NanoMGT: Marker gene typing of low complexity mono-species metagenomic samples using noisy long reads.

Rapid advancements in sequencing technologies have led to significant progress in microbial genomics, yet challenges persist in accurately identifying microbial strain diversity in metagenomic samples, especially when working with noisy long-read data from platforms like Oxford Nanopore Technologies (ONT). In this article, we introduce NanoMGT, a tool designed to enhance marker gene typing in low-complexity mono-species samples, leveraging the unique properties of long reads. NanoMGT excels in its ability to accurately identify mutations amidst high error rates, ensuring the reliable detection of multiple strain-specific marker genes. Our tool implements a novel scoring system that rewards mutations co-occurring across different reads and penalizes densely grouped, likely erroneous variants, thereby achieving a good balance between sensitivity and precision. A comparative evaluation of NanoMGT, using a simulated multi-strain sample of seven bacterial species, demonstrated superior performance relative to existing tools and the advantages of using a threshold-based filtering approach to calling minority variants in ONT's sequencing data. NanoMGT's potential as a post-binning tool in metagenomic pipelines is particularly notable, enabling researchers to more accurately determine specific alleles and understand strain diversity in microbial communities. Our findings have significant implications for clinical diagnostics, environmental microbiology, and the broader field of genomics. The findings offer a reliable and efficient approach to marker gene typing in complex metagenomic samples.

Nanopore sequencing technology for clinical diagnosis of infectious diseases where laboratory capacity is meager: A case report.

In resource-limited settings, patients are often first presented to clinical settings when seriously ill and access to proper clinical microbial diagnostics is often very limited or non-existing. On February 16th, 2022 we were on a field trip to test a completely field-deployable metagenomics sequencing set-up, that includes DNA purification, sequencing, and bioinformatics analyses using bioinformatics tools installed on a laptop for water samples, just outside Moshi, Tanzania. On our way to the test site, we were contacted by the nearby Machame hospital regarding a child seriously ill with diarrhea and not responding to treatment. Within the same day, we conducted an onsite metagenomics examination of a fecal sample from the child, and Campylobacter jejuni was identified as the causative agent. The treatment was subsequently changed, with almost immediate improvement, and the child was discharged on February 21st.

MINTyper: an outbreak-detection method for accurate and rapid SNP typing of clonal clusters with noisy long reads.

For detection of clonal outbreaks in clinical settings, we present a complete pipeline that generates a single-nucleotide polymorphisms-distance matrix from a set of sequencing reads. Importantly, the program is able to handle a separate mix of both short reads from the Illumina sequencing platforms and long reads from Oxford Nanopore Technologies' (ONT) platforms as input. MINTyper performs automated reference identification, alignment, alignment trimming, optional methylation masking, and pairwise distance calculations. With this approach, we could rapidly and accurately cluster a set of DNA sequenced isolates, with a known epidemiological relationship to confirm the clustering. Functions were built to allow for both high-accuracy methylation-aware base-called MinION reads (hac_m Q10) and fast generated lower-quality reads (fast Q8) to be used, also in combination with Illumina data. With fast Q8 reads a higher number of base pairs were excluded from the calculated distance matrix, compared with the high-accuracy methylation-aware Q10 base-calling of ONT data. Nonetheless, when using different qualities of ONT data with corresponding input parameters, the clustering of isolates were nearly identical.