assembly, alignment, annotation. - UCSC Bioinformatics

388 Pages·2014·16.78 MB·English

UNIVERSITY OF CALIFORNIA
SANTA CRUZ
EVALUATION AND APPLICATION OF THE THREE A’S OF
GENOMICS: ASSEMBLY, ALIGNMENT, ANNOTATION.
A dissertation submitted in partial satisfaction
of the requirements for the degree of
DOCTOR OF PHILOSOPHY
in
BIOMOLECULAR ENGINEERING & BIOINFORMATICS
by
Dent A. Earl
December 2014
The Dissertation of Dent A. Earl
is approved:
Professor David Haussler, Chair
Professor Grant Pogson
Professor Joshua Stuart
Professor Richard Green
Dean Tyrus Miller
Vice Provost and Dean of Graduate Studies
Copyright (cid:13)c by
Dent A. Earl
2014
Table of Contents
List of Figures vi
List of Tables ix
Abstract xi
Dedication xiii
Acknowledgments xiv
1 Introduction 1
1.1 Assemblathon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Alignathon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Comparative Genome Annotation . . . . . . . . . . . . . . . . . . . . . . 11
1.3.1 Assembly, to alignment, to annotation, and back to assembly . . 12
1.3.2 How to annotate genomes . . . . . . . . . . . . . . . . . . . . . . 14
1.3.3 So-called ab initio methods . . . . . . . . . . . . . . . . . . . . . 16
1.3.4 Comparative annotation methods . . . . . . . . . . . . . . . . . . 17
1.3.5 Laboratory mice . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2 Assemblathon 27
2.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.3.1 Genome Simulation . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.3.2 Read simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.3.3 Assemblies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.3.4 Evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.3.5 N50 and NG50 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.3.6 Multiple sequence alignment . . . . . . . . . . . . . . . . . . . . 39
2.3.7 Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.3.8 Blocks and contig paths . . . . . . . . . . . . . . . . . . . . . . . 42
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2.3.9 Scaffold paths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.3.10 Structural errors . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.3.11 Long-range contiguity . . . . . . . . . . . . . . . . . . . . . . . . 50
2.3.12 Annotation analysis . . . . . . . . . . . . . . . . . . . . . . . . . 50
2.3.13 Substitution errors . . . . . . . . . . . . . . . . . . . . . . . . . . 54
2.3.14 Copy-number errors . . . . . . . . . . . . . . . . . . . . . . . . . 58
2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
2.5 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
2.5.1 Genome simulation . . . . . . . . . . . . . . . . . . . . . . . . . . 66
2.5.2 Read simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
2.5.3 Cactus alignment assessment . . . . . . . . . . . . . . . . . . . . 67
2.6 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
2.7 Supplemental Figures and Tables . . . . . . . . . . . . . . . . . . . . . . 73
3 Alignathon 107
3.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
3.3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
3.3.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
3.3.2 Competition Organization and Submissions . . . . . . . . . . . . 112
3.3.3 Genomewide Comparison to Simulated Genome Alignments . . . 116
3.3.4 Evaluating Genome Alignments in the Absence of Truth . . . . . 120
3.3.5 Visualizing and Analysing Regional Accuracy . . . . . . . . . . . 126
3.3.6 Comparing the Submissions Directly . . . . . . . . . . . . . . . . 129
3.3.7 Assessing Similarity vs. Homology . . . . . . . . . . . . . . . . . 131
3.3.8 Missing Duplications . . . . . . . . . . . . . . . . . . . . . . . . . 132
3.3.9 Code and Data Repository . . . . . . . . . . . . . . . . . . . . . 133
3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
3.6 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
3.6.1 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
3.6.2 Flies Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
3.6.3 MafTools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
3.6.4 MAF Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . 144
3.6.5 Regional Alignments & PSAR . . . . . . . . . . . . . . . . . . . 144
3.7 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
3.8 Supplemental Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
3.8.1 Details of regional alignments & PSAR . . . . . . . . . . . . . . 146
3.8.2 Details of Submissions . . . . . . . . . . . . . . . . . . . . . . . . 148
3.9 Supplemental Figures and Tables . . . . . . . . . . . . . . . . . . . . . . 162
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4 Comparative Genome Annotation 192
