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Chapter 14 - Advances in Machine Learning for Processing and Comparison of Metagenomic Data
Book chapter

Chapter 14 - Advances in Machine Learning for Processing and Comparison of Metagenomic Data

Jean-Luc Bouchot, William L. Trimble, Gregory Ditzler, Yemin Lan, Steve Essinger and Gail L Rosen
Computational Systems Biology, pp 295-329
2014
DOI: https://doi.org/10.1016/B978-0-12-405926-9.00014-9

Abstract

Dimensionality reduction Feature selection Gene annotation Gene prediction Metagenomic sample comparison Similarity measures Taxonomic classification
Recent advances in next-generation sequencing have enabled high-throughput determination of biological sequences in microbial communities, also known as microbiomes. The large volume of data now presents the challenge of how to extract knowledge—recognize patterns, find similarities, and find relationships—from complex mixtures of nucleic acid sequences currently being examined. In this chapter we review basic concepts as well as state-of-the-art techniques to analyze hundreds of samples which each contain millions of DNA and RNA sequences. We describe the general character of sequence data and describe some of the processing steps that prepare raw sequence data for inference. We then describe the process of extracting features from the data, assigning taxonomic and gene labels to the sequences. Then we review methods for cross-sample comparisons: (1) using similarity measures and ordination techniques to visualize and measure differences between samples and (2) feature selection and classification to select the most relevant features for discriminating between samples. Finally, in conclusion, we outline some open research problems and challenges left for future research.

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