Information theoretic limits of metagenomic binning
Greenberg, Grant
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https://hdl.handle.net/2142/110757
Description
Title
Information theoretic limits of metagenomic binning
Author(s)
Greenberg, Grant
Issue Date
2021-04-30
Director of Research (if dissertation) or Advisor (if thesis)
Shomorony, Ilan
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Metagenomics
Information Theory
Large Deviation Theory
Computational Biology
Bioinformatics
Genomics
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.
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