Biology subjects

Noyes, N.

Publications and source records attributed to Noyes, N..

14 recordsLinked to original sources

bioRxiv2025

RAmpSim: A Thermodynamic Simulator for Hybridization Capturein Metagenomic Sequencing

Hybridization (bait) capture combined with long-read sequencing enables targeted profiling within complex metagenomes but introduces systematic biases from bait multiplicity, sequence composition, and species abundance that existing simulators ignore. We present RAmpSim, a fast simulator that models bait-target hybridization and fragment capture using a thermodynamic nearest-neighbor energy model and Boltzmann-weighted sampling of binding sites. Fragments are generated through multinomial sampling parameterized by bait concentration, binding energy, and genomic abundance before being passed to existing long-read simulators for modeling platform-specific errors. Implemented in Rust, RAmpSim reproduces empirical within-genome coverage and cross-species enrichment patterns observed in capture-based metagenomic datasets. Compared to uniform-coverage baselines, RAmpSims simulated coverage distributions are up to an order of magnitude closer to real data with respect to earth movers distance. Classification analysis reveals high recall in classifying high coverage regions between simulated and experimental distributions while outperforming a uniform baseline. Supporting accurate benchmarking and bait-set evaluation, RAmpSim provides an interpretable, efficient framework for simulating capture-based metagenomic sequencing. Code Availabilityhttps://github.com/az002/RAmpSim.git

bioinformatics↗

bioRxiv2025

Target-enriched sequencing enables genomic characterization within diverse microbial populations - a preprint

Characterizing microbial genetic sequences and key variants is critical for understanding pathogen ecology, transmission, and clinical impact. Yet, conventional metagenomic sequencing often yields too few on-target reads to move beyond species-level identification. We developed a target-enriched (TE) metagenomic workflow, including bait design, an optimized TE shotgun protocol, and the VARIANT++ pipeline, to recover and classify reads at a clustered genomic sequence-variant (GSV) level (see Graphical abstract). The computational component clusters reference genomes by average nucleotide identity, builds a GSV database, and integrates Kraken2, Themisto, and mSWEEP to increase call confidence while reducing false positives. Using Mannheimia haemolytica (Mh), the primary cause of bovine respiratory disease, we designed 114,375 DNA baits targeting sequences across 70 reference genomes. TE libraries from nasopharyngeal swabs of feedlot cattle achieved >250-fold increases in on-target Mh reads ([~]2.5% of non-host reads on average) compared with conventional shotgun sequencing, despite using one-quarter the sequencing depth. This variant-level resolution revealed six GSVs; most samples contained at least two, indicating variant mixtures difficult to detect with culture- or shotgun-based surveys. Because the approach leverages available reference sequences, it can be reconfigured for other microbial targets. TE metagenomics paired with genome-similarity clustering provides a scalable approach to variant-level characterization from complex microbial populations. O_FIG O_LINKSMALLFIG WIDTH=159 HEIGHT=200 SRC="FIGDIR/small/684174v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@5a7247org.highwire.dtl.DTLVardef@4ccf1org.highwire.dtl.DTLVardef@12b0413org.highwire.dtl.DTLVardef@118a0ec_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Overview of the components in our three-part workflow. C_FIG

genomics↗

bioRxiv2024

devider: long-read reconstruction of many diverse haplotypes

Reconstructing haplotypes is important when sequencing a mixture of similar sequences. Long-read sequencing can connect distant alleles to disentangle similar haplotypes, but handling se-quencing errors requires specialized techniques. We present devider, an algorithm for haplotyping small sequences--such as viruses or genes--from long-read sequencing. devider uses a positional de Bruijn graph with sequence-to-graph alignment on an alphabet of informative alleles to provide a fast assembly-inspired approach compatible with various long-read sequencing technologies. On a synthetic Nanopore dataset containing seven HIV strains, devider recovered 97% of the haplotype content compared to 86% for the next best method while taking < 4 minutes and 1 GB of memory for > 8000x coverage. Benchmarking on synthetic mixtures of antimicrobial resistance (AMR) genes showed that devider recovered 83% of haplotypes, 23 percentage points higher than the next best method. On real PacBio and Nanopore datasets, devider recapitulates previously known results in seconds, disentan-gling a bacterial community with > 10 strains and an HIV-1 co-infection dataset. We used devider to investigate the within-host diversity of a long-read bovine gut metagenome enriched for AMR genes, discovering 13 distinct haplotypes for a tet(Q) tetracycline resistance gene with > 18, 000x coverage and 6 haplotypes for a CfxA2 beta-lactamase gene. We found clear recombination blocks for these AMR gene haplotypes, showcasing deviders ability to unveil ecological signals for heterogeneous mixtures.

