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Title:Information theoretic limits of metagenomic binning
Author(s):Greenberg, Grant
Advisor(s):Shomorony, Ilan
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Information Theory
Large Deviation Theory
Computational Biology
Abstract:The goal of metagenomics is to study the composition of microbial communities, typically using high-throughput shotgun sequencing. In the metagenomic binning problem, we observe random substrings (called contigs) from a mixture of genomes and want to cluster them according to their genome of origin. Based on the empirical observation that genomes of different bacterial species can be distinguished based on their tetranucleotide frequencies, we model this task as the problem of clustering N sequences generated by M distinct Markov processes, where M ≪ N. Utilizing the large-deviation principle for Markov processes, we establish the information-theoretic limit for perfect binning. Specifically, we show that the length of the contigs must scale with the inverse of the Chernoff Information between the two most similar species. Our result also implies that contigs should be binned using the conditional relative entropy as a measure of distance, as opposed to the Euclidean distance often used in practice.
Issue Date:2021-04-30
Rights Information:Copyright 2021 Grant Greenberg
Date Available in IDEALS:2021-09-17
Date Deposited:2021-05

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