Imputing missing distances in molecular phylogenetics

Updating the bionomy and geographical distribution of Anopheles (Nyssorhynchus) albitarsis F: A vector of malaria parasites in northern South America

Anopheles albitarsis F is a putative species belonging to the Albitarsis Complex, recognized by rDNA, mtDNA, partial white gene, and microsatellites sequences. It has been reported from the island of Trinidad, Venezuela and Colombia, and incriminated as a vector of malaria parasites in the latter. This study examined mitochondrially encoded cytochrome c oxidase I (MT-CO1) sequences of An. albitarsis F from malaria-endemic areas in Colombia and Venezuela to understand its relations with other members of the Complex, revised and update the geographical distribution and bionomics of An. albitarsis F and explore hypotheses to explain its phylogenetic relationships and geographical expansion. Forty-five MT-CO1 sequences obtained in this study were analyzed to estimate genetic diversity and possible evolutionary relationships. Sequences generated 37 haplotypes clustered in a group where the genetic divergence of Venezuelan populations did not exceed 1.6% with respect to Colombian samples. Anopheles albitarsis F (π = 0.013) represented the most recent cluster located closer to An. albitarsis I (π = 0.009). Barcode gap was detected according to Albitarsis Complex lineages previously reported (threshold 0.014–0.021). Anopheles albitarsis F has a wide distribution in northern South America and might play an important role in the transmission dynamics of malaria due to its high expansion capacity. Future studies are required to establish the southern distribution of An. albitarsis F in Venezuela, and its occurrence in Guyana and Ecuador.

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 .