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Title: Overlap detection on long, error-prone sequencing reads via smooth q -gram
Abstract Motivation Third generation sequencing techniques, such as the Single Molecule Real Time technique from PacBio and the MinION technique from Oxford Nanopore, can generate long, error-prone sequencing reads which pose new challenges for fragment assembly algorithms. In this paper, we study the overlap detection problem for error-prone reads, which is the first and most critical step in the de novo fragment assembly. We observe that all the state-of-the-art methods cannot achieve an ideal accuracy for overlap detection (in terms of relatively low precision and recall) due to the high sequencing error rates, especially when the overlap lengths between reads are relatively short (e.g. <2000 bases). This limitation appears inherent to these algorithms due to their usage of q-gram-based seeds under the seed-extension framework. Results We propose smooth q-gram, a variant of q-gram that captures q-gram pairs within small edit distances and design a novel algorithm for detecting overlapping reads using smooth q-gram-based seeds. We implemented the algorithm and tested it on both PacBio and Nanopore sequencing datasets. Our benchmarking results demonstrated that our algorithm outperforms the existing q-gram-based overlap detection algorithms, especially for reads with relatively short overlapping lengths. Availability and implementation The source code of our implementation in C++ is available at https://github.com/FIGOGO/smoothq. Supplementary information Supplementary data are available at Bioinformatics online.  more » « less
Award ID(s):
1844234
PAR ID:
10215943
Author(s) / Creator(s):
; ; ;
Editor(s):
Yann, Ponty
Date Published:
Journal Name:
Bioinformatics
Volume:
36
Issue:
19
ISSN:
1367-4803
Page Range / eLocation ID:
4838 to 4845
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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