RabbitQC: high-speed scalable quality control for sequencing data | Zendy

Zekun Yin | Zendy; Hao Zhang | Zendy; Meiyang Liu | Zendy; Wen Zhang | Zendy; Hong-Lei Song | Zendy; Haidong Lan | Zendy; Yanjie Wei | Zendy; Beifang Niu | Zendy; Bertil Schmidt | Zendy; Weiguo Liu | Zendy

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RabbitQC: high-speed scalable quality control for sequencing data

Author(s) -

Zekun Yin,

Hao Zhang,

Meiyang Liu,

Wen Zhang,

Hong-Lei Song,

Haidong Lan,

Yanjie Wei,

Beifang Niu,

Bertil Schmidt,

Weiguo Liu

Publication year - 2020

Publication title -

bioinformatics

Language(s) - Uncategorized

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa719

Subject(s) - computer science , scalability , exploit , process (computing) , quality (philosophy) , software , task (project management) , state (computer science) , control (management) , variety (cybernetics) , data mining , database , artificial intelligence , operating system , systems engineering , programming language , philosophy , computer security , epistemology , engineering

Modern sequencing technologies continue to revolutionize many areas of biology and medicine. Since the generated datasets are error-prone, downstream applications usually require quality control methods to pre-process FASTQ files. However, existing tools for this task are currently not able to fully exploit the capabilities of computing platforms leading to slow runtimes.

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