Chen S, Chen Y, Sun F, Waterman MS, Zhang X. A new statistic for efficient detection of repetitive sequences.
Bioinformatics 2019;
35:4596-4606. [PMID:
30993316 DOI:
10.1093/bioinformatics/btz262]
[Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Revised: 03/01/2019] [Accepted: 04/10/2019] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION
Detecting sequences containing repetitive regions is a basic bioinformatics task with many applications. Several methods have been developed for various types of repeat detection tasks. An efficient generic method for detecting most types of repetitive sequences is still desirable. Inspired by the excellent properties and successful applications of the D2 family of statistics in comparative analyses of genomic sequences, we developed a new statistic D2R that can efficiently discriminate sequences with or without repetitive regions.
RESULTS
Using the statistic, we developed an algorithm of linear time and space complexity for detecting most types of repetitive sequences in multiple scenarios, including finding candidate clustered regularly interspaced short palindromic repeats regions from bacterial genomic or metagenomics sequences. Simulation and real data experiments show that the method works well on both assembled sequences and unassembled short reads.
AVAILABILITY AND IMPLEMENTATION
The codes are available at https://github.com/XuegongLab/D2R_codes under GPL 3.0 license.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
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