Gclust: A Parallel Clustering Tool for Microbial Genomic Data

Publication date: Available online 7 January 2020Source: Genomics, Proteomics & BioinformaticsAuthor(s): Ruilin Li, Xiaoyu He, Chuangchuang Dai, Haidong Zhu, Xianyu Lang, Wei Chen, Xiaodong Li, Dan Zhao, Yu Zhang, Xinyin Han, Tie Niu, Yi Zhao, Rongqiang Cao, Rong He, Zhonghua Lu, Xuebin Chi, Weizhong Li, Beifang NiuAbstractThe accelerating growth of the public microbial genomic data imposes substantial burden on the research community that uses such resources. Building databases for non-redundant reference sequences from massive microbial genomic data based on clustering analysis is essential. However, existing clustering algorithms perform poorly on long genomic sequences. In this article, we present Gclust, a parallel program for clustering complete or draft genomic sequences, where clustering is accelerated with a novel parallelization strategy and a fast sequence comparison algorithm using sparse suffix arrays (SSAs). Moreover, genome identity measures between two sequences are calculated based on their maximal exact matches (MEMs). In this paper, we demonstrate the high speed and clustering quality of Gclust by examining four genome sequence datasets. Gclust is freely available for non-commercial use at https://github.com/niu-lab/gclust. We also introduce a web server for clustering user-uploaded genomes at http://niulab.scgrid.cn/gclust.

Source: Genomics, Proteomics and Bioinformatics - January 8, 2020 Category: Bioinformatics Source Type: research