Bioinformatic Analysis of microRNA Genes in Free-Living and Parasitic Nematodes

153 Pages·2003·12.15 MB·English

Bioinformatic Analysis of microRNA
Genes in Free-Living and Parasitic
Nematodes
Rina Ahmed
November 2014
DISSERTATION
zur Erlangung des akademischen Grades der Doktorin
der Naturwissenschaften (Dr. rer. nat.)
eingereicht im Fachbereich Mathematik und Informatik
der Freien Universita¨t Berlin
Begutachtet von:
Prof. Dr. Martin Vingron
Prof. Dr. Kris Gunsalus
1. Gutachter: Prof. Dr. Martin Vingron
2. Gutachterin: Prof. Dr. Kris Gunsalus
Disputation: 26. Februar 2015
Preface
The work that led to this thesis is part of two collaborative projects in which I par-
ticipated. This thesis presents results from both projects. A panel of different bioin-
formatics and statistical methods suitable to analyze small RNA deep sequencing data
were identified and developed. Individual contributions for each project will be detailed
here:
Flexbar Project The work presented in Chapter 3 was published in the special
issue “Next-Generation Sequencing Approaches in Biology” in the journal Biology 1.
The Flexible Barcode and Adapter Remover (FLEXBAR) originated from the Flexible
Adapter Remover (FAR) and has been developed by Matthias Dodt in the bioinformat-
ics group of Dr. Christoph Dieterich. As part of this project, I developed the adapter
removal feature for SOLiD color space reads and focused on the application of small
RNA-seq in letter and color space. Additionally, I was involved in the design of FAR
and in the development of specific features of the subsequently added barcode detec-
tion function for demultiplexing. The final version of FLEXBAR (paper version) has
been extensively revised and enhanced by Johannes R¨ohr through the introduction of
novel and extended features, a cleanup in the source code, redesigned command-line
interface, and optimized parameter settings.
miRNA Project The bioinformatics workflow and the analysis and results presented
in Chapter 2 and 4 were published in Genome Biology and Evolution 2. As part of
this collaborative project, I designed and performed all computational experiments.
The experimental data sets were generated in the group of Dr. Christoph Dieterich
at the Berlin Institute for Medical Systems Biology (BIMSB) which is part of the
Max-Delbru¨ck-Center for Molecular Medicine (MDC). The total RNA of the parasite
samples (Strongyloides ratti) were kindly provided by our collaborator PD. Dr. Norbert
W. Brattig from the Bernhard Nocht Institute for Tropical Medicine in Hamburg. All
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next-generation sequencing was performed in the group of Dr. Wei Chen at BIMSB.
Acknowledgements The research presented in this thesis was funded by the MDC-
NYU Exchange Program and was carried out at BIMSB in the group of Dr. Christoph
Dieterich and the Center for Genomics and Systems Biology at the New York Univer-
sity (NYU) in the group of Prof. Dr. Kris Gunsalus. In the following, I would like to
thank all people who have supported and helped me throughout my PhD studies:
First of all, I would like to thank my supervisor Christoph Dieterich for giving me the
opportunity to pursue this research in his lab and introducing me to the fascinating
world of next-generation sequencing. I am very grateful for his ideas, support, and
fruitful discussions throughout the years and for giving me the opportunity to attend
international conferences. I especially want to thank my co-supervisor Kris Gunsalus
for giving me the chance to work in a very stimulating research environment and tightly
connected with the wet lab group of Fabio Piano. Kris provided a creative and open
minded working environment and I am very grateful for her dedication and support
inside and outside the lab. I would also like to thank Martin Vingron for taking the time
to supervise my PhD thesis as my University advisor. Furthermore, I am very grateful
to all present and former members of the Dietrich, Gunsalus, and Piano groups and
all people from BIMSB for creating this inspiring working atmosphere with plenty of
joyful coffee breaks. Special thanks goes to Nikolaus Rajewsky and Jutta Steinko¨tter
for their guidance and dedication and for providing this excellent research program.
Moreover, I have to thank Jennifer Stewart and Sabrina Deter for helping me with all
organizational issues and loads of paper work. Last but not least, I would like to thank
my friends and family for their tremendous support and patience.
