space POPSEQ Anchoring and ordering contig assemblies from next generation sequencing data by population sequencing Dissertationsschrift zur Erlangung des Grades eines Doktors der Naturwissenschaften an der Technischen Fakultät der Universität Bielefeld von Martin Mascher 28. Oktober 2013 Gedruckt auf alterungsbeständigem Papier (ISO 9706). Contents 1 Introduction 1 1.1 Overview . .2 1.2 Publications . .3 1.3 Acknowledgements . .4 2 Background 5 2.1 Genetic mapping . .5 2.1.1 Meiotic recombination and genetic linkage . .6 2.1.2 Genetic markers . .8 2.1.3 Plant mapping populations . 12 2.1.4 Genetic map construction . 15 2.2 Next generation sequencing technologies . 16 2.2.1 Roche 454 . 18 2.2.2 Illumina . 20 2.3 Genome assembly strategies . 22 2.3.1 DNA fragment assembly . 23 2.3.2 Hierarchical shotgun sequencing . 24 2.3.3 Whole genome shotgun sequencing . 27 2.4 Methods for anchoring sequence assemblies . 31 2.4.1 Integrating physical and genetic maps . 31 2.4.2 The sequence-enriched physical and genetic map of barley 32 2.4.3 Genome zippers: anchoring by collinearity . 36 2.4.4 Direct anchoring of sequence contigs . 38 3 The POPSEQ method 41 3.1 Software used in the POPSEQ pipeline . 41 3.1.1 BWA . 43 3.1.2 SAMtools . 44 3.1.3 MSTMAP . 45 3.2 Barley populations and sequence data . 46 3.3 From FASTQ to marker-by-genotype matrix . 46 3.4 Framework maps . 48 3.4.1 Morex × Barke iSelect map . 50 3.4.2 Morex × Barke GBS map . 50 3.4.3 OWB GBS map . 51 3.5 Mapping SNPs and WGS contigs to the framework map . 53 i 4 Proof-of-principle of POPSEQ 61 4.1 Comparison to sequenced bacterial artificial chromosomes (BACs) 61 4.2 Comparison to the integrated physical and genetic map of barley 62 4.3 Using different framework maps for one population . 66 4.4 Using different populations . 67 5 Applications of POPSEQ in genome-assisted research 71 5.1 Reference-based genetic mapping . 71 5.2 Gene isolation . 73 5.2.1 Mapping the Vrs1 gene . 76 5.2.2 Mapping-by-sequencing with exome capture . 78 5.3 Anchoring physical maps . 79 5.3.1 Genetic anchoring of BAC contigs . 81 5.3.2 Genetic anchoring of single BAC clones . 84 5.4 Comparative genomics . 86 5.4.1 Collinearity . 87 5.4.2 Evolutionary and population genomics . 87 6 Discussion and outlook 93 6.1 Impact of assembly quality and sequencing depth . 93 6.2 Limitations of genetic anchoring in the Triticeae . 96 6.3 POPSEQ for polyploid and outbred species . 98 6.3.1 Polyploids . 98 6.3.2 Outbred species . 100 6.4 Validation and improvement of the POPSEQ algorithm . 102 6.5 Conclusion . 104 Bibliography 107 ii List of Figures 2.1 Crossover . .7 2.2 Segregation in a dihybrid cross . .9 2.3 Mapping populations . 13 2.4 Historic development of sequencing costs . 17 2.5 Schematic overview of hierarchical shotgun sequencing . 25 2.6 The sequence-enriched physical and genetic framework of barley 34 3.1 Overview of POPSEQ . 42 3.2 Distribution of coverage in the Morex × Barke GBS data . 47 3.3 Missing data . 49 3.4 Collinearity between IBSC and GBS maps . 52 3.5 Placing SNPs into a framework . 55 3.6 POPSEQ parameters . 57 4.1 Comparison between MxB iSelect POPSEQ and IBSC anchoring 65 4.2 Comparison between MxB iSelect and MxB GBS POPSEQ . 67 4.3 Comparison of MxB and OWB POPSEQ . 69 5.1 POPSEQ and consensus map . 72 5.2 OWB graphical genotypes . 74 5.3 Mapping-by-sequencing Vrs1 in OWB . 77 5.4 Mapping-by-sequencing . 80 5.5 POPSEQ anchoring of the barley physical map . 82 5.6 Physical vs. genetic distance in barley . 83 5.7 Syntenic blocks between H. vulgare and B. distachyon ..... 88 5.8 Barley whole exome capture performance . 91 6.1 Observed and expected coverage . 94 6.2 Suppressed recombination in the genetic centromere of barley . 97 iii iv List of Tables 2.1 Sequencing platforms . 19 2.2 Progress in genome sequencing . 28 2.3 Features of the barley physical map. 33 2.4 Features of the whole genome shotgun assembly . 35 3.1 Sequence data generated in this study. 48 3.2 Number of anchored WGS SNPs . 56 3.3 Anchoring statistics. 59 4.1 Consistency of POPSEQ positions of physically close WGS contigs 63 4.2 Evaluation of different parameter sets for POPSEQ . 64 5.1 POPSEQ anchoring statistics of FP contigs and BAC clones. 