bioinformatics↗

bioRxiv2024

In vitro antimicrobial activity of non-aureus Staphylococci and Mammaliicocci against Staphylococcus aureus and Streptococcus uberis and their relationship with the presence of intramammary infections in organic dairy cows

Prior studies have suggested that non-aureus staphylococci and mammaliicoci (NASM) possess inhibitory activity against mastitis pathogens. However, their impact on udder health outcomes and the mechanisms underlying this potential protective effect remain poorly understood. Our first objective was to identify NASM species on teat apices of organic dairy cows, assess their within-species phylogeny, and explore associations with presence of intramammary infections (IMI) and genomic features, including antimicrobial peptides (AMPs), virulence, and resistance genes. The second objective was to evaluate the in vitro antimicrobial activity of NASM isolates against mastitis pathogens and examine its associations with taxonomy, phylogeny, AMP genes, and IMI. Milk and teat apex swabs were collected weekly from 114 cows on two organic farms. Milk was cultured to identify Staphylococcus aureus (SAU) or Streptococcus spp. and Streptococcus-like organisms (SSLO) IMI. A case-control was designed to include cows with and without SAU or SSLO IMI. For each selected cow, the teat apex gauze swab collected during the week preceding IMI diagnosis (or corresponding time for controls) was aerobically cultured, and the taxonomy of isolates was determined using mass spectrometry. Isolates classified as NASM were subjected to whole genome sequencing using Illumina MiSeq. The inhibitory activity of NASM isolates was tested against SAU and Streptococcus uberis. Phylogenetic trees were constructed using Snippy and IQ-TREE. Genomes were assembled and annotated to identify species, AMP genes, virulence, and antimicrobial resistance markers. The in vitro antimicrobial activity of NASM varied across species and between cows with and without an IMI. Staphylococcus succinus was the species most frequently associated with highly inhibitory isolates, which were more prevalent in cows without IMI (19.4% vs 5.8%). Organic dairy cow teat apices harbored multiple NASM species and strains. All isolates had at least 1 AMP associated gene in their genome. In vitro antimicrobial activity was generally unrelated to clade membership, except for isolates classified as Staphylococcus succinus. Staphylococcus aureus had high virulence gene prevalence, while NASM species showed lower, species-specific prevalence. This study advances understanding of NASM antimicrobial activity and virulence potential.

microbiology↗

bioRxiv2023

Scalable De Novo Classification of Antibiotic Resistance of Mycobacterium Tuberculosis

We develop a robust machine learning classifier using both linear and nonlinear models (i.e., LASSO logistic regression (LR) and random forests (RF)) to predict the phenotypic resistance of Mycobacterium tuberculosis (MTB) for a broad range of antibiotic drugs. We use data from the CRyPTIC consortium to train our classifier, which consists of whole genome sequencing and antibiotic susceptibility testing (AST) phenotypic data for 13 different antibiotics. To train our model, we assemble the sequence data into genomic contigs, identify all unique 31-mers in the set of contigs, and build a feature matrix M, where M [i, j] is equal to the number of times the i-th 31-mer occurs in the j-th genome. Due to the size of this feature matrix (over 350 million unique 31-mers), we build and use a sparse matrix representation. Our method, which we refer to as MTB++, leverages compact data structures and iterative methods to allow for the screening of all the 31-mers in the development of both LASSO LR and RF. MTB++ is able to achieve high discrimination (F-1 greater than 80%) for the first-line antibiotics. Moreover, MTB++ had the highest F-1 score in all but three classes and was the most comprehensive since it had a F-1 score greater than 75% in all but four (rare) antibiotic drugs. We use our feature selection to contextualize the 31-mers that are used for the prediction of phenotypic resistance, leading to some insights about sequence similarity to genes in MEGARes. Lastly, we give an estimate of the amount of data that is needed in order to provide accurate predictions.

