Species tree estimation is frequently based on phylogenomic approaches that use multiple genes from throughout the genome. However, for a combination of reasons (ranging from sampling biases to more biological causes, as in gene birth and loss), gene trees are often incomplete, meaning that not all species of interest have a common set of genes. Incomplete gene trees can potentially impact the accuracy of phylogenomic inference. We, for the first time, introduce the problem of imputing the quartet distribution induced by a set of incomplete gene trees, which involves adding the missing quartets back to the quartet distribution. We present QT-GILD, an automated and specially tailored unsupervised deep learning technique, accompanied by cues from natural language processing (NLP), which learns the quartet distribution in a given set of incomplete gene trees and generates a complete set of quartets accordingly. QT-GILD is a general-purpose technique needing no explicit modeling of the subject system or reasons for missing data or gene tree heterogeneity. Experimental studies on a collection of simulated and empirical data sets suggest that QT-GILD can effectively impute the quartet distribution, which results in a dramatic improvement in the species tree accuracy. Remarkably, QT-GILD not only imputes the missing quartets but it can also account for gene tree estimation error. Therefore, QT-GILD advances the state-of-the-art in species tree estimation from gene trees in the face of missing data. QT-GILD is freely available in open source form at https://github.com/pythonLoader/QT-GILD..

Covid-19 pandemic, caused by the sars-cov-2 strain of coronavirus, has affected millions of people all over the world and taken thousands of lives. It is of utmost importance that the character of this deadly virus be studied and its nature be analysed. We present here an analysis pipeline comprising phylogenetic analysis on strains of this novel virus to track its evolutionary history among the countries uncovering several interesting relationships, followed by a classification exercise to identify the virulence of the strains and extraction of important features from its genetic material that are used subsequently to predict mutation at those interesting sites using deep learning techniques. In a nutshell, we have prepared an analysis pipeline for hCov genome sequences leveraging the power of machine intelligence and uncovered what remained apparently shrouded by raw data.

This study leveraged the phylogenetic analysis of more than 10K strains of novel coronavirus (SARS-CoV-2) from 67 countries. Due to the requirement of high-end computational power for phylogenetic analysis, we leverage a fast yet highly accurate alignment-free method to develop the phylogenetic tree out of all the strains of novel coronavirus. K-Means clustering and PCA-based dimension reduction technique were used to identify a representative strain from each location. The resulting phylogenetic tree was able to highlight evolutionary relationships of SARS-CoV-2 genome and, subsequently, linked to the interpretation of facts and figures across the globe for the spread of COVID-19. Our analysis revealed that the geographical boundaries could not be explained by the phylogenetic analysis of novel coronavirus as it placed different countries from Asia, Europe and the USA in very close proximity in the tree. Instead, the commute of people from one country to another is the key to the spread of COVID-19. We believe our study will support the policymakers to contain the spread of COVID-19 globally.

No abstract

Species tree estimation is frequently based on phylogenomic approaches that use multiple genes from throughout the genome. However, for a combination of reasons (ranging from sampling biases to more biological causes, as in gene birth and loss), gene trees are often incomplete, meaning that not all species of interest have a common set of genes. Incomplete gene trees can potentially impact the accuracy of phylogenomic inference. We, for the first time, introduce the problem of imputing the quartet distribution induced by a set of incomplete gene trees, which involves adding the missing quartets back to the quartet distribution. We present QT-GILD, an automated and specially tailored unsupervised deep learning technique, accompanied by cues from natural language processing (NLP), which learns the quartet distribution in a given set of incomplete gene trees and generates a complete set of quartets accordingly. QT-GILD is a general-purpose technique needing no explicit modeling of the subject system or reasons for missing data or gene tree heterogeneity. Experimental studies on a collection of simulated and empirical data sets suggest that QT-GILD can effectively impute the quartet distribution, which results in a dramatic improvement in the species tree accuracy. Remarkably, QT-GILD not only imputes the missing quartets but it can also account for gene tree estimation error. Therefore, QT-GILD advances the state-of-the-art in species tree estimation from gene trees in the face of missing data. QT-GILD is freely available in open source form at https://github.com/pythonLoader/QT-GILD.