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Choosing a benchtop sequencing machine to characterise Helicobacter pylori genomes.

ABSTRACT: The fully annotated genome sequence of the European strain, 26695 was first published in 1997 and, in 1999, it was directly compared to the USA isolate J99, promoting two standard laboratory isolates for Helicobacter pylori (H. pylori) research. With the genomic scaffolds available from these important genomes and the advent of benchtop high-throughput sequencing technology, a bacterial genome can now be sequenced within a few days. We sequenced and analysed strains J99 and 26695 using the benchtop-sequencing machines Ion Torrent PGM and the Illumina MiSeq Nextera and Nextera XT methodologies. Using publically available algorithms, we analysed the raw data and interrogated both genomes by mapping the data and by de novo assembly. We compared the accuracy of the coding sequence assemblies to the originally published sequences. With the Ion Torrent PGM, we found an inherently high-error rate in the raw sequence data. Using the Illumina MiSeq, we found significantly more non-covered nucleotides when using the less expensive Illumina Nextera XT compared with the Illumina Nextera library creation method. We found the most accurate de novo assemblies using the Nextera technology, however, extracting an accurate multi-locus sequence type was inconsistent compared to the Ion Torrent PGM. We found the cagPAI failed to assemble onto a single contig in all technologies but was more accurate using the Nextera. Our results indicate the Illumina MiSeq Nextera method is the most accurate for de novo whole genome sequencing of H. pylori.

SUBMITTER: Perkins TT

PROVIDER: S-EPMC3695873 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Choosing a benchtop sequencing machine to characterise Helicobacter pylori genomes.

Perkins Timothy T TT Tay Chin Yen CY Thirriot Fanny F Marshall Barry B

PloS one 20130628 6

The fully annotated genome sequence of the European strain, 26695 was first published in 1997 and, in 1999, it was directly compared to the USA isolate J99, promoting two standard laboratory isolates for Helicobacter pylori (H. pylori) research. With the genomic scaffolds available from these important genomes and the advent of benchtop high-throughput sequencing technology, a bacterial genome can now be sequenced within a few days. We sequenced and analysed strains J99 and 26695 using the bench ...[more]

PMID: 23840736

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Project description:BackgroundAlthough previous epidemiological studies have examined the potential risk factors that increase the likelihood of acquiring Helicobacter pylori infections, most of these analyses have utilized conventional statistical models, including logistic regression, and have not benefited from advanced machine learning techniques.ObjectiveWe examined H. pylori infection risk factors among school children using machine learning algorithms to identify important risk factors as well as to determine whether machine learning can be used to predict H. pylori infection status.MethodsWe applied feature selection and classification algorithms to data from a school-based cross-sectional survey in Ethiopia. The data set included 954 school children with 27 sociodemographic and lifestyle variables. We conducted five runs of tenfold cross-validation on the data. We combined the results of these runs for each combination of feature selection (e.g., Information Gain) and classification (e.g., Support Vector Machines) algorithms.ResultsThe XGBoost classifier had the highest accuracy in predicting H. pylori infection status with an accuracy of 77%-a 13% improvement from the baseline accuracy of guessing the most frequent class (64% of the samples were H. Pylori negative.) K-Nearest Neighbors showed the worst performance across all classifiers. A similar performance was observed using the F1-score and area under the receiver operating curve (AUROC) classifier evaluation metrics. Among all features, place of residence (with urban residence increasing risk) was the most common risk factor for H. pylori infection, regardless of the feature selection method choice. Additionally, our machine learning algorithms identified other important risk factors for H. pylori infection, such as; electricity usage in the home, toilet type, and waste disposal location. Using a 75% cutoff for robustness, machine learning identified five of the eight significant features found by traditional multivariate logistic regression. However, when a lower robustness threshold is used, machine learning approaches identified more H. pylori risk factors than multivariate logistic regression and suggested risk factors not detected by logistic regression.ConclusionThis study provides evidence that machine learning approaches are positioned to uncover H. pylori infection risk factors and predict H. pylori infection status. These approaches identify similar risk factors and predict infection with comparable accuracy to logistic regression, thus they could be used as an alternative method.

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Dataset Information

Choosing a benchtop sequencing machine to characterise Helicobacter pylori genomes.

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Choosing a benchtop sequencing machine to characterise Helicobacter pylori genomes.

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