bioinformatics↗

bioRxiv2023

The impact of kit, environment and sampling contamination on the observed microbiome of bovine milk

Contaminants can easily outnumber bacteria that originate within the milk itself, milk microbiome research currently suffers from a critical knowledge gap; namely, does non-mastitis bovine milk contain a native microbiome? In this study, we sampled external and internal mammary epithelium, stripped and cisternal milk, used numerous negative controls to identify potential sources of microbial contamination. Two algorithms were used to mathematically remove this contamination and to track potential movement of microbes among our samples. Our results suggest that majority (i.e., >75%) of the sequence data generated from bovine milk and mammary epithelium samples represents contaminating DNA. The contaminants in milk samples were primarily sourced from the DNA extraction kits and the internal and external skin of the teat, while the teat canal and apex samples were mainly contaminated during the sampling process. After decontamination, the milk microbiome displayed a more dispersed, less diverse and compositionally distinct bacterial profile compared with the teat skin samples. Similar microbial compositions were observed between cisternal and stripped milk samples, as well as between teat apex and canal samples. Staphylococcus and Acinetobacter were the predominant genera detected in the sequences of milk samples, and bacterial culture showed growth of Staphylococcus and Corynebacterium spp. in 50% (7/14) of stripped milk samples and growth of Staphylococcus spp. in 7% (1/14) of cisternal milk samples. Our study suggests that microbiome data generated from milk samples obtained from clinically healthy bovine udders may be heavily biased by contaminants that enter the sample during the sample collection and processing workflows. ImportanceObtaining a non-contaminated sample of bovine milk is challenging due to the nature of the sampling environment and the route by which milk is typically extracted from the mammary gland. Furthermore, the very low bacterial biomass of bovine milk exacerbates the impacts of contaminant sequences in downstream analyses, which can lead to severe biases. Our finding showed that bovine milk contains very low bacterial biomass, and each contamination event (including sampling procedure and DNA extraction process) introduces bacteria and/or DNA fragments that easily outnumber the native bacterial cells. This finding has important implications for our ability to draw robust conclusions from milk microbiome data, especially if the data have not been subjected to rigorous decontamination procedures. Based on these findings, we strongly urge researchers to include numerous negative controls into their sampling and sample processing workflows; and to utilize several complementary methods for identifying potential contaminants within the resulting sequence data. These measures will improve the accuracy, reliability, reproducibility, and interpretability of milk microbiome data and research.

microbiology↗

bioRxiv2023

Exploring associations between the teat apex metagenome and Staphylococcus aureus intramammary infection risk in primiparous cows under organic directives

The primary objective of this study was to identify associations between teat apex microbiome and Staphylococcus aureus intramammary infection (IMI) risk in primiparous cows during the first 5 weeks after calving. We performed a case-control study using shotgun metagenomics of the teat apex and culture-based milk data collected longitudinally from 710 primiparous cows on 5 organic dairy farms. We observed a strong association between S. aureus DNA in the metagenomic teat apex data prior to parturition and the odds of S. aureus IMI after parturition (OR = 38.9, 95% CI: 14.84-102.21). Differential abundance analysis confirmed this association, with cases having a 23.8 higher log fold change (LFC) in abundance of S. aureus in their samples compared to controls. Of the most prevalent microorganisms in controls, those associated with a lower risk of post-calving S. aureus IMI included Microbacterium phage Min 1 (OR = 0.37, 95% CI: 0.25-0.53), Corynebacterium efficiens (OR = 0.53, 95% CI: 0.30-0.94), Kocuria polaris (OR = 0.54, 95% CI: 0.35-0.82), Micrococcus terreus (OR = 0.64, 95% CI: 0.44-0.93) and Dietzia alimentaria (OR = 0.45, 95% CI: 0.26-0.75). Microcin B17 was the most prevalent antibacterial peptide on the teat apex of cases and controls (99.7% in both groups). The predicted abundance of Microcin B17 was also higher in cases compared to controls (LFC 0.26). Cow and farm random effects often explained a large proportion of the observed variability in the teat apex microbiome, suggesting that our results need to be interpreted within the context of the random effects. IMPORTANCEIntramammary infections (IMI) caused by Staphylococcus aureus remain an important problem for the organic dairy industry. The microbiome on the external skin of the teat apex may play a role in mitigating S. aureus IMI risk, in particular the production of antimicrobial peptides (AMPs) by commensal microbes. However, current studies of the teat apex microbiome utilize a 16S approach, which precludes detection of genomics features such as AMPs. Therefore, further research using a shotgun metagenomic approach is needed to understand what role pre-partum teat apex microbiome dynamics play in IMI risk.

