A sensitive repeat identification framework based on short and long reads
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Computational Bioscience Research Center (CBRC)
KAUST Grant NumberBAS/1/1624-01
Permanent link to this recordhttp://hdl.handle.net/10754/670106
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AbstractNumerous studies have shown that repetitive regions in genomes play indispensable roles in the evolution, inheritance and variation of living organisms. However, most existing methods cannot achieve satisfactory performance on identifying repeats in terms of both accuracy and size, since NGS reads are too short to identify long repeats whereas SMS (Single Molecule Sequencing) long reads are with high error rates. In this study, we present a novel identification framework, LongRepMarker, based on the global de novo assembly and k-mer based multiple sequence alignment for precisely marking long repeats in genomes. The major characteristics of LongRepMarker are as follows: (i) by introducing barcode linked reads and SMS long reads to assist the assembly of all short paired-end reads, it can identify the repeats to a greater extent; (ii) by finding the overlap sequences between assemblies or chomosomes, it locates the repeats faster and more accurately; (iii) by using the multi-alignment unique k-mers rather than the high frequency k-mers to identify repeats in overlap sequences, it can obtain the repeats more comprehensively and stably; (iv) by applying the parallel alignment model based on the multi-alignment unique k-mers, the efficiency of data processing can be greatly optimized and (v) by taking the corresponding identification strategies, structural variations that occur between repeats can be identified. Comprehensive experimental results show that LongRepMarker can achieve more satisfactory results than the existing de novo detection methods (https://github.com/BioinformaticsCSU/LongRepMarker).
CitationLiao, X., Li, M., Hu, K., Wu, F.-X., Gao, X., & Wang, J. (2021). A sensitive repeat identification framework based on short and long reads. Nucleic Acids Research. doi:10.1093/nar/gkab563
SponsorsNational Natural Science Foundation of China [62002388, 61772557]; The NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization [U1909208]; Hunan Provincial Science and Technology Program [2018wk4001]; 111 Project [B18059]; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [BAS/1/1624-01, FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, REI/1/0018-01-01, REI/1/4216-01-01, REI/1/4437-01-01, REI/1/4473-01-01, URF/1/4352-01-01, REI/1/4742-01-01, URF/1/4098-01-01]. Funding for open access charge: The NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization [U1909208]; Hunan Provincial Science and Technology Program [2018wk4001].
PublisherOxford University Press
JournalNucleic Acids Research
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
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