Streaming histogram sketching for rapid microbiome analytics

Rowe, Will; Carrieri, Anna Paola; Alcon-Giner, Cristina; Caim, Shabhonam; Shaw, Alexander; Sim, Kathleen; Kroll, J. Simon; Hall, Lindsay J.; Pyzer‐Knapp, Edward O.; Winn, Martyn

doi:10.1101/408070

2018

DOI: 10.1101/408070

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Streaming histogram sketching for rapid microbiome analytics

Will Rowe

Anna Paola Carrieri²,

Cristina Alcon-Giner³

et al.

Abstract: MotivationThe growth in publically available microbiome data in recent years has yielded an invaluable resource for genomic research; allowing for the design of new studies, augmentation of novel datasets and reanalysis of published works. This vast amount of microbiome data, as well as the widespread proliferation of microbiome research and the looming era of clinical metagenomics, means there is an urgent need to develop analytics that can process huge amounts of data in a short amount of time. To address th… Show more

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2024

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Microbiome-based classification models for fresh produce safety and quality evaluation

Liao,

Wang,

Quon

2024

Microbiol Spectr

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Small sample sizes and loss of sequencing reads during the microbiome data preprocessing can limit the statistical power of differentiating fresh produce phenotypes and prevent the detection of important bacterial species associated with produce contamination or quality reduction. Here, we explored a machine learning-based k -mer hash analysis strategy to identify DNA signatures predictive of produce safety (PS) and produce quality (PQ) and compared it against the amplicon sequence variant (ASV) strategy that uses a typical denoising step and ASV-based taxonomy strategy. Random forest-based classifiers for PS and PQ using 7-mer hash data sets had significantly higher classification accuracy than those using the ASV data sets. We also demonstrated that the proposed combination of integrating multiple data sets and leveraging a 7-mer hash strategy leads to better classification performance for PS and PQ compared to the ASV method but presents lower PS classification accuracy compared to the feature-selected ASV-based taxonomy strategy. Due to the current limitation of generating taxonomy using the 7-mer hash strategy, the ASV-based taxonomy strategy with remarkably less computing time and memory usage is more efficient for PS and PQ classification and applicable for important taxa identification. Results generated from this study lay the foundation for future studies that wish and need to incorporate and/or compare different microbiome sequencing data sets for the application of machine learning in the area of microbial safety and quality of food. IMPORTANCE Identification of generalizable indicators for produce safety (PS) and produce quality (PQ) improves the detection of produce contamination and quality decline. However, effective sequencing read loss during microbiome data preprocessing and the limited sample size of individual studies restrain statistical power to identify important features contributing to differentiating PS and PQ phenotypes. We applied machine learning-based models using individual and integrated k -mer hash and amplicon sequence variant (ASV) data sets for PS and PQ classification and evaluated their classification performance and found that random forest (RF)-based models using integrated 7-mer hash data sets achieved significantly higher PS and PQ classification accuracy. Due to the limitation of taxonomic analysis for the 7-mer hash, we also developed RF-based models using feature-selected ASV-based taxonomic data sets, which performed better PS classification than those using the integrated 7-mer hash data set. The RF feature selection method identified 480 PS indicators and 263 PQ indicators with a positive contribution to the PS and PQ classification.

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Microbiome-based classification models for fresh produce safety and quality evaluation

Liao,

Wang,

Quon

2024

Microbiol Spectr

View full text Add to dashboard Cite

show abstract

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Streaming histogram sketching for rapid microbiome analytics

Cited by 1 publication

References 32 publications

Microbiome-based classification models for fresh produce safety and quality evaluation

Microbiome-based classification models for fresh produce safety and quality evaluation

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