Ultrafast one‐pass FASTQ data preprocessing, quality control, and deduplication using fastp

2023 iMeta 1,338 citations

Abstract

Abstract A large amount of sequencing data is generated and processed every day with the continuous evolution of sequencing technology and the expansion of sequencing applications. One consequence of such sequencing data explosion is the increasing cost and complexity of data processing. The preprocessing of FASTQ data, which means removing adapter contamination, filtering low‐quality reads, and correcting wrongly represented bases, is an indispensable but resource intensive part of sequencing data analysis. Therefore, although a lot of software applications have been developed to solve this problem, bioinformatics scientists and engineers are still pursuing faster, simpler, and more energy‐efficient software. Several years ago, the author developed fastp, which is an ultrafast all‐in‐one FASTQ data preprocessor with many modern features. This software has been approved by many bioinformatics users and has been continuously maintained and updated. Since the first publication on fastp, it has been greatly improved, making it even faster and more powerful. For instance, the duplication evaluation module has been improved, and a new deduplication module has been added. This study aimed to introduce the new features of fastp and demonstrate how it was designed and implemented.

Keywords

Data deduplicationComputer sciencePreprocessorData miningSoftwareAdapter (computing)Data pre-processingDatabaseInformation retrievalArtificial intelligenceComputer hardwareProgramming language

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Publication Info

Year
2023
Type
article
Volume
2
Issue
2
Pages
e107-e107
Citations
1338
Access
Closed

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Cite This

Shifu Chen (2023). Ultrafast one‐pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta , 2 (2) , e107-e107. https://doi.org/10.1002/imt2.107

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DOI
10.1002/imt2.107