Slides of his talk - School of Mathematical Sciences - University of ...

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Illumina Sequencing Technology  3’ 5’ DNA (0.1-1.0 ug) Sample preparation usually involves PCR and size selection Single Cluster molecule growth array 1 2 3 4 5 6 7 8 9 A C T C T G C T G A A G 5’ T G C T A C G A T A C C C G A T C G A T Sequencing T G C T A C G A T … Image acquisition Base calling Slide courtesy <strong>of</strong> G Schroth, Illumina 12
35+ bp reads! Reads mapped to the reference genome! 13
Page 1 and 2: Dealing with the GC-content bias in
Page 3 and 4: First, note base pairing:! A with T
Page 5 and 6: Two hydrogen bonds! 5
Page 8 and 9: GC content here 445/1000! 8
Page 10 and 11: Ability to ! denature! All depend o
Page 14 and 15: Our original motivation:   DNA co
Page 17 and 18: Counts are normalized in 20 kb bins
Page 19 and 20: GC content biases first noted! They
Page 21 and 22: Another view: a human data set! •
Page 23 and 24: Our goals! • To study the nature
Page 25 and 26: Digression: mappability! • Some %
Page 27 and 28: Most of the time we present results
Page 29 and 30: Is the GC-bias specific to a lab, p
Page 31 and 32: Is there a right bin size?  Peopl
Page 33 and 34: Avoiding binning: single position a
Page 35 and 36: Whatʼs interesting about these  
Page 37 and 38: In symbols, ! ˆ " = F gc , gc N gc
Page 39 and 40: Varying the window size from a fixe
Page 41 and 42: Interim conclusion from many such p
Page 43 and 44: Forward and reverse strands behave
Page 45 and 46: Fragment size matters   Rates by
Page 47 and 48: Predicted rates at a given mappable
Page 49 and 50: Some other biases/models! 49
Page 51 and 52: End effects! GC before fragment GC
Page 53 and 54: How well does our correction work?!
Page 55 and 56: How well does our correction work?!
Page 57 and 58: Copy number: crude corrections to t
Page 59: Many thanks to! • Yuval Benjamini
Page 62 and 63:
Other examples and phenomena  (if
Page 64 and 65:
Plots for ChIP-seq sample rep 1, A.
Page 66:
the fragments based on their GC, an
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