Empirical accuracy bounds for next-generation sequencing variant calling workflows

"This thesis investigates the accuracy bounds imposed on alignment-based variant calling workflows due to inherent uncertainties introduced by sequencing platforms. In this work we will use simulated data to empirically quantify the maximum performance that can be expected for alignment and variant detection accuracy in a workflow. Short read sequencers are inherently incapable of producing reads that can be uniquely mapped to every position of the human reference genome, so errors are inevitable. We will analyze the repetitive content of several organisms, and estimate the maximum attainable alignment accuracy as a function of read length. Additionally, we will show that paired-end sequencing with large insert sizes (also referred to as ""mate-pair"" sequencing) is capable of mapping >99% of the human genome. We have developed a set of tools, NEAT (Next-generation Error Analysis Toolkit), which we use to create fault-injected genomic datasets. Our experiments utilize these datasets to showcase how the behavior of BWA and GATK workflows changes as a function of read lengths, error rates, quality scores, error types, and mutation types. We utilize these results to quantify the performance gains that can be expected by altering these properties of an NGS dataset. Our results highlight the sensitivity of alignment software to read lengths and error rates, and the sensitivity of variant callers to quality scores and structural variation."

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Stephens, Zachary Daniel

• Next-Generation Sequencing (NGS) Accuracy Benchmarking
• Next-Generation Error Analysis Toolkit (NEAT)
• Next-Generation Sequencing (NGS) Accuracy Bounds

Statement dc:rights

• Copyright 2015 Zachary Stephens

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/78801

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/78801