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Abstract Objective:Whole genome sequencing (WGS) can help identify transmission of pathogens causing healthcare-associated infections (HAIs). However, the current gold standard of short-read, Illumina-based WGS is labor and time intensive. Given recent improvements in long-read Oxford Nanopore Technologies (ONT) sequencing, we sought to establish a low resource approach providing accurate WGS-pathogen comparison within a time frame allowing for infection prevention and control (IPC) interventions. Methods:WGS was prospectively performed on pathogens at increased risk of potential healthcare transmission using the ONT MinION sequencer with R10.4.1 flow cells and Dorado basecaller. Potential transmission was assessed via Ridom SeqSphere+ for core genome multilocus sequence typing and MINTyper for reference-based core genome single nucleotide polymorphisms using previously published cutoff values. The accuracy of our ONT pipeline was determined relative to Illumina. Results:Over a six-month period, 242 bacterial isolates from 216 patients were sequenced by a single operator. Compared to the Illumina gold standard, our ONT pipeline achieved a mean identity score of Q60 for assembled genomes, even with a coverage rate as low as 40×. The mean time from initiating DNA extraction to complete analysis was 2 days (IQR 2–3.25 days). We identified five potential transmission clusters comprising 21 isolates (8.7% of sequenced strains). Integrating ONT with epidemiological data, >70% (15/21) of putative transmission cluster isolates originated from patients with potential healthcare transmission links. Conclusions:Via a stand-alone ONT pipeline, we detected potentially transmitted HAI pathogens rapidly and accurately, aligning closely with epidemiological data. Our low-resource method has the potential to assist in IPC efforts. 

Abstract MotivationSampling k-mers is a ubiquitous task in sequence analysis algorithms. Sampling schemes such as the often-used random minimizer scheme are particularly appealing as they guarantee at least one k-mer is selected out of every w consecutive k-mers. Sampling fewer k-mers often leads to an increase in efficiency of downstream methods. Thus, developing schemes that have low density, i.e. have a small proportion of sampled k-mers, is an active area of research. After over a decade of consistent efforts in both decreasing the density of practical schemes and increasing the lower bound on the best possible density, there is still a large gap between the two. ResultsWe prove a near-tight lower bound on the density of forward sampling schemes, a class of schemes that generalizes minimizer schemes. For small w and k, we observe that our bound is tight when k≡1(mod w). For large w and k, the bound can be approximated by 1w+k⌈w+kw⌉. Importantly, our lower bound implies that existing schemes are much closer to achieving optimal density than previously known. For example, with the current default minimap2 HiFi settings w = 19 and k = 19, we show that the best known scheme for these parameters, the double decycling-set-based minimizer of Pellow et al. is at most 3% denser than optimal, compared to the previous gap of at most 50%. Furthermore, when k≡1(mod w) and the alphabet size σ goes to ∞, we show that mod-minimizers introduced by Groot Koerkamp and Pibiri achieve optimal density matching our lower bound. Availability and implementationMinimizer implementations: github.com/RagnarGrootKoerkamp/minimizers ILP and analysis: github.com/treangenlab/sampling-scheme-analysis. 

Abstract MotivationSince 2016, the number of microbial species with available reference genomes in NCBI has more than tripled. Multiple genome alignment, the process of identifying nucleotides across multiple genomes which share a common ancestor, is used as the input to numerous downstream comparative analysis methods. Parsnp is one of the few multiple genome alignment methods able to scale to the current era of genomic data; however, there has been no major release since its initial release in 2014. ResultsTo address this gap, we developed Parsnp v2, which significantly improves on its original release. Parsnp v2 provides users with more control over executions of the program, allowing Parsnp to be better tailored for different use-cases. We introduce a partitioning option to Parsnp, which allows the input to be broken up into multiple parallel alignment processes which are then combined into a final alignment. The partitioning option can reduce memory usage by over 4× and reduce runtime by over 2×, all while maintaining a precise core-genome alignment. The partitioning workflow is also less susceptible to complications caused by assembly artifacts and minor variation, as alignment anchors only need to be conserved within their partition and not across the entire input set. We highlight the performance on datasets involving thousands of bacterial and viral genomes. Availability and implementationParsnp v2 is available at https://github.com/marbl/parsnp. 