ecology↗

bioRxiv2022

The K-mer Antibiotic Resistance Gene Variant Analyzer (KARGVA)

Characterization of antibiotic resistance genes (ARGs) from high-throughput sequencing data of metagenomics and cultured bacterial samples is a challenging task, with the need to account for both computational (e.g., string algorithms) and biological (e.g., gene transfers, rearrangements) aspects. Curated ARG databases exist together with assorted ARG classification approaches (e.g., database alignment, machine learning). Besides ARGs that naturally occur in bacterial strains or are acquired through mobile elements, there are chromosomal genes that can render a bacterium resistant to antibiotics through point mutations, i.e., ARG variants (ARGVs). While ARG repositories also collect ARGVs, there are only a few tools that are able to identify ARGVs from metagenomics and high throughput sequencing data, with a number of limitations (e.g., pre-assembly, a posteriori verification of mutations, or specification of species). In this work we present the k-mer, i.e., strings of fixed length k, ARGV analyzer -KARGVA- an open-source, multi-platform tool that provides: (i) an ad hoc, large ARGV database derived from multiple sources; (ii) input capability for various types of high-throughput sequencing data; (iii) a three-way, hash-based, k-mer search setup to process data efficiently, linking k-mers to ARGVs, k-mers to point mutations, and ARGVs to k-mers, respectively; (iv) a statistical filter on sequence classification to reduce type I and II errors. On semi-synthetic data, KARGVA provides very high accuracy even in presence of high sequencing errors or mutations (99.2% and 86.6% accuracy within 1% and 5% base change rates, respectively), and genome rearrangements (98.2% accuracy), with robust performance on ad hoc false positive sets. On data from the worldwide MetaSUB consortium, comprising 3,700+ metagenomics experiments, KARGVA identifies more ARGVs than Resistance Gene Identifier (4.8x) and PointFinder (6.8x), yet all predictions are below the expected false positive estimates. The prevalence of ARGVs is correlated to ARGs but ecological characteristics do not explain well ARGV variance. KARGVA is publicly available at https://github.com/DataIntellSystLab/KARGVA under MIT license.

bioinformatics↗

bioRxiv2022

Advancing antimicrobial resistance monitoring in surface waters with metagenomic and quasimetagenomic methods

Surface waters present a unique challenge for the monitoring of critically important antimicrobial resistance. Metagenomic approaches provide unbiased descriptions of taxonomy and antimicrobial resistance genes in many environments, but for surface water, culture independent data is insufficient to describe critically important resistance. To address this challenge and expand resistome reporting capacity of antimicrobial resistance in surface waters, we apply metagenomic and quasimetagenomic (enriched microbiome) data to examine and contrast water from two sites, a creek near a hospital, and a reservoir used for recreation and municipal water. Approximately 30% of the National Antimicrobial Resistance Monitoring Systems critically important resistance gene targets were identified in enriched data contrasted to only 1% in culture independent data. Four different analytical approaches consistently reported substantially more antimicrobial resistance genes in quasimetagenomic data compared to culture independent data across most classes of antimicrobial resistance. Statistically significant differential fold changes (p<0.05) of resistance determinants were used to infer microbiological differences in the waters. Important pathogens associated with critical antimicrobial resistance were described for each water source. While the single time-point for only two sites represents a small pilot project, the successful reporting of critically important resistance determinants is proof of concept that the quasimetagenomic approach is robust and can be expanded to multiple sites and timepoints for national and global monitoring and surveillance of antimicrobial resistance in surface waters.

