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A Machine Learning Approach to Genome Assessment

Adam Thrash

Department of Computer Science and Engineering; Institute for Genomics, Biocomputing & Biotechnology

Abstract

A key use of high throughput sequencing technology is the sequencing and assembly of full genome sequences. These genome assemblies are commonly assessed using statistics relating to contiguity of the assembly. Measures of contiguity are not strongly correlated with information about the biological completion or correctness of the assembly, and a commonly reported metric, N50, can be misleading. Over the past ten years, multiple research groups have rejected the overuse of N50 and sought to develop more informative metrics. This research seeks to create a ranking method that includes biologically relevant information about the genome, such as completeness and correctness of the genome. Approximately eight hundred genomes were initially selected, and information about their completeness, contiguity, and correctness was gathered using publicly available tools. Using this information, these genomes were scored by subject matter experts. This rating system was explored using supervised machine learning techniques. A number of classifiers and regressors were tested using cross validation.

Two metrics were explored in this research. First, a metric that describes the distance to the ideal genome was created as a way to explore the incorporation of human subject matter expert knowledge into the genome assembly assessment process. Second, random forest regression was found to be the method of supervised learning with the highest scores. A model created by an optimized random forest regressor was saved, and a tool was created to load the saved model and rank genomes provided by the end user. These metrics both serve as ways to incorporate human subject matter expert knowledge into genome assembly assessment.

Introduction

Biological Background

Sequencing

Assembly