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PDF - White Rose Etheses Online

PDF - White Rose Etheses Online

Context Differences

Context Differences between the genomes of organisms are responsible for the full diversity of life, from the multitude of relatively subtle differences between individuals of a species to the vast and obvious differences between organisms of different species. The compositional differences most commonly studied between the genomes of distinct species are the inconsistencies between the number and sequence of their genes. The accumulation of mutations, either individually within a gene sequence or in the loss/gain or duplication of whole genes, coupled with environmental selective pressures, form the major driving force of evolution. Over time, this process has created the diverse range of species we observe in the world. The study of genome sequences and the differences between them is an important element of modern biological research. Over the last ten years, genomic research has been altered completely by the introduction of increasingly high-throughput methods of DNA sequencing. The technology currently available enables the elucidation of enough sequence to cover a whole eukaryote genome several times over in a single day. This provides the means to study differences between the genomes of different individuals and species on a scale scarcely imaginable twenty years ago. These new methods have led to a rapid increase in the number of genome sequences known and the volume of sequencing data readily available, and opened up whole new avenues of research. These include the possibility of quickly and cheaply sequencing the genome of individuals, which may soon render it cost-effective to sequence the genome of every member of a population in order to provide genome-specific healthcare throughout their lifetime (Mardis 2011); the ability to rapidly identify the binding sites of a protein across an entire genome; the capability to study the transcriptome through high- throughput EST sequencing (Nagaraj, Gasser et al. 2007); and the investigation of the combined genome of whole microbial communities sampled directly from their natural environment, a field known as metagenomics. Chapter 1 - Context A short summary of DNA sequencing is provided here, including an overview of the advances in sequencing technology that have led to this point, the advances that can be expected in the immediate future, and the avenues of 3

Chapter 1 - Context research that have recently been opened up. Particular attention will be paid to metagenomics, the challenges associated with analysis of the sequencing datasets generated in such studies, and possible ways to overcome these. 4

  • Page 1: Clustering Large Raw DNA Sequencing
  • Page 5 and 6: Table of contents Table of Contents
  • Page 7 and 8: Table of Contents Extraction of DNA
  • Page 9 and 10: Table of Contents Contig assembly..
  • Page 11 and 12: List of Tables and Figures Table 3.
  • Page 13 and 14: Figure 5.5 The number of sequencing
  • Page 15: Declaration • The implementation
  • Page 20 and 21: DNA sequencing - an overview Sanger
  • Page 22 and 23: sequence, which can then be assembl
  • Page 24 and 25: nucleotides to a strand is detected
  • Page 26 and 27: separated based on size (typically
  • Page 28 and 29: example, a pair of reads produced f
  • Page 30 and 31: The recent improvements in sequenci
  • Page 32 and 33: Chapter 1 - Metagenomics and sequen
  • Page 34 and 35: complement metagenomics and provide
  • Page 36 and 37: such as sampling time and location,
  • Page 38 and 39: aims of the HMP are described as:
  • Page 40 and 41: As with the larger and more complex
  • Page 42 and 43: Methods of sequence comparison Alig
  • Page 44 and 45: where local alignments can identify
  • Page 46 and 47: Ladunga (1994), led to the coining
  • Page 48 and 49: Project summary The aim of this pro
  • Page 51 and 52: 2 A comparison of genomic signature
  • Page 53 and 54: GC content The GC content of DNA, t
  • Page 55 and 56: In order to ascertain the likelihoo
  • Page 58 and 59: To illustrate this point further, i
  • Page 60 and 61: words in each sequence, is benefici
  • Page 62: values, collected as the sample siz
  • Page 65 and 66: On a related note, the authors of t
  • Page 67 and 68: would likely be more closely relate
  • Page 70 and 71:

    Breakdown of simLC by reads-per-spe

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    corresponding ‘true’ dataset. T

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    if the dataset contains 50 sequence

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    Figure 2.5 Clustering of sequences

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    Clustering of Dataset 1 Tables 2.2

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    Table 2.3 Mean recall values of clu

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    use of OFDEG features was only marg

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    Table 2.4 Mean precision and recall

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    sequences from that genome in the d

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    Table 2.6 Mean recall values of clu

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    different proportions were grouped.

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    R. palustris Bradyrhizobium BTAi1 C

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    Figure 2.7(i) - 2.7(xv) Comparative

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    IND Cluster 1 Cluster 4 Cluster 2 C

  • Page 98 and 99:

    TNF Cluster 1 Cluster 4 Cluster 2 C

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    GC + OFDEG Cluster 1 Cluster 4 Clus

  • Page 102 and 103:

    IND + OFDEG Cluster 1 Cluster 4 Clu

  • Page 104 and 105:

    OFDEG + TNF Cluster 1 Cluster 4 Clu

  • Page 106 and 107:

    GC + IND + TNF Cluster 1 Cluster 4

  • Page 108 and 109:

    IND + OFDEG + TNF Cluster 1 Cluster

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    When compared to the distribution o

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    to that achieved with GC feature ve

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    Table 2.8 Time taken (in seconds) t

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    platforms, all with typical lengths

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    feature vectors, which were found t

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    However, because the sequencing rea

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    single-variable GC content feature.