85 v vi List of abbreviations A. thaliana Arabidopsis thaliana BAC Bacterial artificial chromosome BAM Compressed binary SAM format (see below) B. distachyon Brachypodium distachyon BLAST Basic local alignment search tool bp Basepair BWA Burrows-Wheeler aligner cDNA Complementary DNA of messenger RNA cM CentiMorgan cv. Cultivar DH Doubled haploid DNA Deoxyribonucleic acid E. coli Escherichia coli EST Expressed sequence tag F1,F2, . First, second, . filial generation FP contig Fingerprint contig GATK Genome Analysis Toolkit Gb Giga base pair GBS Genotyping-by-sequencing HSP High scoring pair H. vulgare Hordeum vulgare IBSC International Barley Genome Sequencing Consortium Indel Short insertion and deletion polymorphism kb Kilo base pair LD Linkage disequilibrium MAD Median absolute deviation MTP Minimum tiling path MxB Morex × Barke Mb Mega base pair N50 Weighed average contig size, half of the assembly is contained in contigs larger than the N50 NGS Next generation sequencing nt Nucleotide OWB Oregon Wolfe Barleys PCR Polymerase chain reaction RIL Recombinant inbred line vii RNA Ribonucleic acid RNA-seq RNA sequencing using NGS technology SAM Sequence Alignment/Map format VCF Variant call format SNP Single nucleotide polymorphism Vrs1 Six-rowed spike gene WGS Whole genome shotgun viii ix The continuing rapid fall in the cost of computer components is making it possible for most DNA sequencing laboratories to have their own small com- puter. The fact that DNA sequencing is now a fast procedure, and the avail- ability of computers gives the possibility of more efficient overall strategies for sequence determination. – Rodger Staden, 1979 1 Introduction Next generation sequencing (NGS) provides the opportunity to rapidly and at relatively low cost establish gene space assemblies for virtually any species. These assemblies consist of tens to hundreds of thousands of short contiguous pieces of DNA sequence (contigs) and often represent only the low-copy por- tion of the genome. Despite the limitations of such assemblies, they have been widely proposed as surrogates for draft genome sequences for the purposes of gene isolation, genomics-assisted breeding and the assessment of diversity within and between species (Brenchley et al., 2012; IBSC, 2012; Xu et al., 2012; Guo et al., 2012). However in most cases, particularly those concerning large and complex genomes, they remain disconnected collections of short sequence contigs that are not embedded in a genomic context. Bringing these fragments together into a tentative linear order, or even associating contigs with individ- ual chromosomes or chromosome arms, has been a major and costly undertak- ing. In a recent example, the International Barley Genome Sequencing Consor- tium (IBSC, 2012) had reported a gene space assembly of the 5.1 Gb genome of barley. The development and use of a BAC-based physical map, BAC end sequences, flow-sorted and chromosome-arm survey sequences, fully sequenced BAC clones and conserved synteny were all required to fully contextualize only 410 Mb of genomic sequence IBSC (2012). These genomic resources provide an established path towards a reference sequence by sequencing a minimum tiling path of overlapping BAC clones and hierarchically (Feuillet et al., 2012). The development of the necessary resources requires a substantial amount of time, labor and finances which makes this strategy prohibitive for smaller and more poorly resourced research communities, e.g. research in non-model organisms or orphan crops. The establishment of a BAC-based reference sequence of the maize genome took about seven years, required the coordinated effort of sev- eral laboratories and cost about US $50 million (Chandler and Brendel, 2002; Martienssen et al., 2004; Schnable et al., 2009). Similarly, the reference se- quence of a single 1 Gb chromosome of hexaploid wheat has not been finished five years after the publication of a physical map (Paux et al., 2008). Emerging technologies such as longer sequence reads (Schadt et al., 2010), optical mapping (Lam et al., 2012a) and novel assembly algorithms (such as ALLPATHS-LG (Gnerre et al., 2011)) may speed up the process of data col- lection and analysis as well as increase the contiguity and completeness of WGS assemblies, but their applicability to large genomes where abundant sequence repeats (the bane of any assembler), arising from paralogous dupli- cations, repetitive elements, ancestral duplications and polyploidy, remains to 1 be assessed. It has been common practice to associate mapped genetic markers with sequence resources based on sequence similarity in order to link genetic and physical maps (Chen et al., 2002; Wei et al., 2007). While the order of BAC contigs on a physical is in the order of thousands, NGS technology produces hundres dof thousands of sequence contigs. For example, (IBSC, 2012) re- ported an assembly that consists of over 350,000 contigs longer than 1 kb. The number of markers afforded by conventional genotyping strategies is sim- ply not commensurate with the large number of short sequence contigs. In the absence of an appropriate molecular or analytical method to estab- lish short-range connectivity (i. e. to link physically close sequence contigs), we used the power of genetic segregation to directly and linearly arrange sequence contigs into closely associated recombination bins along a target genome.