College of Agriculture & Natural Resources

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The collections in this community comprise faculty research works, as well as graduate theses and dissertations.

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Subject: search.filters.subject.whole-genome sequencing

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    FOOD SAFETY IN THE ERA OF NEXT-GENERATION SEQUENCING: GENOMIC CHARACTERIZATION OF SHIGA TOXIN-PRODUCING ESCHERICHIA COLI AND METAGENOMIC SURVEILLANCE OF IRRIGATION SURFACE WATER
    (2023) Huang, Xinyang; Meng, Jianghong; Food Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In this study, we first utilized high-throughput next-generation sequencing (NGS) and bioinformatic analyses to characterize potential public health threats posed by non-top-7 Shiga toxin-producing Escherichia coli (STEC). NGS allowed us to detect virulence (n = 46) and antimicrobial resistance (AMR) (n = 27) factors within the genomes of the STEC strains, to make genome-wide comparisons with published human clinical isolates, and to characterize three novel O-antigen gene clusters. We found that the distribution of 33 virulence genes and 15 AMR determinants exhibited significant differences among serotypes (P < 0.05), and that 47 strains were genetically related to human clinical strains inferred from a pan-genome phylogenetic tree. We secondly developed a web tool, PhyloPlus, that allowed users to generate customized bacterial and archaeal phylogenies, which can be incorporated into their own microbial community studies. We also utilized two public datasets (human microbiome, n = 60; fermented food metagenomes, n = 62) to illustrate how application of phylogeny improved our analyses. We showed that the integration of phylogenies introduced alternative phylogeny-based diversity metrics and allowed more conservative null model constructions, thereby reducing potential inflation of type I errors. Finally, we employed deep metagenomic shotgun sequencing, and our developed web tool, to investigate on a collection of 404 surface water samples collected from four regions in Latin America. We reported the high detection rates of pathogenic and contaminant bacteria in these samples, including Salmonella (29.21%), Listeria (6.19%), and E. coli (35.64%), necessitating the monitoring and proper treatment on these surface waters. We also described the regional differences in terms of sample taxonomic composition and the resistome, and further presented key factors that drove the separation patterns for each sampling region. We utilized recent metagenomic assembly and binning algorithms to report the construction of 1,461 de-replicated metagenome-assembled genomes (MAGs) that were of at least medium quality. The incorporation of the MAGs into the taxonomic classifier Kraken2’s database led to a 12.85% increase in classifiable sequence reads. Additionally, we conducted network analysis on AMR genes and the genus-level taxonomy, based on assembled contigs, to provide information to better understand the dynamics of the transferring of AMR genes.
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    Benchmarking Long-Read Assemblers for Genomic Analyses of Bacterial Pathogens Using Oxford Nanopore Sequencing
    (MDPI, 2020-12-01) Chen, Zhao; Erickson, David L.; Meng, Jianghong
    Oxford Nanopore sequencing can be used to achieve complete bacterial genomes. However, the error rates of Oxford Nanopore long reads are greater compared to Illumina short reads. Long-read assemblers using a variety of assembly algorithms have been developed to overcome this deficiency, which have not been benchmarked for genomic analyses of bacterial pathogens using Oxford Nanopore long reads. In this study, long-read assemblers, namely Canu, Flye, Miniasm/Racon, Raven, Redbean, and Shasta, were thus benchmarked using Oxford Nanopore long reads of bacterial pathogens. Ten species were tested for mediocre- and low-quality simulated reads, and 10 species were tested for real reads. Raven was the most robust assembler, obtaining complete and accurate genomes. All Miniasm/Racon and Raven assemblies of mediocre-quality reads provided accurate antimicrobial resistance (AMR) profiles, while the Raven assembly of Klebsiella variicola with low-quality reads was the only assembly with an accurate AMR profile among all assemblers and species. All assemblers functioned well for predicting virulence genes using mediocre-quality and real reads, whereas only the Raven assemblies of low-quality reads had accurate numbers of virulence genes. Regarding multilocus sequence typing (MLST), Miniasm/Racon was the most effective assembler for mediocre-quality reads, while only the Raven assemblies of Escherichia coli O157:H7 and K. variicola with low-quality reads showed positive MLST results. Miniasm/Racon and Raven were the best performers for MLST using real reads. The Miniasm/Racon and Raven assemblies showed accurate phylogenetic inference. For the pan-genome analyses, Raven was the strongest assembler for simulated reads, whereas Miniasm/Racon and Raven performed the best for real reads. Overall, the most robust and accurate assembler was Raven, closely followed by Miniasm/Racon.
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    A Machine Learning Model for Food Source Attribution of Listeria monocytogenes
    (MDPI, 2022-06-16) Tanui, Collins K.; Benefo, Edmund O.; Karanth, Shraddha; Pradhan, Abani K.
    Despite its low morbidity, listeriosis has a high mortality rate due to the severity of its clinical manifestations. The source of human listeriosis is often unclear. In this study, we investigate the ability of machine learning to predict the food source from which clinical Listeria monocytogenes isolates originated. Four machine learning classification algorithms were trained on core genome multilocus sequence typing data of 1212 L. monocytogenes isolates from various food sources. The average accuracies of random forest, support vector machine radial kernel, stochastic gradient boosting, and logit boost were found to be 0.72, 0.61, 0.7, and 0.73, respectively. Logit boost showed the best performance and was used in model testing on 154 L. monocytogenes clinical isolates. The model attributed 17.5 % of human clinical cases to dairy, 32.5% to fruits, 14.3% to leafy greens, 9.7% to meat, 4.6% to poultry, and 18.8% to vegetables. The final model also provided us with genetic features that were predictive of specific sources. Thus, this combination of genomic data and machine learning-based models can greatly enhance our ability to track L. monocytogenes from different food sources.