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    3 Preparation and analysis of high-

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    elonging to either the host species

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    Materials and Methods Inoculation o

  • Page 133 and 134:

    Assay sequences: • Cucumber mosai

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    Analysis of extracted RNA by qRT-PC

  • Page 137 and 138:

    Analysis of extracted DNA by qPCR E

  • Page 139 and 140:

    Table 3.5 Amount of DNA sequenced f

  • Page 141 and 142:

    Results Comparison of bacterial ino

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    Mean Ct Value Mean Ct Value (a) (b)

  • Page 145 and 146:

    the CMV assay. If the poor amplific

  • Page 147 and 148:

    40 30 20 Mean Ct Value (COX Assay)

  • Page 149 and 150:

    qRT-PCR Analysis of Viral Treatment

  • Page 151 and 152:

    qPCR analysis of DNA extracts in pr

  • Page 153 and 154:

    Table 3.8 Mean Ct values observed i

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    The full inoculation method involve

  • Page 157 and 158:

    Table 3.9 Mean Ct values observed i

  • Page 159 and 160:

    qRT-PCR Analysis of Dummy Inoculate

  • Page 161 and 162:

    Results of high-throughput DNA sequ

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    Figure 3.10 Proportion of sequence

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    Figure 3.11 Proportion of sequence

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    • Viral treatment groups Table 3.

  • Page 169 and 170:

    Figure 3.12 Proportion of sequence

  • Page 171 and 172:

    Figure 3.13 Proportion of sequence

  • Page 173 and 174:

    Discussion Datasets produced from b

  • Page 175 and 176:

    Datasets produced from viral treatm

  • Page 177:

    present in the samples. The use of

  • Page 180 and 181:

    Introduction An evaluation of the p

  • Page 182 and 183:

    discussed elsewhere, the length of

  • Page 184 and 185:

    single clustering method, CLARA. Th

  • Page 186 and 187:

    UT (A. thaliana) UT (unassigned) UT

  • Page 188 and 189:

    Results The scope for the four feat

  • Page 190 and 191:

    Chapter 4 - Results GC (i) Cluster

  • Page 192 and 193:

    TNF GC + IND Cluster 1 UT (A. thali

  • Page 194 and 195:

    IND + OFDEG IND + TNF Cluster 1 UT

  • Page 196 and 197:

    GC + IND + TNF Cluster 1 GC + OFDEG

  • Page 198 and 199:

    Coherent with clustering results ob

  • Page 200 and 201:

    UT+Psp2126 - five clusters Figure 4

  • Page 202 and 203:

    IND + OFDEG (viii) Cluster 1 Cluste

  • Page 204 and 205:

    OFDEG + TNF Cluster 1 Cluster 4* Cl

  • Page 206 and 207:

    GC + IND + TNF (xii) Cluster 1 Clus

  • Page 208 and 209:

    IND + OFDEG + TNF (xiv) Cluster 1 C

  • Page 210 and 211:

    Once again, clustering results prod

  • Page 212 and 213:

    Discussion Several trends were iden

  • Page 214:

    produce large numbers of these feat

  • Page 217 and 218:

    Introduction Previous chapters have

  • Page 219 and 220:

    1981). Partitioning around mediods

  • Page 221 and 222:

    strength (Tibshirani and Walther 20

  • Page 223 and 224:

    Where these linkage metrics are mea

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    many of the methods described previ

  • Page 227 and 228:

    Data can be grouped with an SOM in

  • Page 229 and 230:

    separation of data in each case. So

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    here). Euclidean distance, the defa

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    Beyond this general pattern within

  • Page 235 and 236:

    1.00 0.80 0.60 0.40 0.20 0 Chapter

  • Page 237 and 238:

    Parameter selection for spectral cl

  • Page 239 and 240:

    1.0 0.8 0.6 0.4 0.2 KASP Clustering

  • Page 241 and 242:

    HHSOM When originally published by

  • Page 243 and 244:

    No. of sequences assigned to node 3

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    No. of sequences assigned to node 3

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    No. of sequences assigned to node 5

  • Page 249 and 250:

    Comparison of partitioning clusteri

  • Page 251 and 252:

    Table 5.4 Precision and recall stat

  • Page 253 and 254:

    Cluster Species 1 2 3 4 5 6 7 A. th

  • Page 255 and 256:

    een grouped into the cluster. Preci

  • Page 257 and 258:

    Discussion The level of accuracy ac

  • Page 260 and 261:

