Analysis of Heterogeneous Genomic Samples Using Image Normalization and Machine Learning

Abstract

AbstractBackgroundAnalysis of heterogeneous populations such as viral quasispecies is one of the most challenging bioinformatics problems. Although machine learning models are becoming to be widely employed for the analysis of sequencing data associated with such populations, their straightforward application is impeded by multiple challenges associated with technological limitations and biases, difficulty of selection of relevant features and need to compare genomic datastes of different sizes and structures.MethodsWe propose a novel preprocessing approach to transform irregular genomic data into normalized image data. Such representation allows to restate the problems of classification and comparison of heterogeneous populations as image classification problems which can be solved using variety of available machine learning tools. We then apply the proposed approach to two important molecular epidemiology problems: inference of viral infection stage and detection of viral transmission clusters and outbreaks using next-generation sequencing data.ResultsThe infection staging method has been applied to HCV HVR1 samples collected from 108 recently and 257 chronically infected individuals. The SVM-based image classification approach achieved more than 95% accuracy for both recently and chronically HCV-infected individuals. Clustering has been performed on the data collected from 33 epidemiologically curated outbreaks, yielding more than 97% accuracy.AvailabilityThe developed software is freely available at https://bitbucket.org/adv_bio_coll/chronic_vs_clinic