4.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192
4.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
4.2.1 The promise of comparative power . . . . . . . . . . . . . . . . . 193
4.3 Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
4.3.1 An interdependent analysis cycle . . . . . . . . . . . . . . . . . . 195
4.3.2 Improving assemblies . . . . . . . . . . . . . . . . . . . . . . . . . 206
4.3.3 Alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
4.3.4 Improving assemblies improves prediction qualities . . . . . . . . 211
4.3.5 Comparative prediction trumps ab initio . . . . . . . . . . . . . . 219
4.3.6 Alignments recapitulate known results . . . . . . . . . . . . . . . 231
4.3.7 Transitive gene predictions change with phylogenetic distance . . 239
4.3.8 Suggestive findings . . . . . . . . . . . . . . . . . . . . . . . . . . 242
4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254
4.4.1 The alignments are results . . . . . . . . . . . . . . . . . . . . . . 254
4.4.2 Transitive alignment declines in effeciency with distance . . . . . 255
4.4.3 Comparative annotation performs well . . . . . . . . . . . . . . . 256
4.4.4 GPIP improves assemblies, alignments; leads to biological results 260
4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264
4.6 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266
4.6.1 Nomenclature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267
4.6.2 Assembly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267
4.6.3 RNAseq . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268
4.6.4 Cactus alignment . . . . . . . . . . . . . . . . . . . . . . . . . . . 268
4.6.5 TransMap pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . 268
4.6.6 Augustus pipeline . . . . . . . . . . . . . . . . . . . . . . . . . . 271
4.6.7 GPIP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
4.6.8 Comparative Assembly Hub construction . . . . . . . . . . . . . 291
4.7 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292
Bibliography 293
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List of Figures
1.1 The number of publications using mouse strains since 1980. . . . . . . . 22
2.1 The phylogeny of the simulated haploid genomes. . . . . . . . . . . . . . 30
2.2 N50 statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.3 An adjacency graph example. . . . . . . . . . . . . . . . . . . . . . . . . 43
2.4 Assembly coverage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.5 Correctly contiguous pairs as a function of separation distance. . . . . . 51
2.6 Substitution (base) errors for top assembly from each team. . . . . . . . 57
2.7 Copy-number errors for the top assembly from each team. . . . . . . . . 59
2.8 Scaffold gap and error subgraphs. . . . . . . . . . . . . . . . . . . . . . . 69
2.9 Dot plot showing the alignment between haplotype alpha 1 and alpha 2. 73
2.10 Distributions of four annotation types for root. . . . . . . . . . . . . . . 74
2.11 Distributions of four annotation types for other genomes. . . . . . . . . 74
2.12 N50 statistics for all accessed assemblies. . . . . . . . . . . . . . . . . . . 75
2.13 Assembly coverage along E. coli stratified by block length. . . . . . . . . 81
2.14 Assembly coverage along hap. alpha 1 stratified by block length. . . . . 82
2.15 Assembly coverage along hap. alpha 2 stratified by block length. . . . . 83
2.16 Assembly coverage along hapl. alpha 1 stratified by contig path length. . 84
2.17 Assembly coverage along hapla˙lpha 2 stratified by contig path length. . 85
2.18 Assembly coverage along hapl. alpha 1 stratified by contig length. . . . . 86
2.19 Assembly coverage along hap. alpha 2 stratified by contig length. . . . . 87
2.20 Assembly coverage along hap. alpha 1 stratified by scaffold path length. 88
2.21 Assembly coverage along hap. alpha 2 stratified by scaffold path length. 89
2.22 Assembly coverage along hap. alpha 1 stratified by scaffold length. . . . 90
2.23 Assembly coverage along hap. alpha 2 stratified by scaffold length. . . . 91
2.24 Contig path ends and structural errors along haplotype alpha 1.. . . . . 94
2.25 Contig path ends and structural errors along haplotype alpha 2.. . . . . 96
2.26 Substitution (base) errors for all the assessed assemblies. . . . . . . . . . 97
2.27 Copy number errors for all the assessed assemblies. . . . . . . . . . . . . 98
2.28 Pairwise Pearson’s correlation matrix . . . . . . . . . . . . . . . . . . . . 103
2.29 QQ plot matrix of pairwise Pearson’s. . . . . . . . . . . . . . . . . . . . 104
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2.30 Coverage of simulated sequencing reads along the chromosome. . . . . . 104
