NORTH: a highly accurate and scalable Naive Bayes based ORTHologous gene clustering algorithm

Abstract

AbstractBackgroundThe principal objective of comparative genomics is inferring attributes of an unknown gene by comparing it with well-studied genes. In this regard, identifying orthologous genes plays a pivotal role as the orthologous genes remain less diverged in the course of evolution. However, identifying orthologous genes is often difficult, slow, and idiosyncratic, especially in the presence of multiplicity of domains in proteins, evolutionary dynamics (gene duplication, transfer, loss, introgression etc.), multiple paralogous genes, incomplete genome data, and for distantly related species where similarity is hard to recognize.MotivationAdvances in identifying orthologs have mostly been constrained to developing databases of genes or methods which involve computationally expensive BLAST search or constructing phylogenetic trees to infer orthologous relationships. These methods do not generally scale well and cannot analyze large amount of data from diverse organisms with high accuracy. Moreover, most of these methods involve manual parameter tuning, and hence are neither fully automated nor free from human bias.ResultsWe present NORTH, a novel, automated, highly accurate and scalable machine learning based orhtologous gene clustering method. We have utilized the biological basis and intuition of orthologous genes and made an effort to incorporate appropriate ideas from machine learning (ML) and natural language processing (NLP). We have discovered that the BLAST search based protocols deeply resemble a “text classification” problem. Thus, we employ the robust bag-of-words model accompanied by a Naive Bayes classifier to cluster the orthologous genes. We studied 1,255,877 genes in the largest 250 ortholog clusters from the KEGG database, across 3,880 organisms comprising the six major groups of life, namely, Archaea, Bacteria, Animals, Fungi, Plants and Protists. Despite having more than a million of genes on distantly related species with acute data imbalance, NORTH is able to cluster them with 98.48% Precision, 98.43% Recall and 98.44% F1 score, showing that automatic orthologous gene clustering can be both highly accurate and scalable. NORTH is available as a web interface with a server side application, along with cross-platform native applications (available at https://nibtehaz.github.io/NORTH/) – allowing queries based on individual genes.