Master’s Thesis Presentation • Bioinformatics • MT-MAG: Accurate and Interpretable Machine Learning For Complete/Partial Taxonomic Assignments of Metagenome-assembled GenomesExport this event to calendar

Thursday, May 12, 2022 — 2:00 PM EDT

Please note: This master’s thesis presentation will be given online.

Wanxin Li, Master’s candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Lila Kari, Yaoliang Yu

We propose MT-MAG, a novel machine learning-based software tool for the complete or partial hierarchically-structured taxonomic classification of metagenome-assembled genomes (MAGs). MT-MAG is capable of classifying large and diverse metagenomic datasets: a total of 245.68 Gbp in the training sets, and 9.6 Gbp in the test sets analyzed in this study. MT-MAG is, to the best of our knowledge, the first machine learning method for taxonomic assignment of metagenomic data that offers a “partial classification” option. MT-MAG outputs complete or partial classification paths, and interpretable numerical classification confidences of its classifications, at all taxonomic ranks.

To assess the performance of MT-MAG, we define a “weighted classification accuracy,” with a weighting scheme reflecting the fact that partial classifications at different ranks are not equally informative. For the two benchmarking datasets analyzed (genomes from human gut microbiome species, and bacterial and archaeal genomes assembled from cow rumen metagenomic sequences), MT-MAG achieves an average of 80.13% in weighted classification accuracy. At the Species level, MT-MAG outperforms DeepMicrobes, the only other comparable software tool, by an average of 35.75% in weighted classification accuracy.

In addition, MT-MAG is able to completely classify an average of 67.7% of the sequences at the Species level, compared with DeepMicrobes which only classifies 47.45%. Moreover, MT-MAG provides additional information for sequences that are not completely classified at the Species level, resulting in the partial or complete classification of 95.15% of the genomes in the datasets analyzed. In particular, MT-MAG classifies, on average, 88.84% of the test sequences to the Phylum level, 88.39% to the Class level, 86.81% to the Order level, 81.17% to the Family level, and 71.13% to the Genus level.


To join this master’s thesis presentation on Zoom, please go to https://uwaterloo.zoom.us/j/93859643471.

Location 
Online master’s thesis presentation
200 University Avenue West

Waterloo, ON N2L 3G1
Canada
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