Bioinformatical approaches to RNA structure prediction & Sequencing of an ancient human genome By Stinus Lindgreen A dissertation submitted to the University of Copenhagen in partial fulfillment of the requirements for the degree of Ph.D. at the Faculty of Science, University of Copenhagen. Submitted January 29, 2010 Supervisors: Professor Anders Krogh Paul P. Gardner, Ph.D (part 1) Assistant professor Jakob Skou Pedersen (part 2) DEPARTMENTOFBIOLOGY UNIVERSITYOFCOPENHAGEN Preface The work presented in this dissertation sums up the research I have done as a Ph.D student for the past 3 years. During these years, I have been financed by a Faculty stipend from the Faculty of Science at the University of Copenhagen. My initial work was financed by a Novo Scholarship. The work was carried out at the Department of Biology, University of Copenhagen, with a brief stay at the Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK. I have divided my dissertation into two main parts, reflecting the fact that my re- search has been focused on two distinct subjects. In the first part, I will give a brief introduction to the field of RNA secondary structure prediction and multiple alignment of structural RNAs. I will also present some first author papers focusing on this subject: My work on measuring basepair conservation in multiple alignments, the development of the algorithm MASTR for simultaneous alignment and structure prediction, and the webserver WAR for performing alignment and structure prediction of RNAs using an ensemble of methods. In the second part, I will give a short introduction to the field of next generation sequencing and the challenges these new technologies present. I will focus on mapping of reads to a reference genome and subsequent genotyping with focus on ancient DNA. Here, I will also present my first author papers on this subject: The development of the program SNPest for genotyping and SNP calling, and my work on the first ancient genome from a human individual. The dissertation will end with a conclusion where I will sum up my work and discuss possible future directions of research. Stinus Lindgreen January 2010 Contents Alignment and structure prediction of RNA Introduction . 1 Non-coding RNAs . 1 Brief introduction to sequence alignment . 3 Secondary structure prediction of a single RNA sequence . 7 Comparative structure prediction . 13 Simultaneous alignment and structure prediction . 16 Combining approaches . 23 Research Papers Chapter 1 . 31 Lindgreen S, Gardner PP and Krogh A (2006): Measuring covariation in RNA alignments: Physical realism improves information measures. Bioinformatics 22(24):2988-2995. Chapter 2 . 41 Lindgreen S, Gardner PP and Krogh A (2007): MASTR: Multiple alignment and structure prediction of non-coding RNAs using simu- lated annealing. Bioinformatics 23(24):3304-3311. Chapter 3 . 51 Torarinsson E and Lindgreen S (2008): WAR: Webserver for aligning structural RNAs. Nucleic Acids Research (Web Server Issue) W79-84. Sequencing and assembly of an ancient genome Introduction . 59 Next generation sequencing . 59 Ancient DNA . 62 Mapping reads to a reference genome . 65 Genotyping . 68 First diploid genome of an ancient human individual . 74 Research Papers Chapter 4 . 81 Lindgreen S, Krogh A and Pedersen JS (2010): SNPest: Estimating genotype using a probabilistic graphical model. Draft. Chapter 5 . 95 Rasmussen M, Yingrui L, Lindgreen S et al. (2010): Ancient Human Genome Sequence of an Extinct Palaeo-Eskimo. Nature (in press) Conclusion Concluding Remarks . 103 Acknowledgements . 105 Curriculum Vitae . 107 Introduction In this first part of my dissertation, I will focus on RNA bioinformatics. I will intro- duce the field of RNA secondary structure prediction and show the connection to RNA multiple alignment. This will lead to a presentation of three first author papers I have in this field, covering different aspects of RNA bioinformatics: Measuring covariation in RNA alignments, simultaneously aligning and predicting the structure of multiple RNA sequences, and combining various RNA structure prediction methods in a webserver. Non-coding RNAs In recent years, it has become more and more evident that RNA is of much more im- portance in living organisms than merely being the mediator of information between the DNA and protein levels. RNA has the ability to both carry genetic information – known from e.g. RNA virus genomes – and act as catalytic agents in the cell as seen in many cases such as Ribosomal RNAs (rRNA) and transfer RNAs (tRNA) important for protein formation, and the RNA parts important in the spliceosome. Many important functional RNAs have been discovered in recent years, making the study of these non-coding RNAs (ncRNA) highly relevant (see e.g. the excellent review in [BFF+05]). The fact that RNAs are able to both carry genetic information