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	
  
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Self-­‐Updating	
  Platform	
  for	
  the	
  Estimation	
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  Rates	
  
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taxon coverage
data
available
A1 A4 A7 A2
!"## ...
Effect	
  of	
  data	
  mining	
  parameterization	
  
h#p://www.supersmart-­‐project.org	
  -­‐	
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0 5 10 15 20...
Recovering	
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  simulated	
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data sim
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Install	
  a	
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Acknowledgements	
  
h#p://www.supersmart-­‐project.org	
  -­‐	
  @rvosa	
  
The	
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• Alexandre	
  Antonelli	...
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Self-Updating Platform for the Estimation of Rates of Speciation, Migration And Relationships of Taxa: SUPERSMART

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Slides for my lightning talk on the SUPERSMART platform to the SSB/SSE/ASN annual meeting, Austin, TX, USA. SSB Spotlight Session: "Next generation phylogenetic inference 2". Monday, June 20th 2016, 3:20PM, Ballroom A.

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Self-Updating Platform for the Estimation of Rates of Speciation, Migration And Relationships of Taxa: SUPERSMART

  1. 1.    h#p://www.supersmart-­‐project.org   Self-­‐Updating  Platform  for  the  Estimation  of  Rates   of  Speciation,  Migration,  And  Relationships  of  Taxa   Rutger  Vos,  Naturalis  Biodiversity  Center,  Leiden,  the  Netherlands   @rvosa  
  2. 2. Methods  to  construct  large  species  trees   !"#$%&'()*+),- .+,$/0$1(+)+2$,3++- !"#$%&%$$ 4%&'53%,+ Tree inference !"#$%&'()*+),- Tree inference (same study) Supertree methods (e.g. MRP) .67+3*%,3'8 !"#$%'(&%)* 4%&'53%,+ Concatenation Tree inference (e.g. ML, Bayesian) 9'-,$/0$-7+4'+-$ 1/3$3//,$,%8%2 4%&'53%,+ :%4;5/)+$,3++ 1(+)6-$&+<+&2 =6&&$%&'()*+), 1-7+4'+-$/3$5+&/>2 !+,-.!/0.1 3+?4%&'53%,+ Identify & align orthologous sequences; Tree inference Decompose & add markers Infer trees & place in backbone !! " # # # # # $ $ $ $ $ % % % % % " " " " " && & & &&&& # # # # # # # # # # $ $ $ $ $ % % % % % $ $ $ $ $ % % % % % $ $ $ $ $ " " " " " ! ! ! % % % % % $ $ $ $ $ " " " " " !! " # # # # # $ $ $ $ $ % % % % % " " " " " !! " & & & &&&&&&&& ! $ &&& & && !! " # # # # # $ $ $ $ $ % % % % % " " " " " & & & & &&&& # # # # # # # # # # $ $ $ $ $ % % % % % $ $ $ $ $ % % % % % $ $ $ $ $ " " " " " ! ! ! % % % % % $ $ $ $ $ " " " " " !! " # # # # # $ $ $ $ $ % % % % % " " " " " !! " & & & &&&&&&&& ! $ && & & && !! " # # # # # $ $ $ $ $ % % % % % " " " " " ! # # # # # # # # # # $ $ $ $ $ % % % % % $ $ $ $ $ % % % % % $ $ $ $ $ " " " " " ! ! % % % % % $ $ $ $ $ " " " " " ! " # # # # # $ $ $ $ $ % % % % % " " " " " !! " ! ! ! $ ! ! !! " !! " # # # # # $ $ $ $ $ % % % % % " " " " " ! # # # # # # # # # # $ $ $ $ $ % % % % % $ $ $ $ $ % % % % % $ $ $ $ $ " " " " " ! ! % % % % % $ $ $ $ $ " " " " " ! " # # # # # $ $ $ $ $ % % % % % " " " " " !! " ! ! ! $ ! ! !! " # # # # # # # # # # $ $ $ $ $ % % % % % $ $ $ $ $ % % % % % $ $ $ $ $ " " " " " ! ! ! ! % % % % % $ $ $ $ $ " " " " " !! " # # # # # $ $ $ $ $ % % % % % $ $ $ $ $ % % % % % $ $ $ $ $ " " " " " !! ! ! #" # # # # $ $ $ $ % % % % " " " " !! " # # # # # # # # # # $ $ $ $ $ % % % % % $ $ $ $ $ % % % % % $ $ $ $ $ " " " " " ! ! ! ! % % % % % $ $ $ $ $ " " " " " !! " # # # # # $ $ $ $ $ % % % % % $ $ $ $ $ % % % % % $ $ $ $ $ " " " " " !! ! ! #" # # # # $ $ $ $ % % % % " " " " !! " h#p://www.supersmart-­‐project.org  -­‐  @rvosa  
  3. 3. The  SUPERSMART  algorithm   !" #" $" #% &% &" !% $% &% &' &" #% #" #' !' !" !% $% $' $" !' $" &' $' !" #% #" !% &% &" #' $% !" #" $" 80 0 Time (Ma) 80 0 Time (Ma)Relative time 1 0 h#p://www.supersmart-­‐project.org  -­‐  @rvosa  
  4. 4. Data  mining   h#p://www.supersmart-­‐project.org  -­‐  @rvosa   taxon coverage data available A1 A4 A7 A2 !"## $# #%$#! !" #" "" "$! $%%% S1 S2 S6 S5 S3 S7 !"# %$% "$!" & & & & & & & & & & & & & "!"# %% $! "#" $& & & & & & & & & & & !" #" #" " & & & & & && & & & & & & & & & %!" % #" " ! #" $$& & & & & & & & & & !"## $# "# $! !"""& & & & & & & & & A3 S1 S2 S6 S5 S3 S7 S4 A1 A 3A 2 A 4 A 5A6A 7 !"##%$ !"# %$% "!"# %% !"##%$# !"##%$# !" #" !" #" !""" %!" % $! "#" $ $! #" $$ #" " $! $%%% #" " "" " #%$#! "# $! "$!" %$#!" $ %$!" $ $#" $#" Alignments Exemplarspecies Minimally sparse supermatrix (minimum of two markers per exemplar species) $
  5. 5. Effect  of  data  mining  parameterization   h#p://www.supersmart-­‐project.org  -­‐  @rvosa   0 5 10 15 20 0.050.100.150.20 Averaged posterior probabilities on nodes Minimum marker coverage per taxon Maximumaverageduncorrectedpairwisedistance p  <  0.8   0.8  <  p  <  0.95   p  >  0.95  
  6. 6. Recovering  a  simulated  tree   Simulated Tree Re-estimated Tree data sim 0.000.10 data sim 60708090 % of invariant sites per alignment % data sim 0102030 Number of indels per alignment count data sim 04080120 data sim 0200400 data sim 02040 average distance within alignment relativeeditdistance Average size of indels per alignment size(nucleotides) Number of gaps per sequence gapcount % gaps per sequence % !" #" time (myr) 80 0 0 80 h#p://www.supersmart-­‐project.org  -­‐  @rvosa  
  7. 7. Install  a  vagrant  box,  then:  $  smrt [COMMAND] [OPTIONS]   h#p://www.supersmart-­‐project.org  -­‐  @rvosa  
  8. 8. Acknowledgements   h#p://www.supersmart-­‐project.org  -­‐  @rvosa   The  smar,es:   • Alexandre  Antonelli   • Rutger  Vos   • Hannes  He#ling   • Mike  Sanderson   • Bengt  Oxelman   • Karin  Nilsson   • Mats  Töpel   • Hervé  Sauquet   • Henrik  Nilsson   • Daniele  Silvestro   • Fabien  Condamine   • Ruud  Scharn   Thank  you  to:   • Erick  Matsen   • Meg  Pirrung   • You,  the  audience!  
  • Vhjkjh

    Feb. 24, 2021

Slides for my lightning talk on the SUPERSMART platform to the SSB/SSE/ASN annual meeting, Austin, TX, USA. SSB Spotlight Session: "Next generation phylogenetic inference 2". Monday, June 20th 2016, 3:20PM, Ballroom A.

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