Biology subjects

Dillon, M.

Publications and source records attributed to Dillon, M..

3 recordsLinked to original sources

bioRxiv2018

q2-sample-classifier: machine-learning tools for microbiome classification and regression

Microbiome studies often aim to predict outcomes or differentiate samples based on their microbial compositions, tasks that can be efficiently performed by supervised learning methods. Here we present a benchmark comparison of supervised learning classifiers and regressors implemented in scikit-learn, a Python-based machine-learning library. We additionally present q2-sample-classifier, a plugin for the QIIME 2 microbiome bioinformatics framework, that facilitates application of the scikit-learn classifiers to microbiome data. Random forest, extra trees, and gradient boosting models demonstrate the highest performance for both supervised classification and regression of microbiome data. Automated feature selection and hyperparameter tuning enhance performance of most methods but may not be necessary under all circumstances. The q2-sample-classifier plugin makes these methods more accessible and interpretable to a broad audience of microbiologists, clinicians, and others who wish to utilize supervised learning methods for predicting sample characteristics based on microbiome composition. The q2-sample-classifier source code is available at https://github.com/qiime2/q2-sample-classifier. It is released under a BSD-3-Clause license, and is freely available including for commercial use.

bioinformatics↗

bioRxiv2017

q2-longitudinal: a QIIME 2 plugin for longitudinal and paired-sample analyses of microbiome data

Studies of host-associated and environmental microbiomes often incorporate longitudinal sampling or paired samples in their experimental design. Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity, offering advantages over cross-sectional and pre/post study designs. To support the needs of microbiome researchers performing longitudinal studies, we developed q2-longitudinal, a software plugin for the QIIME 2 microbiome analysis platform (https://qiime2.org). The q2-longitudinal plugin incorporates multiple methods for analysis of longitudinal and paired-sample data, including paired differences and distances, linear mixed effects models, microbial interdependence test, first differencing, and volatility analyses. The q2-longitudinal package (https://github.com/qiime2/q2-longitudinal) is open source software released under a BSD-3-Clause license and is freely available, including for commercial use.

bioinformatics↗

bioRxiv2016

The genome-wide transcriptional response to varying RpoS levels in Escherichia coli K-12

The alternative sigma factor RpoS is a central regulator of a many stress responses in Escherichia coli. The level of functional RpoS differs depending on the stress. The effect of these differing concentrations of RpoS on global transcriptional responses remains unclear. We investigated the effect of RpoS concentration on the transcriptome during stationary phase in rich media. We show that 23% of genes in the E. coli genome are regulated by RpoS level, and we identify many RpoS-transcribed genes and promoters. We observe three distinct classes of response to RpoS by genes in the regulon: genes whose expression changes linearly with increasing RpoS level, genes whose expression changes dramatically with the production of only a little RpoS (\"sensitive\" genes), and genes whose expression changes very little with the production of a little RpoS (\"insensitive\"). We show that sequences outside the core promoter region determine whether a RpoS-regulated gene in sensitive or insensitive. Moreover, we show that sensitive and insensitive genes are enriched for specific functional classes, and that the sensitivity of a gene to RpoS corresponds to the timing of induction as cells enter stationary phase. Thus, promoter sensitivity to RpoS is a mechanism to coordinate specific cellular processes with growth phase, and may also contribute to the diversity of stress responses directed by RpoS.\n\nImportanceThe sigma factor RpoS is a global regulator that controls the response to many stresses in Escherichia coli. Different stresses result in different levels of RpoS production, but the consequences of this variation are unknown. We describe how changing the level of RpoS does not influence all RpoS-regulated genes equally. The cause of this variation is likely the action of transcription factors that bind the promoters of the genes. We show that the sensitivity of a gene to RpoS levels explains the timing of expression as cells enter stationary phase, and that genes with different RpoS sensitivities are enriched for specific functional groups. Thus, promoter sensitivity to RpoS is a mechanism to coordinate specific cellular processes in response to stresses.

microbiology↗