Computer Science and Engineering, Department of



Adam Voshall

First Advisor

Jitender S. Deogun

Date of this Version



A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Computer Science, Under the Supervision of Professor Jitender S. Deogun. Lincoln, Nebraska: May, 2018.

Copyright (c) 2018 Adam Voshall


Accurate and comprehensive transcriptome assemblies lay the foundation for a range of analyses, such as differential gene expression analysis, metabolic pathway reconstruction, novel gene discovery, or metabolic flux analysis. With the arrival of next-generation sequencing technologies it has become possible to acquire the whole transcriptome data rapidly even from non-model organisms. However, the problem of accurately assembling the transcriptome for any given sample remains extremely challenging, especially in species with a high prevalence of recent gene or genome duplications, those with alternative splicing of transcripts, or those whose genomes are not well studied. This thesis provides a detailed overview of the strategies used for transcriptome assembly, including a review of the different statistics available for measuring the quality of transcriptome assemblies with the emphasis on the types of errors each statistic does and does not detect and simulation protocols to computationally generate RNAseq data that present biologically realistic problems such as gene expression bias and alternative splicing. Using such simulated RNAseq data, a comparison of the accuracy, strengths, and weaknesses of seven representative assemblers including de novo, genome-guided methods shows that all of the assemblers individually struggle to accurately reconstruct the expressed transcriptome, especially for alternative splice forms. Using a consensus of several de novo assemblers can overcome many of the weaknesses of individual assemblers, generating an ensemble assembly with higher accuracy than any individual assembler.

Advisor: Jitender S. Deogun