henhaobio
Sign in

bioRxivBioinformaticsPreprint

LoRTIS Software Suite: Transposon mutant analysis using long-read sequencing

Martin C. LottiD, Muhammad YasiriD, A. Keith TurneriD, Sarah BastkowskiiD, Andrew J. PageiD, Mark Alexander WebberiD, Ian G. CharlesiD

PDFDOI

Abstract

To date transposon insertion sequencing (TIS) methodologies have used short-read nucleotide sequencing technology. However, short-read sequences are unlikely to be matched correctly within repeated genomic regions which are longer than the sequence read. This drawback may be overcome using long-read sequencing technology. We have developed a suite of new analysis tools, the "LoRTIS software suite" (LoRTIS-SS), that produce transposon insertion site mapping data for a reference genome using long-read nucleotide sequence data. Long-read nucleotide sequence data can be applied to TIS, this enables the unique mapping of transposon insertion sites within long genomic repeated sequences. Here we present long-read TIS analysis software, LoRTIS-SS, which uses the Snakemake framework to manage the workflow. A docker image is provided, complete with dependencies and ten scripts are included for experiment specific data processing before or after use of the main workflow. The workflow uses long-read nucleotide sequence data such as those generated by the MinION sequencer (Oxford Nanopore Technologies). The unique mapping properties of long-read sequence data were exemplified by reference to the ribosomal RNA genes of Escherichia coli strain BW25113, of which there are 7 copies of [~]4.9 kbases in length that are at least 99% similar. Of reads that matched within rRNA genes, approximately half matched uniquely. The software workflow outputs data compatible with the established Bio-TraDIS analysis toolkit allowing for existing workflows to be easily upgraded to support long-read sequencing.

Citations
1

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.