Estimating the contribution of sequence context to nucleotide - - PowerPoint PPT Presentation

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Estimating the contribution of sequence context to nucleotide - - PowerPoint PPT Presentation

May 31, 2023 122 likes •364 views

Estimating the contribution of sequence context to nucleotide substitution rate heterogeneity Helen Lindsay and Gavin A. Huttley The Gamma Model Yang (1993) used a gamma distribution to model rate variation in - and - globin genes

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SLIDE 1

Estimating the contribution of sequence context to nucleotide substitution rate heterogeneity

Helen Lindsay and Gavin A. Huttley

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SLIDE 2

The Gamma Model

  • Yang (1993) used a gamma distribution

to model rate variation in α- and β- globin genes

  • The gamma distribution is often

approximated by four equi-probable bins

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SLIDE 3

Gamma rate variation

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SLIDE 4

Improvements on the Gamma model

  • Allow sites to change rates
  • Allow clustering of rates
  • Consider other/multiple rate

distributions

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SLIDE 5

What causes substitution rate variation?

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SLIDE 6

What causes substitution rate variation?

Natural selection

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SLIDE 7

What causes substitution rate variation?

Natural selection Differential repair

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SLIDE 8

What causes substitution rate variation?

Natural selection Differential repair Nucleotide properties

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SLIDE 9

AG CG TG (slow) (fast)

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SLIDE 10

Data

  • 470 alignments, each 50 000

nucleotides long, of introns from human, chimpanzee and macaque one- to-one orthologs.

  • Sampled from Ensembl version 49.
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SLIDE 11
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SLIDE 12

The baseline model

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SLIDE 13

The CpG model

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SLIDE 14

The Gamma Model

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SLIDE 15

Gamma vs Dinucleotide models

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SLIDE 16

Gamma vs Dinucleotide models

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SLIDE 17

Gamma vs Dinucleotide models

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SLIDE 18

186.05 40.77 51.07 175.52

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SLIDE 19

Accounting for CpG substitutions decreases rate variation

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SLIDE 20
  • Independent sites
  • Reversible
  • Compositional

variance

Alignment position (nucleotides) G+C% G+C%(alignment) GA GG rate

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SLIDE 21

Advantages of dinucleotide models

  • Less likelihood computation
  • Equivalently parameter-rich
  • No assumed distribution of rate

variation

  • Can incorporate known mutation

biases, for example deamination of methylated cytosine.

  • Smaller alphabet than amino acids
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SLIDE 22

Acknowledgements

Australian National University

  • Gavin Huttley
  • Hua Ying

University of Singapore

  • Von Bing Yap