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Comparison of DNA Sequence Assembly Algorithms Using Mixed Data Sources Comparison of DNA Sequence Assembly Algorithms Using Mixed Data Sources A Thesis Submitted to the College of Graduate Studies and Research in Partial Fulfillment of the Requirements for the degree of Master of Science in the Department of Computer Science University of Saskatchewan Saskatoon By Tejumoluwa Abegunde c Tejumoluwa Abegunde, April/2010. All rights reserved. Permission to Use In presenting this thesis in partial fulfilment of the requirements for a Postgraduate degree from the University of Saskatchewan, I agree that the Libraries of this University may make it freely available for inspection. I further agree that permission for copying of this thesis in any manner, in whole or in part, for scholarly purposes may be granted by the professor or professors who supervised my thesis work or, in their absence, by the Head of the Department or the Dean of the College in which my thesis work was done. It is understood that any copying or publication or use of this thesis or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to the University of Saskatchewan in any scholarly use which may be made of any material in my thesis. Requests for permission to copy or to make other use of material in this thesis in whole or part should be addressed to: Head of the Department of Computer Science 176 Thorvaldson Building 110 Science Place University of Saskatchewan Saskatoon, Saskatchewan Canada S7N 5C9 i Abstract DNA sequence assembly is one of the fundamental areas of bioinformatics. It involves the cor- rect formation of a genome sequence from its DNA fragments (\reads") by aligning and merging the fragments. There are different sequencing technologies | some support long DNA reads and the others, shorter DNA reads. There are sequence assembly programs specifically designed for these different types of raw sequencing data. This work explores and experiments with these different types of assembly software in order to compare their performance on the type of data for which they were designed, as well as their per- formance on data for which they were not designed, and on mixed data. Such results are useful for establishing good procedures and tools for sequence assembly in the current genomic environment where read data of different lengths are available. This work also investigates the effect of the presence or absence of quality information on the results produced by sequence assemblers. Five strategies were used in this research for assembling mixed data sets and the testing was done using a collection of real and artificial data sets for six bacterial organisms. The results show that there is a broad range in the ability of some DNA sequence assemblers to handle data from various sequencing technologies, especially data other than the kind they were designed for. For exam- ple, the long-read assemblers PHRAP and MIRA produced good results from assembling 454 data. The results also show the importance of having an effective methodology for assembling mixed data sets. It was found that combining contiguous sequences obtained from short-read assemblers with long DNA reads, and then assembling this combination using long-read assemblers was the most appropriate approach for assembling mixed short and long reads. It was found that the results from assembling the mixed data sets were better than the results obtained from separately assembling individual data from the different sequencing technologies. DNA sequence assemblers which do not depend on the availability of quality information were used to test the effect of the presence of quality values when assembling data. The results show that regardless of the availability of quality information, good results were produced in most of the assemblies. In more general terms, this work shows that the approach or methodology used to assemble DNA sequences from mixed data sources makes a lot of difference in the type of results obtained, and that a good choice of methodology can help reduce the amount of effort spent on a DNA sequence assembly project. ii Acknowledgements I would like to formally thank: My supervisor, Dr. Anthony Kusalik for providing me with this opportunity, and for his hard work, patient encouragement, and guidance throughout my studies. My committee members, Dr. Ian Mcquillan and Dr. Barry Ziola for their guidance and support, and Dr. Andrew Sharpe for serving as my external examiner. My fellow lab colleagues, for their friendship and support. Good luck to each of you in your future aspirations. My parents, for their unending love and support in all my efforts and aspirations. Also to my siblings, David and Dammy for their love and support. My best friend, Austin Ogun for your love and support. Also to Toyin Ake-Johnson for always ensuring I smile even when it seemed tough. My friends that supported me during the course of my studies, I cannot mention all, but I appreciate you all. iii I humbly dedicate this thesis to God for grace, strength, and guidance. iv Contents Permission to Use i Abstract ii Acknowledgements iii Contents v List of Tables vii List of Figures x List of Abbreviations xiii 1 Introduction 1 1.1 Thesis Organization . 