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Contents
1 Introduction 1
1.1 Objectives and Thesis Structure . . . . . . . . . . . . . . . . . . . . . . 1
1.2 The Animal Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.1 Caenorhabditis elegans . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2.2 Pristionchus pacificus . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.3 Relationship with Parasitic Nematodes . . . . . . . . . . . . . . . 5
1.3 Post-transcriptional Regulation of Gene Expression . . . . . . . . . . . . 7
1.3.1 microRNA Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4 Next-Generation Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.4.1 Illumina/Solexa System . . . . . . . . . . . . . . . . . . . . . . . 15
1.4.2 ABI SOLiDTM System . . . . . . . . . . . . . . . . . . . . . . . . 17
1.4.3 Small RNA Sequencing . . . . . . . . . . . . . . . . . . . . . . . 19
2 Materials and Methods 23
2.1 Small RNA Sequencing . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.1.1 Nematode Strains and Culture . . . . . . . . . . . . . . . . . . . 24
2.1.2 Total RNA Isolation and Small RNA Library Generation . . . . 25
2.2 Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.2.1 Small RNA Sequencing Data . . . . . . . . . . . . . . . . . . . . 25
2.2.2 Publicly Available Data . . . . . . . . . . . . . . . . . . . . . . . 26
2.3 Bioinformatics Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.3.1 Preprocessing of Small RNA Sequencing Data . . . . . . . . . . . 27
2.3.1.1 Quality Filtering . . . . . . . . . . . . . . . . . . . . . . 28
2.3.1.2 Barcode Detection . . . . . . . . . . . . . . . . . . . . . 29
2.3.1.3 Adapter Removal . . . . . . . . . . . . . . . . . . . . . 30
2.3.2 Mapping of Short Sequencing Reads to a Reference . . . . . . . . 32
2.3.3 Identification of microRNA Genes from Small RNA-Seq Data . . 34
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CONTENTS
2.3.3.1 Quantification of microRNA Expression Levels . . . . . 35
2.3.3.2 Identification of Novel microRNA Genes . . . . . . . . . 35
2.3.4 Differential Expression Analysis . . . . . . . . . . . . . . . . . . . 38
2.3.4.1 Normalizing microRNA Sequencing Data . . . . . . . . 38
2.3.4.2 Defining Differential Expression . . . . . . . . . . . . . 40
2.3.4.3 Correction for Multiple Hypothesis Testing . . . . . . . 42
2.3.5 Inference of microRNA Gene Families and Phylogeny . . . . . . 43
2.3.5.1 Grouping of microRNA Gene Families . . . . . . . . . . 44
2.3.5.2 Multiple Sequence-structure Alignments of RNA . . . . 45
2.3.5.3 Building Phylogenetic Trees . . . . . . . . . . . . . . . 46
2.3.5.4 Performance Evaluation . . . . . . . . . . . . . . . . . . 49
2.3.6 Single-Mutation Seed Network . . . . . . . . . . . . . . . . . . . 50
3 FLEXBAR - Flexible Barcode and Adapter Processing for Next-
Generation Sequencing 51
3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.2 Program Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.2.1 Algorithmic Implementation . . . . . . . . . . . . . . . . . . . . . 54
3.2.2 Trim-end Modes . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.2.3 Quality Clipping and Read Filtering . . . . . . . . . . . . . . . . 56
3.3 Program Usage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.4 Program Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.4.1 Adapter Removal from microRNA Short Reads in Color Space . 59
4 Conserved microRNAs are Candidate Post-Transcriptional Regula-
tors of Developmental Arrest in Free-Living and Parasitic Nematodes 63
4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.2 Sequencing of microRNAs from Three Nematodes . . . . . . . . . . . . . 65
4.3 Unbiased Identification of Novel microRNA Genes . . . . . . . . . . . . 67
4.4 Most microRNA Genes Are Not Conserved among Distantly Related
Nematodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.5 Evaluation of microRNA Homology Assignment . . . . . . . . . . . . . . 72
4.6 microRNA Expression Changes from Sequencing Data Agree with Pub-
lished qRT-PCR Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.7 Differential Expression Analysis Identifies Cross-Species Candidate Reg-
ulators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
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CONTENTS
4.8 P. pacificus miR-34 Seed Neighbors are Upregulated in Dauer Larvae . 85
5 Discussion 89
5.1 FLEXBAR - Leading Solution in Barcode and Adapter Processing . . . 90
5.2 Comprehensive Bioinformatic Analysis Identifies Cross-Species Candi-
date Regulators in Nematodes . . . . . . . . . . . . . . . . . . . . . . . . 91
5.3 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
5.4 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
References 99
Abbreviations 119
Summary 123
Zusammenfassung 125
Appendix A - Supplemental Material 129
Appendix B - Supplemental CD 133
Curriculum Vitae 135
Selbst¨andigkeitserkl¨arung 139
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