Abstract Motivation: The study of bacterial genome dynamics is vital for understanding the mechanisms underlying microbial adaptation, growth, and their impact on host phenotype. Structural variants (SVs), genomic alterations of 50 base pairs or more, play a pivotal role in driving evolutionary processes and maintaining genomic heterogeneity within bacterial populations. While SV detection in isolate genomes is relatively straightforward, metagenomes present broader challenges due to the absence of clear reference genomes and the presence of mixed strains. In response, our proposed method rhea, forgoes reference genomes and metagenome-assembled genomes (MAGs) by encompassing all metagenomic samples in a series (time or other metric) into a single co-assembly graph. The log fold change in graph coverage between successive samples is then calculated to call SVs that are thriving or declining. Results: We show rhea to outperform existing methods for SV and horizontal gene transfer (HGT) detection in two simulated mock metagenomes, particularly as the simulated reads diverge from reference genomes and an increase in strain diversity is incorporated. We additionally demonstrate use cases for rhea on series metagenomic data of environmental and fermented food microbiomes to detect specific sequence alterations between successive time and temperature samples, suggesting host advantage. Our approach leverages previous work in assembly graph structural and coverage patterns to provide versatility in studying SVs across diverse and poorly characterized microbial communities for more comprehensive insights into microbial gene flux. Availability and implementation: rhea is open source and available at: https://github.com/treangenlab/rhea. 

Abstract 16S rRNA targeted amplicon sequencing is an established standard for elucidating microbial community composition. While high‐throughput short‐read sequencing can elicit only a portion of the 16S rRNA gene due to their limited read length, third generation sequencing can read the 16S rRNA gene in its entirety and thus provide more precise taxonomic classification. Here, we present a protocol for generating full‐length 16S rRNA sequences with Oxford Nanopore Technologies (ONT) and a microbial community profile with Emu. We select Emu for analyzing ONT sequences as it leverages information from the entire community to overcome errors due to incomplete reference databases and hardware limitations to ultimately obtain species‐level resolution. This pipeline provides a low‐cost solution for characterizing microbiome composition by exploiting real‐time, long‐read ONT sequencing and tailored software for accurate characterization of microbial communities. © 2024 Wiley Periodicals LLC. Basic Protocol: Microbial community profiling with Emu Support Protocol 1: Full‐length 16S rRNA microbial sequences with Oxford Nanopore Technologies sequencing platform Support Protocol 2: Building a custom reference database for Emu 

The microbes present in the human gastrointestinal tract are regularly linked to humanhealth and disease outcomes. Thanks to technological and methodological advances in re-cent years, metagenomic sequencing data, and computational methods designed to analyzemetagenomic data, have contributed to improved understanding of the link between thehuman gut microbiome and disease. However, while numerous methods have been recentlydeveloped to extract quantitative and qualitative results from host-associated microbiomedata, improved computational tools are still needed to track microbiome dynamics withshort-read sequencing data. Previously we have proposed KOMB as ade novotool foridentifying copy number variations in metagenomes for characterizing microbial genomedynamics in response to perturbations. In this work, we present KombOver (KO), whichincludes four key contributions with respect to our previous work: (i) it scales to largemicrobiome study cohorts, (ii) it includes bothk-core andK-truss based analysis, (iii)we provide the foundation of a theoretical understanding of the relation between variousgraph-based metagenome representations, and (iv) we provide an improved user experiencewith easier-to-run code and more descriptive outputs/results. To highlight the aforemen-tioned benefits, we applied KO to nearly 1000 human microbiome samples, requiring lessthan 10 minutes and 10 GB RAM per sample to process these data. Furthermore, wehighlight how graph-based approaches such ask-core andK-truss can be informative forpinpointing microbial community dynamics within a myalgic encephalomyelitis/chronic fa-tigue syndrome (ME/CFS) cohort. KO is open source and available for download/use at:https://github.com/treangenlab/komb 

In the aftermath of COVID-19, screening for pathogens has never been a more relevant problem. However, computational screening for pathogens is challenging due to a variety of factors, including (i) the complexity and role of the host, (ii) virulence factor divergence and dynamics, and (iii) population and community-level dynamics. Considering a potential pathogen's molecular interactions, specifically individual proteins and protein interactions can help pinpoint a potential protein of a given microbe to cause disease. However, existing tools for pathogen screening rely on existing annotations (KEGG, GO, etc), making the assessment of novel and unannotated proteins more challenging. Here, we present an LLM-inspired approach that considers protein sequence and structure to predict protein virulence. We present a two-stage model incorporating evolutionary features captured from the DistilProtBert language model and protein structure in a graph convolutional network. Our model performs better than sequence alone for virulence function when high-quality structures are present, thus representing a path forward for virulence prediction of novel and unannotated proteins.