AmpliconTyper - a tool for analysing ONT multiplex PCR data from environmental and other complex samples.

Area of Science:

• Microbiology
• Bioinformatics
• Genomics

Background:

• Amplicon sequencing, including 16S rRNA sequencing, is vital for studying bacterial community diversity.
• Existing tools for amplicon data processing primarily support short-read platforms like Illumina, with fewer options for long-read Oxford Nanopore Technologies (ONT).
• Processing complex environmental samples with numerous organisms presents unique challenges for amplicon sequencing data analysis.

Purpose of the Study:

• To develop a robust tool for analyzing multiplex amplicon sequencing data from complex environmental samples using ONT devices.
• To enable accurate classification of sequencing reads into target and non-target organisms.
• To provide capabilities for identifying genetic markers such as single nucleotide polymorphisms (SNPs) and their implications.

Main Methods:

• Development of AmpliconTyper, a novel software tool (v0.1.28).
• Utilizes machine learning for high-specificity and high-sensitivity classification of sequencing reads.
• Supports user-defined model training with public and/or user-generated data.
• Enables generation of amplicon consensus sequences and SNP identification.

Main Results:

• AmpliconTyper demonstrates high specificity and sensitivity in classifying reads from complex environmental samples.
• The tool successfully identifies target organism reads in ONT-sequenced environmental samples.
• It can identify user-specified lineage or antimicrobial resistance (AMR) markers and report genotype implications.

Conclusions:

• AmpliconTyper provides a powerful solution for analyzing multiplex amplicon sequencing data from environmental samples using ONT.
• The tool's machine learning approach enhances the accuracy of organism identification and marker detection.
• AmpliconTyper facilitates robust microbial community analysis and genetic marker discovery in complex datasets.