Transcriptomic analysis of Douglas-fir megagametophyte development and abortion by Ian Boyes B.Sc., University of Victoria, 2009 A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE in the Department of Biology (cid:13)c Ian Boyes, 2013 University of Victoria All rights reserved. This thesis may not be reproduced in whole or in part, by photocopying or other means, without the permission of the author. ii Transcriptomic analysis of Douglas-fir megagametophyte development and abortion by Ian Boyes B.Sc., University of Victoria, 2009 Supervisory Committee Dr. Patrick von Aderkas, Co-Supervisor (Department of Biology) Dr. Ju¨rgen Ehlting, Co-Supervisor (Department of Biology) Dr. Steve Perlman, Departmental Member (Department of Biology) iii Supervisory Committee Dr. Patrick von Aderkas, Co-Supervisor (Department of Biology) Dr. Ju¨rgen Ehlting, Co-Supervisor (Department of Biology) Dr. Steve Perlman, Departmental Member (Department of Biology) ABSTRACT Douglas-fir develops a megagametophyte regardless of the pollination state of the ovule, whereas many other conifers develop a megagametophye in response to polli- nation. Megagametophytes in unfertilized ovules degrade two weeks following fertil- ization of the surrounding population. This is mediated by programmed cell death (PCD). Pollinated and unpollinated megagametophytes were dissected from Douglas- fir cones and extracted for RNA, which was then used as input for sequencing. A transcriptome was assembled from this data and expression levels were calculated. The data were fitted to quadratic regressions to produce coexpression groups. There is no clear upregulation of PCD effectors in the unpollinated megagametophyte. Po- tential regulators of megagametophyte fate are present in the data. Some are as- sociated with ABA signalling and proanthocyanadin biosynthesis while others share similarity to known regulators of PCD. Seed development processes are represented iv in the expression data, which support current knowledge of conifer seed development and provide targets for research. v Contents Supervisory Committee ii Abstract iii Table of Contents v List of Tables ix List of Figures x List of Abbreviations xv Acknowledgements xviii 1 Introduction 1 1.1 Douglas-fir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 The Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.2 Douglas-fir Reproduction . . . . . . . . . . . . . . . . . . . . . 2 1.1.3 Embryogenesis . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.1.4 Seed Abortion . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.2 Programmed Cell Death . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.2.1 Programmed Cell Death in Animals and Yeast . . . . . . . . . 11 1.2.2 Programmed Cell Death in Plants . . . . . . . . . . . . . . . . 19 1.3 Objectives and Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . 27 vi 2 Analytical Steps in RNA-Seq 30 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.1.1 Next-Generation Sequencing . . . . . . . . . . . . . . . . . . . 30 2.1.2 RNA-Seq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.2 Computing Considerations . . . . . . . . . . . . . . . . . . . . . . . . 36 2.2.1 The Linux Environment . . . . . . . . . . . . . . . . . . . . . 36 2.2.2 Computing Strategies . . . . . . . . . . . . . . . . . . . . . . . 38 2.3 Data Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 2.3.1 FASTA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 2.3.2 FASTQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 2.3.3 SAM and BAM . . . . . . . . . . . . . . . . . . . . . . . . . . 44 2.3.4 File Interconversion . . . . . . . . . . . . . . . . . . . . . . . . 46 2.4 Processing Read Data . . . . . . . . . . . . . . . . . . . . . . . . . . 48 2.4.1 Read Data Assessment . . . . . . . . . . . . . . . . . . . . . . 48 2.4.2 Read Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 2.5 Transcriptome Assembly . . . . . . . . . . . . . . . . . . . . . . . . . 58 2.5.1 The Overlap-Layout-Consensus Method . . . . . . . . . . . . 58 2.5.2 The De Bruijn Graph Method . . . . . . . . . . . . . . . . . . 60 2.5.3 Transcriptome Assemblers . . . . . . . . . . . . . . . . . . . . 61 2.5.4 Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 2.5.5 Further Assembly . . . . . . . . . . . . . . . . . . . . . . . . . 67 2.6 Annotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 2.6.1 BLAST . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 2.6.2 Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 2.7 Expression Profiling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 2.7.1 Read Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . 71 vii 2.7.2 Read Counting . . . . . . . . . . . . . . . . . . . . . . . . . . 72 2.7.3 Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 2.7.4 Differential Expression . . . . . . . . . . . . . . . . . . . . . . 76 2.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 3 Transcriptomics of Douglas-fir Ovular Development 80 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 3.1.1 Seed Development in Douglas-fir . . . . . . . . . . . . . . . . . 80 3.1.2 RNA-Seq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 3.2.1 Material Collection . . . . . . . . . . . . . . . . . . . . . . . . 82 3.2.2 Transcriptome Sequencing . . . . . . . . . . . . . . . . . . . . 85 3.2.3 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . 85 3.2.4 De novo Assembly . . . . . . . . . . . . . . . . . . . . . . . . 86 3.2.5 Annotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 3.2.6 Read Mapping and Counting . . . . . . . . . . . . . . . . . . 88 3.2.7 Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 3.2.8 Differential Expression Analysis . . . . . . . . . . . . . . . . . 88 3.2.9 Quadratic Regression . . . . . . . . . . . . . . . . . . . . . . . 89 3.2.10 Finding PCD-related Genes . . . . . . . . . . . . . . . . . . . 92 3.2.11 Heat Map Generation . . . . . . . . . . . . . . . . . . . . . . 92 3.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 93 3.3.1 Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 3.3.2 Comparison of Fertilized and Unfertilized Megagametophytes . 99 3.3.3 Prefertilization and Early Embryogenesis . . . . . . . . . . . . 105 3.3.4 Regulators of Embryo Developmcent . . . . . . . . . . . . . . 112 3.3.5 Accumulation of Seed Reserves . . . . . . . . . . . . . . . . . 116 viii 3.3.6 Preparation for Dormancy . . . . . . . . . . . . . . . . . . . . 119 3.3.7 Vegetative and Reproductive Tissues . . . . . . . . . . . . . . 121 3.3.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 Appendix 128 References 149 ix List of Tables 1.1 Possible genes of interest in Douglas-fir PCD during abortion . . . . 28 2.1 Quality scoring systems used in the FASTQ format. . . . . . . . . . 45 2.2 The data fields of a SAM line . . . . . . . . . . . . . . . . . . . . . 47 3.1 Biorad Experion RNA analysis . . . . . . . . . . . . . . . . . . . . . 94 3.2 Read counts assessed by FastQC. These include the counts from the raw libraries and the reads retained as pairs or lone mates after trim- ming. Counts are in millions. . . . . . . . . . . . . . . . . . . . . . . 94 3.3 Transcripts fitting each regression in pollinated samples. . . . . . . 98 3.4 Transcripts fitting each regression in unpollinated samples. . . . . . 99 A.1 Multi k-mer assembly results . . . . . . . . . . . . . . . . . . . . . . 129 A.2 Number of hits for each BLAST database queried . . . . . . . . . . 130 A.3 Bowtie alignment rates . . . . . . . . . . . . . . . . . . . . . . . . . 131 A.4 Pairwise differential expression analysis . . . . . . . . . . . . . . . . 132 x List of Figures 1.1 The inner bract and scale surface . . . . . . . . . . . . . . . . . . . 4 1.2 The outer bract and scale surface . . . . . . . . . . . . . . . . . . . 4 1.3 The Douglas-fir seed with well-developed archegonia . . . . . . . . . 5 1.4 Themegagametophytewhenthearchegoniaareformedandwhenthe central cell is formed . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.5 An ovule ready for fertilization . . . . . . . . . . . . . . . . . . . . . 7 1.6 The Douglas-fir seed with a developing embryo . . . . . . . . . . . . 8 2.1 Illumina cluster generation . . . . . . . . . . . . . . . . . . . . . . . 34 2.2 Illumina paired-end sequencing . . . . . . . . . . . . . . . . . . . . . 34 2.3 The basis of RNA-seq . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.4 Steps in an RNA-seq workflow . . . . . . . . . . . . . . . . . . . . . 37 2.5 A sample of FASTA file content . . . . . . . . . . . . . . . . . . . . 41 2.6 Two lines of a FASTQ file . . . . . . . . . . . . . . . . . . . . . . . 43 2.7 Sample box plots of per-base quality output from FastQC . . . . . . 51 2.8 Sample per-base nucleotide content from FastQC . . . . . . . . . . . 52 2.9 Possible events during Illumina sequencing that can be corrected by read filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 2.10 The OLC method of sequence assembly . . . . . . . . . . . . . . . . 59 2.11 The de Bruijn Graph method of sequence assembly . . . . . . . . . 62 2.12 A subgraph resulting from large-scale collapsing of DBGs . . . . . . 63