Robust Estimation of Evolutionary Distances with Information Theory

Machine learning based imputation techniques for estimating phylogenetic trees from incomplete distance matrices

BACKGROUND

With the rapid growth rate of newly sequenced genomes, species tree inference from genes sampled throughout the whole genome has become a basic task in comparative and evolutionary biology. However, substantial challenges remain in leveraging these large scale molecular data. One of the foremost challenges is to develop efficient methods that can handle missing data. Popular distance-based methods, such as NJ (neighbor joining) and UPGMA (unweighted pair group method with arithmetic mean) require complete distance matrices without any missing data.

RESULTS

We introduce two highly accurate machine learning based distance imputation techniques. These methods are based on matrix factorization and autoencoder based deep learning architectures. We evaluated these two methods on a collection of simulated and biological datasets. Experimental results suggest that our proposed methods match or improve upon the best alternate distance imputation techniques. Moreover, these methods are scalable to large datasets with hundreds of taxa, and can handle a substantial amount of missing data.

CONCLUSIONS

This study shows, for the first time, the power and feasibility of applying deep learning techniques for imputing distance matrices. Thus, this study advances the state-of-the-art in phylogenetic tree construction in the presence of missing data. The proposed methods are available in open source form at https://github.com/Ananya-Bhattacharjee/ImputeDistances .

On the complexity measures of mutation hotspots in human TP53 protein

The role of sequence complexity in 23 051 somatic missense mutations including 73 well-known mutation hotspots across 22 major cancers was studied in human TP53 proteins. A role for sequence complexity in TP53 protein mutations is suggested since (i) the mutation rate significantly increases in low amino acid pair bias complexity; (ii) probability distribution complexity increases following single point substitution mutations and strikingly increases after mutation at the mutation hotspots including six detectable hotspot mutations (R175, G245, R248, R249, R273, and R282); and (iii) the degree of increase in distribution complexity is significantly correlated with the frequency of missense mutations (r = -0.5758, P < 0.0001) across 20 major types of solid tumors. These results are consistent with the hypothesis that amino acid pair bias and distribution probability may be used as novel measures for protein sequence complexity, and the degree of complexity is related to its susceptibility to mutation, as such, it may be used as a predictor for modeling protein mutations in human cancers.