Back

A novel tool for designing targeted gene amplicons and an optimised set of primers for high-throughput sequencing in tuberculosis genomic studies

Wang, L.; Thawong, N.; Thorpe, J.; Higgins, M.; Tan, M. K. I.; Sawaengdee, W.; Mahasirimongkol, S.; Perdigao, J.; Campino, S.; Clark, T. G.; Phelan, J. E.

2025-01-15 bioinformatics
10.1101/2025.01.13.632698 bioRxiv
Show abstract

Amplicon sequencing (Amp-Seq) of Mycobacterium tuberculosis genes associated with drug resistance and strain typing offers a cost-effective approach for profiling infections and tailoring the clinical management of tuberculosis. However, Amp-Seq assays require continual updates to incorporate new loci and mutations linked to drug resistance. We introduce TOAST (Tuberculosis Optimised Amplicon Sequencing Tool), a customisable software tool that optimises amplicon design across any loci and sequencing platforms (e.g., Illumina, Oxford Nanopore Technology (ONT)), informed by an integrated and expanding database of mutations from >50K M. tuberculosis isolates. TOAST software allows users to define parameters such as melting temperature, amplicon length, and GC content, while accounting for potential primer interactions like homodimer formation and non-specific binding. To demonstrate its robustness, we designed 33 amplicons in a single multiplex group, prioritising coverage of resistance-associated mutations in the form of insertions, deletions and single nucleotide polymorphisms (SNPs) for 13 different drugs. An efficient experimental protocol was established, resulting in a minimum depth coverage exceeding 50-fold for each amplicon region as validated by ONT sequencing of two clinical samples with multi-drug resistance. TOAST software enables the development of Amp-Seq assays for the rapid detection of drug-resistant TB, enhancing treatment strategies, improving outcomes, and curbing resistant infections. The cost-effectiveness and adaptability of Amp-Seq approaches make it crucial for managing TB in resource-limited settings, advancing global control efforts.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.