2.31 Substitution phasing statistics using the Cactus MSA. . . . . . . . . . . 105
2.32 Phasing N50 statistics using the Cactus MSA. . . . . . . . . . . . . . . . 106
3.1 The phylogenies of the three test sets. . . . . . . . . . . . . . . . . . . . 110
3.2 Simulated primate and mammal F-score results. . . . . . . . . . . . . . 117
3.3 Primate and Mammal F-score stratified by phylogenetic distance. . . . . 119
3.4 Simulated mammal values versus statistical values. . . . . . . . . . . . . 122
3.5 Fly results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
3.6 Overall pairwise coverage values in the flies dataset. . . . . . . . . . . . 125
3.7 Region 4 of simHuman regional analysis of the sim. mammal data set. . 127
3.8 Region 2 of D. melanogaster (dm3) regional analysis of the fly data set. 128
3.9 The Jaccard distance matrix and clustering of submissions. . . . . . . . 130
3.10 Region 1 of simHuman regional analysis of the sim. primate data set. . . 170
3.11 Pseudo F-score stratified by phylogenetic distance for fly data set. . . . 171
3.12 Correlation between PSAR-based pseudo-F-Scores and F-Scores. . . . . 172
3.13 Correlation between coverage and recall. . . . . . . . . . . . . . . . . . . 174
3.14 Correlation between PSAR precision and precision. . . . . . . . . . . . . 175
3.15 Pseudo F-score for the regional analysis of sim. mammal data set. . . . 176
3.16 Change in F-Score between original submissions and transitive closure . 177
3.17 Overall average prop. of duplications for transitively closed submissions. 178
3.18 Overall average prop. of duplications for original submissions. . . . . . . 179
3.19 Correlation of values between regional and whole genome analyses. . . . 183
4.1 Project flow chart. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
4.2 An example of the noStop: alignmentPartialMap label. . . . . . . . . . . 197
4.3 An example use of alignmentPartialMap to detect an assembly mistake. 198
4.4 An example of the alignmentAbutsEdge label. . . . . . . . . . . . . . . . 199
4.5 An example of the containsNs label. . . . . . . . . . . . . . . . . . . . . 200
4.6 An example of the hasOkCopies label. . . . . . . . . . . . . . . . . . . . 201
4.7 An example of the hasBadCopies label. . . . . . . . . . . . . . . . . . . 202
4.8 An example of the preexisting label. . . . . . . . . . . . . . . . . . . . . 203
4.9 An example of the outOfFrame label. . . . . . . . . . . . . . . . . . . . . 204
4.10 An example of the nonsense label. . . . . . . . . . . . . . . . . . . . . . 205
4.11 An example of the nonsynon label. . . . . . . . . . . . . . . . . . . . . . 205
4.12 An example of the synon label. . . . . . . . . . . . . . . . . . . . . . . . 206
4.13 Density of lengths of attributes of input transcripts. . . . . . . . . . . . 209
4.14 N-curves of all study genomes and data releases. . . . . . . . . . . . . . 210
4.15 Phylogenetic tree.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
4.16 Fraction of transcripts from C57BL/6J that map to other strains. . . . . 214
4.17 Change in tag counts between release 1302 and 1405. . . . . . . . . . . . 216
4.18 Distribution of mRNA lengths 1405 as ratios of length in C57BL/6J. . . 218
4.19 Density of average percent “ok” tags in Augustus chromosome 6 runs. . 221
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4.20 Density of average percent “ok” tags in Augustus C6TR runs. . . . . . . 221
4.21 Overview of Augustus results with respect to C57BL/6J. . . . . . . . . . 222
4.22 Jaccard similarity matrix for four variants of Augustus on C57BL/6J
C6TR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224
4.23 Jaccard similarity matrix for four variants of Augustus on rn5. . . . . . 226
4.24 Augustus results on comparative assembly hub with respect to C57BL/6J.227
4.25 AugustusresultsoncomparativeassemblyhubwithrespecttoC57BL/6NJ.229
4.26 The Nnt region with repect to the strains. . . . . . . . . . . . . . . . . . 232
4.27 The deletion in crb1 rd8 region. . . . . . . . . . . . . . . . . . . . . . . . 234
4.28 Variation within the Prdm9 gene. . . . . . . . . . . . . . . . . . . . . . . 235
4.29 A 25 bp deletion in LPJ, 129S1/SvImJ and FVBNJ within the Disc1 gene.237
4.30 Raw counts of annotation labels in data release 1405. . . . . . . . . . . . 240
4.31 Percent of transcripts containing annotations vs. phylogenetic distance. 243
4.32 Annotations per transcript as a function of phylogenetic distance. . . . . 244
4.33 A nonsynonymous mutation in A/J, in ZFP655.. . . . . . . . . . . . . . 246
4.34 A nonsynonymous mutation in A/J, in ROPN1L. . . . . . . . . . . . . . 247
4.35 A nonsynonymous mutation in NZO/HlLtJ, in GTF2I. . . . . . . . . . . 249