microbiology↗

bioRxiv2021

Syotti: Scalable Bait Design for DNA Enrichment

Bait-enriched sequencing is a relatively new sequencing protocol that is becoming increasingly ubiquitous as it has been shown to successfully amplify regions of interest in metagenomic samples. In this method, a set of synthetic probes ("baits") are designed, manufactured, and applied to fragmented metagenomic DNA. The probes bind to the fragmented DNA and any unbound DNA is rinsed away, leaving the bound fragments to be amplified for sequencing. This effectively enriches the DNA for which the probes were designed. Most recently, Metsky et al. (Nature Biotech 2019) demonstrated that bait-enrichment is capable of detecting a large number of human viral pathogens within metagenomic samples. In this work, we formalize the problem of designing baits by defining the Minimum Bait Cover problem, which aims to find the smallest possible set of bait sequences that cover every position of a set of reference sequences under an approximate matching model. We show that the problem is NP-hard, and that it remains NP-hard under very restrictive assumptions. This indicates that no polynomial-time exact algorithm exists for the problem, and that the problem is intractable even for small and deceptively simple inputs. In light of this, we design an efficient heuristic that takes advantage of succinct data structures. We refer to our method as syotti. The running time of syotti shows linear scaling in practice, running at least an order of magnitude faster than state-of-the-art methods, including the recent method of Metsky et al. At the same time, our method produces bait sets that are smaller than the ones produced by the competing methods, while also leaving fewer positions uncovered. Lastly, we show that syotti requires only 25 minutes to design baits for a dataset comprised of 3 billion nucleotides from 1000 related bacterial substrains, whereas the method of Metsky et al. shows clearly super-linear running time and fails to process even a subset of 8% of the data in 24 hours. Our implementation is publicly available at https://github.com/jnalanko/syotti.

bioinformatics↗

bioRxiv2021

Towards routine employment of computational tools for antimicrobial resistance determination via high-throughput sequencing

Antimicrobial resistance (AMR) is a growing threat to public health and farming at large. In clinical and veterinary practice, timely characterization of the antibiotic susceptibility profile of bacterial infections is a crucial step in optimizing treatment. High-throughput sequencing is a promising option for clinical point-of-care and ecological surveillance, opening the opportunity to develop genotyping-based AMR determination as a possibly faster alternative to phenotypic testing. In the present work, we compare the performance of state-of-the-art methods for detection of AMR using high-throughput sequencing data from clinical settings. We consider five computational approaches based on alignment (AMRPlusPlus), deep learning (DeepARG), k-mer genomic signatures (KARGA, ResFinder) or hidden Markov models (Meta-MARC). We use an extensive collection of 585 isolates with available AMR resistance profiles determined by phenotypic tests across nine antibiotic classes. We show how the prediction landscape of AMR classifiers is highly heterogeneous, with balanced accuracy varying from 0.40 to 0.92. Although some algorithms--ResFinder, KARGA, and AMRPlusPlus- exhibit overall better balanced accuracy than others, the high per-AMR-class variance and related findings suggest that: (1) all algorithms might be subject to sampling bias present both in data repositories used for training and experimental/clinical settings; and (2) a portion of clinical samples might contain uncharacterized AMR genes that the algorithms--mostly trained on known AMR genes--fail to generalize upon. These results lead us to formulate practical advice for software configuration and application, and give suggestions for future study designs to further develop AMR prediction tools from proof-of-concept to bedside.

genomics↗

bioRxiv2018

Tychus: a whole genome sequencing pipeline for assembly, annotation and phylogenetics of bacterial genomes

