Alignment-Free Sequence Analysis and Applications

Author:

Ren Jie1,Bai Xin12,Lu Yang Young1,Tang Kujin1,Wang Ying3,Reinert Gesine4,Sun Fengzhu12

Affiliation:

1. Molecular and Computational Biology Program, University of Southern California, Los Angeles, California 90089, USA;

2. Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai 200433, China

3. Department of Automation, Xiamen University, Xiamen, Fujian 361005, China

4. Department of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom

Abstract

Genome and metagenome comparisons based on large amounts of next-generation sequencing (NGS) data pose significant challenges for alignment-based approaches due to the huge data size and the relatively short length of the reads. Alignment-free approaches based on the counts of word patterns in NGS data do not depend on the complete genome and are generally computationally efficient. Thus, they contribute significantly to genome and metagenome comparison. Recently, novel statistical approaches have been developed for the comparison of both long and shotgun sequences. These approaches have been applied to many problems, including the comparison of gene regulatory regions, genome sequences, metagenomes, binning contigs in metagenomic data, identification of virus–host interactions, and detection of horizontal gene transfers. We provide an updated review of these applications and other related developments of word count–based approaches for alignment-free sequence analysis.

Publisher

Annual Reviews

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