Information theoretic limits of metagenomic binning

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2021

Author dc:creator

• Greenberg, Grant

Contributors dc:contributor

• Shomorony, Ilan

• Metagenomics
• Information Theory
• Large Deviation Theory
• Computational Biology
• Bioinformatics
• Genomics

Statement dc:rights

• Copyright 2021 Grant Greenberg

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/110757

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/110757