Biology-aware machine learning methods for characterizing microbiome genotype and phenotype

PROJECT SUMMARY 1 The Mirarab laboratory designs leading computational methods for answering biological and biomedical ques- 2 tions, focusing on scalability and accuracy.

These methods span several areas (e.g., microbiome pro?ling, 3 multiple sequence alignment, and phylogenomics), and a common thread among them is evolutionary mod- 4 eling.

The lab has developed scalable and accurate methods for reconstructing evolutionary histories (i.e., 5 phylogenies) and using these histories in downstream biomedical applications. Reconstructing phylogenies is a 6 fundamental goal and a precursor to many biological analyses. Methods developed by this lab (e.g., ASTRAL) 7 are at the forefronts of modern genome-wide phylogenetics.

Moreover, biomedical research increasingly uses 8 evolutionary histories in diverse areas like microbiome analyses, immunology, epidemiology, and comparative 9 genomics.

While the lab has previously focused more on inferring species histories, it has recently started 10 to shift its focus to developing methods for microbiome analyses.

The inference and the use of evolutionary 11 histories in analyzing environmental microbiome samples present a unique set of challenges. 12 In the next ?ve years, the Mirarab lab will focus on designing, testing, and applying improved methods for 13 statistical analyses of microbiome data.

These methods will target two questions. (i) Pro?ling: What organisms 14 constitute a given sample? (ii) Association: How are samples different in their organismal composition, and 15 how do these differences connect to measurable characteristics of their environment?

While both questions 16 have been subject to considerable research, many computational challenges remain, providing an opportunity 17 for better methods to make a signi?cant impact.

Instead of focusing solely on new algorithms, the lab will 18 also work on building better reference datasets and combining data from multiple sources.

Thus, the project 19 aims to harness the unprecedented computational power, large available datasets, and recent advances in 20 machine learning to improve state-of-the-art dramatically. The project will not use off-the-shelf machine learning 21 methods in a black-box fashion.

Instead, it develops methods that incorporate biological knowledge (e.g., of the 22 evolutionary relationships) into machine learning methods in a principled biologically-motivated fashion. 23 The lab will pursue several ambitious goals for both pro?ling and association questions.

The project will 24 (i) create methods to infer a continuously-updated reference alignment and tree encompassing all sequenced 25 prokaryotic genomes (half a million currently) to be used for pro?ling, (ii) build methods for ultra-sensitive sam- 26 ple pro?ling, (iii) use deep learning to connect data obtained using amplicon sequencing and metagenomics, 27 (iv) build discordance-aware phylogenetic measures of sample differentiation, and (v) develop machine learning 28 methods for associating a pro?led microbiome to phenotypes of interest such as disease.

These new methods 29 will draw on statistics, machine learning, discrete optimization, and high-performance computing.

Consistent 30 with the goals of MIRA, the project may explore new unforeseen opportunities if they ?t its general goals.