RabbitQCPlus 2.0: More efficient and versatile quality control for sequencing data

Publication type:
Journal article
Metadata:
Autoren
  • Lifeng Yan
  • Zekun Yin
  • Hao Zhang
  • Zhan Zhao
  • Mingkai Wang
  • André Müller
  • Felix Kallenborn
  • Alexander Wichmann
  • Yanjie Wei
  • Beifang Niu
  • Bertil Schmidt
  • Weiguo Liu
DOI
10.1016/j.ymeth.2023.06.007
ISSN
1046-2023
Zeitschrift
Methods
Sprache
en
Paginierung
39 - 50
Datum der Veröffentlichung
2023
Status
Published
Herausgeber
Elsevier BV
Herausgeber URL
http://dx.doi.org/10.1016/j.ymeth.2023.06.007
Datum der Datenerfassung
2023
Titel
RabbitQCPlus 2.0: More efficient and versatile quality control for sequencing data
Ausgabe der Zeitschrift
216

Data source: Crossref

Other metadata sources:
Abstract
Assessing the quality of sequencing data plays a crucial role in downstream data analysis. However, existing tools often achieve sub-optimal efficiency, especially when dealing with compressed files or performing complicated quality control operations such as over-representation analysis and error correction. We present RabbitQCPlus, an ultra-efficient quality control tool for modern multi-core systems. RabbitQCPlus uses vectorization, memory copy reduction, parallel (de)compression, and optimized data structures to achieve substantial performance gains. It is 1.1 to 5.4 times faster when performing basic quality control operations compared to state-of-the-art applications yet requires fewer compute resources. Moreover, RabbitQCPlus is at least 4 times faster than other applications when processing gzip-compressed FASTQ files and 1.3 times faster with the error correction module turned on. Furthermore, it takes less than 4 minutes to process 280 GB of plain FASTQ sequencing data, while other applications take at least 22 minutes on a 48-core server when enabling the per-read over-representation analysis. C++ sources are available at https://github.com/RabbitBio/RabbitQCPlus.
Addresses
  • School of Software, Shandong University, Jinan, China.
Autoren
  • Lifeng Yan
  • Zekun Yin
  • Hao Zhang
  • Zhan Zhao
  • Mingkai Wang
  • André Müller
  • Felix Kallenborn
  • Alexander Wichmann
  • Yanjie Wei
  • Beifang Niu
  • Bertil Schmidt
  • Weiguo Liu
DOI
10.1016/j.ymeth.2023.06.007
eISSN
1095-9130
Externe Identifier
  • PubMed Identifier: 37330158
Funding acknowledgements
  • Natural Science Foundation of Shandong Province: ZR2021QF089
  • Deutsche Forschungsgemeinschaft: 439669440 TRR319 RMaP TP C01
  • National Natural Science Foundation of China: 62102231
  • National Natural Science Foundation of China: 61972231
  • Ministry of Education of the People's Republic of China:
Open access
false
ISSN
1046-2023
Zeitschrift
Methods (San Diego, Calif.)
Schlüsselwörter
  • Sequence Analysis, DNA
  • Algorithms
  • Quality Control
  • Data Compression
  • Software
  • High-Throughput Nucleotide Sequencing
Sprache
eng
Medium
Print-Electronic
Online publication date
2023
Paginierung
39 - 50
Datum der Veröffentlichung
2023
Status
Published
Datum der Datenerfassung
2023
Titel
RabbitQCPlus 2.0: More efficient and versatile quality control for sequencing data.
Sub types
  • Research Support, Non-U.S. Gov't
  • Journal Article
Ausgabe der Zeitschrift
216

Data source: Europe PubMed Central

Abstract
Assessing the quality of sequencing data plays a crucial role in downstream data analysis. However, existing tools often achieve sub-optimal efficiency, especially when dealing with compressed files or performing complicated quality control operations such as over-representation analysis and error correction. We present RabbitQCPlus, an ultra-efficient quality control tool for modern multi-core systems. RabbitQCPlus uses vectorization, memory copy reduction, parallel (de)compression, and optimized data structures to achieve substantial performance gains. It is 1.1 to 5.4 times faster when performing basic quality control operations compared to state-of-the-art applications yet requires fewer compute resources. Moreover, RabbitQCPlus is at least 4 times faster than other applications when processing gzip-compressed FASTQ files and 1.3 times faster with the error correction module turned on. Furthermore, it takes less than 4 minutes to process 280 GB of plain FASTQ sequencing data, while other applications take at least 22 minutes on a 48-core server when enabling the per-read over-representation analysis. C++ sources are available at https://github.com/RabbitBio/RabbitQCPlus.
Date of acceptance
2023
Autoren
  • Lifeng Yan
  • Zekun Yin
  • Hao Zhang
  • Zhan Zhao
  • Mingkai Wang
  • André Müller
  • Felix Kallenborn
  • Alexander Wichmann
  • Yanjie Wei
  • Beifang Niu
  • Bertil Schmidt
  • Weiguo Liu
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/37330158
DOI
10.1016/j.ymeth.2023.06.007
eISSN
1095-9130
Zeitschrift
Methods
Schlüsselwörter
  • Error correction
  • Gzip-compressed
  • HPC
  • Over-representation
  • Quality control
  • Sequencing data
  • Vectorization
  • Software
  • High-Throughput Nucleotide Sequencing
  • Data Compression
  • Quality Control
  • Algorithms
  • Sequence Analysis, DNA
Sprache
eng
Country
United States
Paginierung
39 - 50
PII
S1046-2023(23)00099-3
Datum der Veröffentlichung
2023
Status
Published
Datum, an dem der Datensatz öffentlich gemacht wurde
2023
Titel
RabbitQCPlus 2.0: More efficient and versatile quality control for sequencing data.
Sub types
  • Journal Article
  • Research Support, Non-U.S. Gov't
Ausgabe der Zeitschrift
216

Data source: PubMed

Beziehungen:
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