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

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GC matters! 2
First, note base pairing:! A with T, C with G! G with C, T with A! 3
Page 1: Dealing with the GC-content bias in
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 12 and 13: Illumina Sequencing Technology  3
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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Slides of his talk - School of Mathematical Sciences - University of ...

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