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MTG-Link: leveraging barcode information from linked-reads to assemble specific loci.

Authors :
Guichard A
Legeai F
Tagu D
Lemaitre C
Source :
BMC bioinformatics [BMC Bioinformatics] 2023 Jul 14; Vol. 24 (1), pp. 284. Date of Electronic Publication: 2023 Jul 14.
Publication Year :
2023

Abstract

Background: Local assembly with short and long reads has proven to be very useful in many applications: reconstruction of the sequence of a locus of interest, gap-filling in draft assemblies, as well as alternative allele reconstruction of large Structural Variants. Whereas linked-read technologies have a great potential to assemble specific loci as they provide long-range information while maintaining the power and accuracy of short-read sequencing, there is a lack of local assembly tools for linked-read data.<br />Results: We present MTG-Link, a novel local assembly tool dedicated to linked-reads. The originality of the method lies in its read subsampling step which takes advantage of the barcode information contained in linked-reads mapped in flanking regions. We validated our approach on several datasets from different linked-read technologies. We show that MTG-Link is able to assemble successfully large sequences, up to dozens of Kb. We also demonstrate that the read subsampling step of MTG-Link considerably improves the local assembly of specific loci compared to other existing short-read local assembly tools. Furthermore, MTG-Link was able to fully characterize large insertion variants and deletion breakpoints in a human genome and to reconstruct dark regions in clinically-relevant human genes. It also improved the contiguity of a 1.3 Mb locus of biological interest in several individual genomes of the mimetic butterfly Heliconius numata.<br />Conclusions: MTG-Link is an efficient local assembly tool designed for different linked-read sequencing technologies. MTG-Link source code is available at https://github.com/anne-gcd/MTG-Link and as a Bioconda package.<br /> (© 2023. The Author(s).)

Details

Language :
English
ISSN :
1471-2105
Volume :
24
Issue :
1
Database :
MEDLINE
Journal :
BMC bioinformatics
Publication Type :
Academic Journal
Accession number :
37452278
Full Text :
https://doi.org/10.1186/s12859-023-05395-w