3 2 Background Information 4 2.1 DNA Sequencing . 4 2.2 Sequence Assembly . 7 2.3 Sequence Assemblers . 8 2.3.1 Long-read assemblers . 11 2.3.2 Short-read assemblers . 17 2.4 Finishing Phase . 21 2.5 Objectives of the research . 21 3 Data and Methodology 23 3.1 Data . 23 3.1.1 Real Sequencing Data . 24 3.1.2 Artificial Sequencing Data . 27 3.1.3 Summary of Data Sets . 32 3.2 Methodology . 34 3.2.1 Accuracy of results . 34 3.2.2 Execution time . 37 3.2.3 Memory usage . 37 3.2.4 System Dependencies . 37 3.2.5 Restrictions and constraints . 38 3.3 Effect of Quality values on Accuracy of Contigs . 38 3.4 Statistical Analysis . 38 3.5 Computer Resources . 39 4 Results 42 4.1 Assembling short reads using long-read assemblers . 42 4.2 Assembling long reads on short-read assemblers . 47 4.3 Assembling mixed data sets . 51 4.3.1 Assembling 454 reads merged with Sanger reads . 51 4.3.2 Assembling Illumina reads merged with Sanger reads . 52 4.3.3 Assembling Illumina reads merged with 454 reads . 54 4.3.4 Assembling Illumina contigs merged with 454 contigs . 54 4.3.5 Assembling 454 contigs merged with Sanger reads . 55 v 4.3.6 Assembling Illumina contigs merged with Sanger reads . 56 4.3.7 Assembling merged 454, Illumina and Sanger reads . 57 4.4 The effect of quality values when assembling DNA reads . 57 4.4.1 Statistical results for Sanger data with and without quality data . 57 4.4.2 Statistical results for Illumina data with and without quality data . 58 4.4.3 Statistical results for 454 data with and without quality data . 59 5 Discussion 62 5.1 Conclusions and Recommendations . 62 5.2 Related Work . 66 5.3 Future Work . 67 References 69 A Tables of Data sets 72 B Tables of Results 73 C Graphs 94 D Graphs 112 E Statistical Results 127 vi List of Tables 3.1 Characteristics of the datasets of real Illumina data. 24 3.2 Characteristics of the datasets of real Sanger data. 25 3.3 Characteristics of the datasets of real 454 data. 25 3.4 Characteristics of the genome sequences for the organisms. 31 3.5 Characteristics of the datasets of artificial Sanger data. 31 3.6 Characteristics of the datasets of artificial 454 data. 32 3.7 Characteristics of the datasets of artificial Illumina data. 32 3.8 Summary of all the data sets used in this work. 33 3.9 An example of the SPSS output for the independent samples test. 40 3.10 An example of one-way ANOVA output from SPSS when comparing the means for genome coverage between five assemblers. 40 3.11 An example of Games-Howell post-hoc output from SPSS when comparing the means for genome coverage between five assemblers (one-way ANOVA test). The results are an extract from Table E.17 of Appendix E. 41 4.1 Results to show which long-read assemblers can handle short reads. An \×" indicates that the assembler was not able to successfully work with this type of data, while a check mark indicates that it could. 43 4.2 Results from assembling 454 data using short- and long-read assemblers. The results from the long-read assemblers are shown in bold font. 44 4.3 Results from assembling Illumina data using short- and long-read assemblers. The results from the long-read assemblers are in bold font. 46 4.4 Results to show which short-read assemblers can handle long reads. An \X" indicates that the assembler successfully works, while a \×" indicates otherwise. 48 4.5 Results for running short- and long-read assemblers with Sanger data. The results from the short-read assemblers are in bold. 50 4.6 Results for assembling 454 reads merged with Sanger reads. 52 4.7 Results for assembling Illumina reads merged with Sanger reads. 53 4.8 Results for assembling Illumina reads merged with 454 reads. 55 4.9 Results for assembling Illumina contigs merged with 454 contigs using PHRAP. 56 4.10 Results for assembling merged 454, Illumina and Sanger reads. 58 4.11 Results for assembling real Sanger reads to test the effect of quality values. 59 4.12 Results for assembling real Illumina reads to test the effect of quality values.
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