Long-read sequencing for identification of insertion sites in large transposon mutant libraries

Yasir, Muhammad, Turner, A. Keith, Lott, Martin, Rudder, Steven, Baker, David, Bastkowski, Sarah, Page, Andrew J., Webber, Mark A. and Charles, Ian G. (2022) Long-read sequencing for identification of insertion sites in large transposon mutant libraries. Scientific Reports, 12 (1). ISSN 2045-2322

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Transposon insertion site sequencing (TIS) is a powerful method for associating genotype to phenotype. However, all TIS methods described to date use short nucleotide sequence reads which cannot uniquely determine the locations of transposon insertions within repeating genomic sequences where the repeat units are longer than the sequence read length. To overcome this limitation, we have developed a TIS method using Oxford Nanopore sequencing technology that generates and uses long nucleotide sequence reads; we have called this method LoRTIS (Long-Read Transposon Insertion-site Sequencing). LoRTIS enabled the unique localisation of transposon insertion sites within long repetitive genetic elements of E. coli, such as the transposase genes of insertion sequences and copies of the ~ 5 kb ribosomal RNA operon. We demonstrate that LoRTIS is reproducible, gives comparable results to short-read TIS methods for essential genes, and better resolution around repeat elements. The Oxford Nanopore sequencing device that we used is cost-effective, small and easily portable. Thus, LoRTIS is an efficient means of uniquely identifying transposon insertion sites within long repetitive genetic elements and can be easily transported to, and used in, laboratories that lack access to expensive DNA sequencing facilities.

Item Type: Article
Additional Information: Funding Information: The author(s) acknowledge the support of the Biotechnology and Biological Sciences Research Council (BBSRC); MY, ML, SB, AKT, MAW and IGC were supported by the BBSRC Institute Strategic Programme Microbes in the Food Chain BB/R012504/1 and its constituent project BBS/E/F/000PR10349. Genomic analysis used the MRC ‘CLIMB’ cloud computing environment supported by grant MR/L015080/1.
Faculty \ School: Faculty of Science > School of Biological Sciences
Faculty of Medicine and Health Sciences > Norwich Medical School
Faculty of Science
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Depositing User: LivePure Connector
Date Deposited: 11 Mar 2022 11:30
Last Modified: 16 Apr 2022 00:29
URI: https://ueaeprints.uea.ac.uk/id/eprint/83992
DOI: 10.1038/s41598-022-07557-x

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