1101/gr.209601.116 · DOI

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Metadata

Authors
Chen, S., Zhou, Y., Chen, Y., & Gu, J.
Affiliations
  • Chinese Academy of Sciences (CN)
  • Department of Bioinformatics, HaploX Biotechnology, Shenzhen, China
Corresponding author
Chen, Shifu
Venue
Bioinformatics
Published
2018-09-01
Volume
34
Issue
17
Pages
i884-i890
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
OpenAlex
W2951912016
Cited by
29,721
References
6
Citations by year
2026: 6556 · 2025: 6937 · 2024: 6063 · 2023: 4192 · 2022: 3147 · 2021: 1820 · 2020: 787 · 2019: 187 · 2018: 9
FWCI
258.91
Type
journal article
Language
en
Open access
Locations
  • Bioinformatics (publishedVersion, cc-by-nc)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
  • Special Funds for Future Industries of Shenzhen (JSGG20160229123927512)
  • National Science Foundation of China (61472411)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Physical Sciences › Mathematics › Theoretical Computer Science › Algorithms and Data Compression
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Keywords
quality control, adapter trimming, quality filtering, multithreading, C++, high-throughput sequencing data, fastp
MeSH terms
Humans, Programming Languages, Quality Control

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