Sequence clustering in bioinformatics: an empirical study. (18th September 2018)

• Record Type: Journal Article
• Authors: Zou, Quan; Lin, Gang; Jiang, Xingpeng; Liu, Xiangrong; Zeng, Xiangxiang
• Is Part Of: Briefings in bioinformatics. Volume 21:Number 1(2020)
• Page Start: 1
• Page End: 10
• Publication Date: 2018-09-18
• Subjects: operational taxonomic unit -- 16S ribosomal RNA -- microbiome -- sequence clustering -- sequence redundancy removal; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bby090
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Sequence clustering is a basic bioinformatics task that is attracting renewed attention with the development of metagenomics and microbiomics. The latest sequencing techniques have decreased costs and as a result, massive amounts of DNA/RNA sequences are being produced. The challenge is to cluster the sequence data using stable, quick and accurate methods. For microbiome sequencing data, 16S ribosomal RNA operational taxonomic units are typically used. However, there is often a gap between algorithm developers and bioinformatics users. Different software tools can produce diverse results and users can find them difficult to analyze. Understanding the different clustering mechanisms is crucial to understanding the results that they produce. In this review, we selected several popular clustering tools, briefly explained the key computing principles, analyzed their characters and compared them using two independent benchmark datasets. Our aim is to assist bioinformatics users in employing suitable clustering tools effectively to analyze big sequencing data. Related data, codes and software tools were accessible at the link http://lab.malab.cn/∼lg/clustering/ .