SummaryTychus is a tool that allows researchers to perform massively parallel whole genome sequence (WGS) analysis with the goal of producing a high confidence and comprehensive description of the bacterial genome. Key features of the Tychus pipeline include the assembly, annotation, alignment, variant discovery and phylogenetic inference of large numbers of WGS isolates in parallel using open-source bioinformatics tools and virtualization technology. All prerequisite tools and dependencies come packaged together in a single suite that can be easily downloaded and installed on Linux and Mac operating systems.\n\nAvailabilityTychus is freely available as an open-source package under the MIT license, and can be downloaded via GitHub (https://github.com/Abdo-Lab/Tychus).\n\nContactzaid.abdo@colostate.edu

bioinformatics↗

bioRxiv2017

Associations between sexual habits, menstrual hygiene practices, demographics and the vaginal microbiome as revealed by Bayesian network analysis

The vaginal microbiome plays an influential role in several disease states in reproductive age women, including bacterial vaginosis (BV). While demographic characteristics are associated with differences in vaginal microbiome community structure, little is known about the influence of sexual and hygiene habits. Furthermore, associations between the vaginal microbiome and risk symptoms of bacterial vaginosis have not been fully elucidated. Using Bayesian network (BN) analysis of 16S rRNA gene sequence results, demographic and extensive questionnaire data, we describe both novel and previously documented associations between habits of women and their vaginal microbiome. The BN analysis approach shows promise in uncovering complex associations between disparate data types. Our findings based on this approach support published associations between specific microbiome members (e.g., Eggerthella, Gardnerella, Dialister, Sneathia and Ruminococcaceae), the Nugent score (a BV diagnostic) and vaginal pH (a risk symptom of BV). Additionally, we found that several microbiome members were directly connected to other risk symptoms of BV (such as vaginal discharge, odor, itch, irritation, and yeast infection) including L. jensenii, Corynebacteria, and Proteobacteria. No direct connections were found between the Nugent Score and risk symptoms of BV other than pH, indicating that the Nugent Score may not be the most useful criteria for assessment of clinical BV. We also found that demographics (i.e., age, ethnicity, previous pregnancy) were associated with the presence/absence of specific vaginal microbes. The resulting BN revealed several as-yet undocumented associations between birth control usage, menstrual hygiene practices and specific microbiome members. Many of these complex relationships were not identified using common analytical methods, i.e., ordination and PERMANOVA. While these associations require confirmatory follow-up study, our findings strongly suggest that future studies of the vaginal microbiome and vaginal pathologies should include detailed surveys of participants sanitary, sexual and birth control habits, as these can act as confounders in the relationship between the microbiome and disease. Although the BN approach is powerful in revealing complex associations within multidimensional datasets, the need in some cases to discretize the data for use in BN analysis can result in loss of information. Future research is required to alleviate such limitations in constructing BN networks. Large sample sizes are also required in order to allow for the incorporation of a large number of variables (nodes) into the BN, particularly when studying associations between metadata and the microbiome. We believe that this approach is of great value, complementing other methods, to further our understanding of complex associations characteristic of microbiome research.

microbiology↗

bioRxiv2017

Resistome SNP Calling via Read Colored de Bruijn Graphs

MotivationThe resistome, which refers to all of the antimicrobial resistance (AMR) genes in pathogenic and non-pathogenic bacteria, is frequently studied using shotgun metagenomic data [14, 47]. Unfortunately, few existing methods are able to identify single nucleotide polymorphisms (SNPs) within metagenomic data, and to the best of our knowledge, no methods exist to detect SNPs within AMR genes within the resistome. The ability to identify SNPs in AMR genes across the resistome would represent a significant advance in understanding the dissemination and evolution of AMR, as SNP identification would enable \"fingerprinting\" of the resistome, which could then be used to track AMR dynamics across various settings and/or time periods.\n\nResultsWe present LueVari, a reference-free SNP caller based on the read colored de Bruijn graph, an extension of the traditional de Bruijn graph that allows repeated regions longer than the k-mer length and shorter than the read length to be identified unambiguously. We demonstrate LueVari was the only method that had reliable sensitivity (between 73% and 98%) as the performance of competing methods varied widely. Furthermore, we show LueVari constructs sequences containing the variation which span 93% of the gene in datasets with lower coverage (15X), and 100% of the gene in datasets with higher coverage (30X).\n\nAvailabilityCode and datasets are publicly available at https://github.com/baharpan/cosmo/tree/LueVari.

bioinformatics↗