Advances in Machine Learning for Processing and Comparison of Metagenomic Data

Jean Luc Bouchot, William L. Trimble, Gregory Ditzler, Yemin Lan, Steve Essinger, Gail Rosen

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationComputational Systems Biology
Subtitle of host publicationFrom Molecular Mechanisms to Disease: Second Edition
PublisherElsevier Inc.
Number of pages35
ISBN (Print)9780124059269
StatePublished - Dec 2013


  • Dimensionality reduction
  • Feature selection
  • Gene annotation
  • Gene prediction
  • Metagenomic sample comparison
  • Similarity measures
  • Taxonomic classification

ASJC Scopus subject areas

  • General Biochemistry, Genetics and Molecular Biology


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