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Bioinformatics Pre-Processing of Microbiome Data with An Application to Metagenomic Forensics

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Statistical Analysis of Microbiome Data

Part of the book series: Frontiers in Probability and the Statistical Sciences ((FROPROSTAS))

Abstract

One of the key challenges in metagenomic forensics is to establish a microbial fingerprint that can help in the prediction of the geographical origin of the metagenomic samples. Understanding a combination of different aspects such as microbiome sample sources, sequencing technologies, bioinformatics processing, statistical and machine learning methods, play a vital role in a comprehensive analysis of metagenomic data. We demonstrate the analysis of metagenomic samples from 23 different cities around the world to construct classification models that can be utilized to predict the geographical location for unknown samples obtained from these regions. We also describe the bioinformatics pre-processing of the raw sequencing data and estimate the abundance profiles of microbes in the samples using multiple tools. For the prediction of the geographical origin of samples, we trained and evaluated a variety of supervised learning classifiers including an adaptive optimal ensemble classifier that performs well on different data generating procedures.

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Acknowledgements

This work was partially supported by the National Science Foundation under Award DMS-1461948 to SG.

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Authors and Affiliations

  1. Department of Biostatistics, University of Florida, Gainesville, FL, USA

    Samuel Anyaso-Samuel, Archie Sachdeva, Subharup Guha & Somnath Datta

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  1. Samuel Anyaso-Samuel
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  2. Archie Sachdeva
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Correspondence to Somnath Datta .

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  1. Biostatistics, University of Florida, Gainesville, FL, USA

    Somnath Datta

  2. Biostatistics, University of Florida, Gainesville, FL, USA

    Subharup Guha

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Anyaso-Samuel, S., Sachdeva, A., Guha, S., Datta, S. (2021). Bioinformatics Pre-Processing of Microbiome Data with An Application to Metagenomic Forensics. In: Datta, S., Guha, S. (eds) Statistical Analysis of Microbiome Data. Frontiers in Probability and the Statistical Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-73351-3_3

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