and catalyze biochem- ical processes has led researchers to propose the RNA World Hypothesis [PJP98, JPP98, Sza99]: The idea that the first life on earth was based on RNA and not protein, since RNA is a simpler polymere to form and can carry out the role of both DNA and protein (although less efficiently). This could also explain why ncRNAs play a key role in many crucial pathways, and why thousands of the human genes do not encode proteins but are transcribed into functional RNA molecules [WHL+05]. Analyzing RNA and being able to predict the secondary structure could therefore present us with key insights into how cells function. The field of bioinformatical analysis of RNA molecules is therefore growing rapidly. One of the important aspects of ncRNA – as is also the case with proteins – is that the function is intimately tied to the structure of the molecule, thus making methods for RNA structure prediction important. Through evolution, the sequences of related RNAs 1 2 can diverge at the nucleotide level while keeping the structure intact. Pure sequence comparison methods therefore fail when applied to ncRNAs. With RNA, you distinguish between primary, secondary and tertiary structure as illustrated in Fig. 1. The primary structure of an RNA is the linear sequence of nucleotides that make up the RNA molecule, normally written in the 5’ to 3’ direction. The four nucleotides adenine, cytosine, guanine, and uracil are abbreviated A, C, G, and U. The pyrimidines (C and U) are the smallest of the nucleotides with a single nitrogen–containing ring, while the purines (A and G) are larger with two nitrogen–containing rings. The nucleotides can form base pairs by hydrogen bonds. These base pairing inter- actions constitute the secondary structure of an RNA. The base pairs are mainly formed between adenine and uracil and between guanine and cytosine, which are called the Watson–Crick or classical base pairs, but non–standard base pairs are seen, especially the wobble base pair between guanine and uracil. The tertiary structure is formed by contacts between secondary structure elements and constitue the actual three dimensional structure of the molecule. This can include new hydrogen bonds and Van der Waals attractions. For various reasons, RNA structure prediction focuses on the secondary structure although it is ultimately the tertiarty structure that determines the functionality. First of all, the secondary structure forms fast and introduces strong base pairing interactions, thereby contributing the major part of the folding energetics and forming a stable scaf- fold for the tertiary interactions [OTJ04]. This, combined with the stacking of base pairs into stems, make the secondary structure highly informative. Secondly, the cur- rent knowledge about the three dimensional structure is lacking, making it less feasible although some progress is being made [FMT+09]. A Primary Structure 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 5’ GCGGAUUUAGCUCAGDDGGGAGAGCGCCAGACUGAAYA.CUGGAGGUCCUGUGT.CGAUCCACAGAAUUCGCACCAΨ Ψ 3’ B Secondary StructureC Tertiary Structure 75 3’ C 5’ A C T ΨC 3’ 5’ A G C Loop Acceptor C G Stem G C 70 G U T ΨC D Loop 5A U Acceptor 15 U A Loop D Loop D G U U 60 Stem D A A A 65 C U C G A C A U C G 10 A C G C G G A G 25 U G U G G A C C 50 G U T Ψ. C 20 G G C AG 55 G 45 C G A U A G C 40 30 Ψ Variable C . Anticodon Anticodon U A Loop G Y. Loop A A Loop 35 Figure 1: Illustration of primary structure (the sequence at the top), secondary structure (the draw- ing on the left) and tertiary structure (the model to the right) illustrated using a tRNA molecule. The illustration is from [Gar03]. 3 Brief introduction to sequence alignment During evolution mutations will change the sequence of nucleotides in the DNA and thus possibly the encoded RNA and protein polymers as well. Therefore, to compare related sequences it is often useful to align them in order to find both segments that are conserved and segments that are variable. The sequence comparison problem is en- countered frequently in the biological sciences. Given a number of e.g. RNA or protein sequences, the goal of the alignment is to find similarities as well as differences between them. An alignment is an ordering of the sequences in an N L matrix, where N is the number of sequences and L is the length of the alignment.× L is at least equal to the length of the longest sequence. Each entry in the alignment matrix contains either a nucleotide/amino acid from one of the sequences or a gap character, normally denoted by ‘-’. A row in the matrix contains a single sequence possibly padded with gaps to have length L. A column of the alignment contains N nucleotides/gaps, corresponding to one from each of the aligned sequences. Sequence alignment is a well studied area in bioinformatics since it is of such general importance.