4.36 A nonsynonymous mutation in NZO/HlLtJ, in JAK3. . . . . . . . . . . 250
4.37 A nonsynonymous mutation in AKR/J, in MPST. . . . . . . . . . . . . 252
4.38 A nonsynonymous mutation in AKR/J, in CERKL. . . . . . . . . . . . 253
4.39 The TransMap process. . . . . . . . . . . . . . . . . . . . . . . . . . . . 269
4.40 Chromosome 6 test region.. . . . . . . . . . . . . . . . . . . . . . . . . . 270
4.41 Transcript object hierarchy used by metaFilter. . . . . . . . . . . . . . . 277
4.42 Example colors used for visualization. . . . . . . . . . . . . . . . . . . . 288
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List of Tables
2.1 Groups that submitted assemblies. . . . . . . . . . . . . . . . . . . . . . 31
2.2 Genome simulation statistics. . . . . . . . . . . . . . . . . . . . . . . . . 33
2.3 Rankings of the top assembly from each team in eight. . . . . . . . . . . 37
2.4 Coverage statistics for the top assembly from each team. . . . . . . . . . 41
2.5 Structural error statistics for the top assembly from each team. . . . . . 48
2.6 Inclusion of annotated features within perfect paths. . . . . . . . . . . . 53
2.7 Summary of metrics used in the analysis. . . . . . . . . . . . . . . . . . 61
2.8 Rankings of all assemblies in eight categories. . . . . . . . . . . . . . . . 76
2.9 N50 statistics for all accessed assemblies. . . . . . . . . . . . . . . . . . . 79
2.10 Structural error statistics for all assessed assemblies. . . . . . . . . . . . 92
2.11 Substitution (base) errors in homozygous valid columns. . . . . . . . . . 99
2.12 Substitution (base) errors in heterozygous valid columns. . . . . . . . . 101
3.1 Summary statistics for the simulated genomes. . . . . . . . . . . . . . . 111
3.2 Summary statistics for the fly genomes. . . . . . . . . . . . . . . . . . . 113
3.3 Submissions to the Alignathon. . . . . . . . . . . . . . . . . . . . . . . . 114
3.4 AutoMZ and TBA submission runtime details. . . . . . . . . . . . . . . 150
3.5 A listing of simulated genome events for the two phylogenies. . . . . . . 163
3.6 Summary statistics of submissions for the primate data set. . . . . . . . 164
3.7 Summary statistics of submissions for the mammal data set. . . . . . . . 165
3.8 Summary statistics of submissions for the fly data set. . . . . . . . . . . 165
3.9 Primate simulation F-score results. . . . . . . . . . . . . . . . . . . . . . 166
3.10 Mammal simulation F-score results. . . . . . . . . . . . . . . . . . . . . 167
3.11 Mammal simulation F-score results stratified by annotation type. . . . . 168
3.12 Primate simulation F-score results stratified by annotation type . . . . . 169
3.13 Fly pseudo F-score regional results. . . . . . . . . . . . . . . . . . . . . . 173
3.14 Jaccard distance values for all submissions to the primate dataset. . . . 180
3.15 Jaccard distance values for all submissions to the mammal dataset. . . . 181
3.16 Jaccard distance values for all submissions to the fly dataset. . . . . . . 182
3.17 Primate simulation precision results. . . . . . . . . . . . . . . . . . . . . 184
3.18 Primate simulation recall results. . . . . . . . . . . . . . . . . . . . . . . 185
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3.19 Mammal simulation precision results. . . . . . . . . . . . . . . . . . . . . 186
3.20 Mammal simulation recall results.. . . . . . . . . . . . . . . . . . . . . . 187
3.21 Mammal simulation precision results stratified by annotation type. . . . 188
3.22 Mammal simulation recall results stratified by annotation type. . . . . . 189
3.23 Primate simulation precision results stratified by annotation type. . . . 190
3.24 Primate simulation recall results stratified by annotation type. . . . . . 191
4.1 N50 values for all assemblies used in this study. . . . . . . . . . . . . . . 208
4.2 Summary statistics on the lengths of attributes of the input transcripts. 211
4.3 Mouse strains used in this study. . . . . . . . . . . . . . . . . . . . . . . 212
4.4 Summary of “ok” tags for four runs of Augustus and one of TransMap. 220
4.5 Jaccard statistics for Augustus C57BL/6J C6TR. . . . . . . . . . . . . . 222
4.6 Jaccard statistics for Augustus Rattus C6TR-rn5.. . . . . . . . . . . . . 225
4.7 Known C57BL/6J C57BL/6NJ mutations. . . . . . . . . . . . . . . . . . 233
4.8 Known FVB/NJ mutations. . . . . . . . . . . . . . . . . . . . . . . . . . 238
4.9 Mouse lab codes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267
4.10 Labels used by gene-check.. . . . . . . . . . . . . . . . . . . . . . . . . . 274
4.11 Labels used by filters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278
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