    6 A comparison of de novo sequence

  • Page 262 and 263:

    where a pairwise comparison is made

  • Page 264 and 265:

    performed at random. This also impr

  • Page 266 and 267:

    Dataset Organism Genome Size Genome

  • Page 268 and 269:

    Results UT+Psp2126 The UT+Psp2126 d

  • Page 270 and 271:

    Metric Contigs Combined length (bp)

  • Page 272 and 273:

    As such, the increase in total leng

  • Page 274 and 275:

    As such, the predictions of mapping

  • Page 276 and 277:

    Combined length (bp) 450000 425000

  • Page 278 and 279:

    Combined length (bp) 80000 60000 40

  • Page 280 and 281:

    unclustered reads, for random clust

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    Sample 1 - blackberry + suspected b

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    Sample 2 - ivy + supected bacterial

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    Sample 3 - tomato + Pepino mosaic v

  • Page 288 and 289:

    Speed of assembly The time taken fo

  • Page 290 and 291:

    Discussion UT+Psp2126 In previous c

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    of the dataset before and after clu

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    The UT+Psp2126 dataset cannot be th

  • Page 298 and 299:

    7 Abstract Discussion and future di

  • Page 300 and 301:

    pathogen material extracted from th

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    would be beneficial in spite of the

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    investigation might be made into wh

  • Page 306 and 307:

    Sequence assembly As new sequencing

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    al. 2012). This method of character

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    Appendix A-1: Use of perl scripts i

  • Page 312 and 313:

    Appendix A-3 randomSeqWriter.pl #!

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    if (@alphabet < @names) { } foreach

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    Appendix A-4 featureWriter.pl #! /u

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    } print "GC content done...\n"; #ge

  • Page 320 and 321:

    } } #OFDEG if ($seqLength < $shorte

  • Page 322 and 323:

    } } else { } $revtethash{$tetraseq}

  • Page 324 and 325:

    } } $iteration++; @wordSizeArray =

  • Page 326 and 327:

    } } else { } if ($Odist ne "") { }

  • Page 328 and 329:

    } push (@CEF_array, $CEF); #calcula

  • Page 330 and 331:

    Appendix A-5 featureComboWriter.pl

  • Page 332 and 333:

    {$feat}}) { } } } else { } print OU

  • Page 334 and 335:

    \n"; } $rangeSplit[0] = 2; $rangeUL

  • Page 336 and 337:

    Appendix A-7 claraResultsSummariser

  • Page 338 and 339:

    $speciesPresent{$species}; } if (ex

  • Page 340 and 341:

    Appendix A-8 avePRwriter.pl #! /usr

  • Page 342 and 343:

    Appendix A-9 SAMseqAssigner.pl #! /

  • Page 344 and 345:

    } else { } close OUTFH; close PAFH;

  • Page 346 and 347:

    if ($method eq "fuzzyk" || $method

  • Page 348 and 349:

    Appendix A-11 contigInfo.pl #! /usr

  • Page 350 and 351:

    } #grep lists of reads used in each

  • Page 352 and 353:

    } $meanLength = $totalLength/$numCt

  • Page 354 and 355:

    } unless ($spCumLength > ($spSumCon

  • Page 356 and 357:

    } } else { } $seqLine = $_; chomp $

  • Page 358 and 359:

    } } if ($clusters{$ID} == $clusterN

  • Page 360 and 361:

    Appendix B-1 A table detailing the

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    Taxon Genome size Reads used Total

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    Taxon Genome size Reads used Total

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    Taxon Genome size Reads used Total

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    Taxon Genome size Reads used Total

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    Taxon Genome size Reads used Total

  • Page 372 and 373:

    Taxon Genome size Reads used Total

  • Page 374 and 375:

    Species Genus Family Order Class Ph

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    Species Genus Family Order Class Ph

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    Species Genus Family Order Class Ph

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    Species Genus Family Order Class Ph

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    Species Genus Family Order Class Ph

  • Page 384 and 385:

    Species Genus Family Order Class Ph

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    Species Genus Family Order Class Ph

  • Page 388 and 389:

    Species Genus Family Order Class Ph

  • Page 390 and 391:

    Table of Abbreviations Abbreviation

  • Page 392 and 393:

    Abbreviation Term Definition PAM Pa

  • Page 394 and 395:

    Bernardi, G. and G. Bernardi (1986)

  • Page 396 and 397:

    Eisen, J. A. (2007). "Environmental

  • Page 398 and 399:

    Kannan, R., S. Vempala, et al. (200

  • Page 400 and 401:

    Mavromatis, K., N. Ivanova, et al.

  • Page 402 and 403:

    Rico, A., S. L. McCraw, et al. (201

  • Page 404 and 405:

    Teeling, H., J. Waldmann, et al. (2

  • Page 406 and 407:

    Wendl, M. C. (2006). "A general cov

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