A Fast Parallel K-Modes Algorithm for Clustering Nucleotide Sequences to Predict Translation Initiation Sites

Journal of computational biology: A journal of computational molecular cell biology

Luis EnriqueZárate^1¹  Guilherme TorresCastro^1² 

[1] Address correspondence to: Dr. Henrique C. Freitas, Department of Computer Science, Pontifícia Universidade Católica de Minas Gerais, Av. Dom Jose Gaspar 500, Belo Horizonte 30535-901, Minas Gerais, Brazil^2;Department of Computer Science, Pontifícia Universidade Católica de Minas Gerais, Belo Horizonte, Brazil^1

关键词: clustering;    K-means;    K-modes;    nucleotide sequences;    parallel computing;    translation initiation site;   

DOI  :  10.1089/cmb.2018.0245

学科分类：生物科学（综合）

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

Predicting the location of the translation initiation sites (TIS) is an important problem of molecular biology. In this field, the computational cost for balancing non-TIS sequences is substantial and demands high-performance computing. In this article, we present an optimized version of the K-modes algorithm to cluster TIS sequences and a comparison with the standard K-means clustering. The adapted algorithm uses simple instructions and fewer computational resources to deliver a significant speedup without compromising the sequence clustering results. We also implemented two optimized parallel versions of the algorithm, one for graphics processing units (GPUs) and the other one for general-purpose multicore processors. In our experiments, the GPU K-modes's performance was up to 203 times faster than the respective sequential version for processing Arabidopsis thaliana sequence.

【 授权许可】

Unknown