Can phylogenomic data matrix end incongruence in the tree of life?

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1 Can phylogenomic data matrix end incongruence in the tree of life? 系统发育基因组数据能否解决生命之树中的冲突? Xing-Xing Shen( 沈星星 ) Rokas Lab Mar

2 自我简介 2

3 Course / workshop Workshop on Molecular Evolution (Since 1988) Workshop on Phylogenomics (Since 2017) 3

4 4 Phylogenetics

5 Utilities of phylogenetic tree Relationship Species delimitation Divergence time and Biogeography Evolutionary process (selections, ancestral state) 5

6 6 Molecular Phylogenetics

7 No. of genes Histories of data sampling High Phylogenomics Low 7

8 Why we need many genes? Gene A Gene B 8 Rokas & Chatzimanolis (2008) in Phylogenomics (W. J. Murphy, Ed.)

9 Histories of data sampling 9 (Slide from Casey Dunn)

10 Phylogenomics Phylogenomics: inference of species phylogeny with lots of genes Whole genome Whole transcriptome Targeted enrichment Directed PCR 10 (Slide from Casey Dunn)

11 Phylogenomics 11 Delsuc et al. 2005

12 Phylogenomic power 12 (Slide from Antonis Rokas)

13 13 But Incongruences still exist in phylogenomic studies

14 Incongruence: avian Kimball et al, MPE 2013

15 Incongruence: squamates Mitochondrial genome Castoe et al. PNAS 2009

16 Incongruence: Ascoideaceae ( 酱霉科 ) ML BS=82 ML BS=100 1,559-gene and 38-taxon Riley et al. PNAS ,233-gene and 86 yeasts Shen et al. G3 2016

17 How could we quantify phylogenetic signal for each gene?

18 Maximum Likelihood ( 最大似然法 ) Substitution model 18

19 Maximum Likelihood ( 最大似然法 ) Sp1 Sp3 Sp1 Sp2 Sp1 Sp2 Sp2 Sp4 Sp3 Sp4 Sp4 lnl= lnl= lnl= Sp3

20 Maximum Likelihood (ML) ML tree lnl= N i = 1 log(l i Tree) (Slide from Derrick Zwickl)

21 Maximum Likelihood ( 最大似然法 )

22 Maximum Likelihood ( 最大似然法 ) A T Sp1=A Sp2=T 3 unrooted trees C A G T Sp3=C Sp4=G G C A G T C

23 Likelihood ( 似然值 ) A C T G Y (1) A C T G T A X L (1) =π A * P AA *P AC *P AT *P TT *P TG (16) A C T G G C L (16) =π G * P GA *P GC *P GC *P CT *P CG L(site1 tree) =L (1) + L (2) + + L 16

24 Likelihood ( 似然值 ) Sp1=A C Sp2=T T Sp3=C C Sp4=C C A C T G X Y L(Data tree) = L(site1 tree) * L(site2 tree) * L(siten tree) Log L(Data tree) = Log L(site1 tree) + Log L(site2 tree)..+log L(siten tree) lnl= N i = 1 LnL i

25 Maximum Likelihood (ML) ML tree lnl= N i = 1 log(l i Tree) (Slide from Derrick Zwickl)

26 Maximum Likelihood (ML):gap/missing/ambiguous characters Sp1= A Sp2= G 1 3 Sp3= G Sp4= C Sp5= G Sp1= A Sp2= G A T Sp3= G C Sp4= C Sp5= G - C

27 Site-wise log likelihood support (SLS) 1 lnl= N i = 1 SLS i 27

28 Incongruence: Ascoideaceae ( 酱霉科 ) ML BS=82 ML BS=100 1,559-gene and 38-taxon Riley et al. PNAS ,233-gene and 86 yeasts Shen et al. G3 2016

29 Site-wise log likelihood support (SLS) T1 T2 SLS 1 =log(p 1 T1) - log(p 1 T2)=

30 Gene-wise log likelihood support (GLS) GLS 1 =lnl(g 1 T1) - lnl(g 1 T2)=

31 ΔGLS ΔGLS plot DPM1 A single gene displays very strong difference in gene-wise log likelihood scores for T1 against T2. 31

32 ΔGLS Removal DPM1 32

33 Removal of the strongest gene ML BS=100 ML BS=46 1,233-gene (T1) Shen et al. G ,232-gene (T2) Shen et al. G Removal of the strongest gene witched the ML tree s support from T1 to T2 33

34 Contentious relationships in phylogenomic studies can be driven by a handful of genes 34

35 Improved sampling 35

36 Sampling of 3 Additional Taxa Breaks the Long Branch... GLS (DPM1) = 340 GLS (DPM1) = ~20 Phaffomycetaceae Saccharomycetaceae / Saccharomycodaceae 36

37 Three large phylogenomic data matrices Plant:103 taxa and 674 gens Wickett et al. PANS 2014 Animal: 58 taxa and 4682 genes Chen et al. Syst Biol 2015 Fungi: 86 yeasts and 1233 genes Shen et al. G3 2016

38 ΔGLS plot (17 branches) 6 branches show a single or a handful of genes display very strong ΔGLS 38

39 Phylogenomics 39 Delsuc et al. 2005

40 ΔSLS Difference in Site-wise log likelihood support (Δ SLS) within individual gene Sites 40

41 Conclusions A tiny amount of data in very large phylogenomic data matrix can drive the resolution of specific internodes Why it happens? Wrong sequence, paralogs, HGTs Distribution of phylogenetic signal for each of the main alternative hypotheses Majority of genes support your results? 41

42 Small case on the Figshare 42

43 常规 PCR 简并引物 43

44 标准 PCR 结果 测定非特异性扩增 PCR 产物需要许多额外工作 PCR product Gel cutting Cloning Cleanup Sequencing 44 Shen et al (Mol Biol Evol)

45 PCR 高效的巢式 PCR 简并引物 1ST RUN: 简并引物 2ND RUN: 45 Shen et al (PLoS One)

46 比较标准 PCR 和巣式 PCR 扩增效果 46 巢式 PCR 显著提高 NPCL 扩增成功率和易产生单一且亮的目标条带 Shen et al (PLoS One)

47 两栖动物 : 有尾目 Hynobiidae (3) Cryptobranchidae (1) Sirenidae (2) Ambystomatidae (1) Dicamptodontidae (1) Salamandridae (3) Proteidae (2) Rhyacotritonidae (1) Plethodontidae (4) Amphiumidae (1) 47 Shen et al (MBE)

48 分子标记 PCR 结果 BPTF MIOS LPHN2 LIG4 48 Shen et al (Mol Biol Evol)

49 Thank you! 49