A new statistic for efficient detection of repetitive sequences

Abstract

MOTIVATION\nDetecting 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.\n\n\nRESULTS\nUsing 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 CRISPR 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. The codes are available at https://github.com/XuegongLab/D2R_codes under GPL 3.0 license.\n\n\nSUPPLEMENTARY INFORMATION\nSupplementary data are available at Bioinformatics online.