analysis of the bread wheat genome using whole- genome shotgun sequencing manuel spannagl mips,...
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Analysis of the bread wheat genome using whole-genome shotgun sequencing
Manuel SpannaglMIPS, Helmholtz Center Munich
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Wheat - why bother?
① Many varieties incl. bread wheat, durum („pasta“) wheat…
② Third most-produced cereal with 651 millions tons (2010), cultivated worldwide in different climates
③ Leading source of vegetable protein in human food
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The Challenge
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Wheat – a WGS approachAims and Goals
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① 5x 454 WGS sequencing => 85 Gb sequence, 220 million reads
② ~79% of reads repeat-related
③ direct Low-copy-number genome assembly (LCG, Newbler) => collapses many homologous gene sequences
④ to prevent collapsing of homologous gene sequences and reduce complexity => orthologous group assembly at high stringency
Wheat – a WGS approach
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① Use fully sequenced and analysed reference genomes (rice, Brachypodium, sorghum)
② Group genes into families (Orthologous Groups)
③ Use the orthologous group representatives as sequence baits to capture corresponding sequence reads.
④ Do sub-assembly for each „orthologous bin“ seperately
WGS assembly using „in silico exon capture“
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Bread Wheat Genaology
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Ortholome directed assembly circumvents limitations faced by WGS assembly
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The ortholome directed assembly delivers ordered segments
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The ortholome directed assembly delivers ordered segments II
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Gene Copy Retention after Polyploidization- Calibration of the method-
97% 99% 100%
Maize
Hexaploid Rice„TRice“
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Gene Copy Retention after Polyploidization
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Gene Copy Retention after Polyploidization
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Expanded Wheat Gene Families
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Shotguns (Illumina 80x (T.monococcum)) and 454 (3x (Ae.tauschii))
cDNA seq‘s from the Ae. speltoides group (B)
Can A and D genome shotgun data be used to dissect the ABD of wheat?
The Three Nephews: the A, B and D‘s of wheat
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The Three Nephews: Similarity on a Sequence Basis
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Wheat A, B and D Assignment using Machine Learning (SVM)
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Particular Gene Categories are preferentially retained
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Franz Marc„Hocken im Schnee“
Almost full gene complement detected and structured
10000s of pseudogenes detected
Separation of A, B and D using machine learning with > 75% accuracy
Complementary to chromosome sorting approaches
Applicable to polyploids in general to get genome overview
Rapid and economic approach to pragmatically cope with limitations in sequence technology
Summary
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acknowledgements
MIPSMatthias PfeiferKlaus MayerAll other group members
The UK Wheat ConsortiumMike BevanNeil HallAnthony HallKeith EdwardsRachel Brenchley
CSHLDick McCombie
UC Davis & USDA AlbanyJan DvorakMincheng LuoOlin Anderson
Kansas State UniversityBikram GillSunish Segal
EBIPaul KerseyDan Bolser