ZORRO : A masking program for incorporating Alignment Accuracy in

Phylogenetic Inference

Sourav ChatterjiMartin Wu

Probabilistic Masking using pair-HMMs

• Probabilistic formulation of alignment problem.

• Can answer additional questions– Alignment Reliability– Sub-optimal Alignments

Durbin et al., Cambridge University Press (1998)

Probabilistic Masking

• What is the probability residues xi and yj are homologous?

• Posterior Probability the residues xi and yj are homologous

• Can be calculated efficiently for all pairs (and gaps) in quadratic time.

y]y]Pr[x,Pr[x,y]y]x,x,,,yyPr[xPr[x

]]yyPr[xPr[x jjiijjii

An Ideal Weighting Scheme

• Accounts for correlations between pairs– e.g. A-C and A-D

• Accounts for distance between the sequences in a pair– e.g. C-D

The Zorro Weighting Scheme

4 3

3

3 3

Calculate Ne, the number of pairs that share an edge e.

The Zorro Weighting Scheme

4 3

3

3 3

•Normalize the edge weight by Ne.•Weight of a pair is sum of normalized weights of edges on the path.

Scoring Multiple Alignment Columns

• Calculate the “posterior probability matrix” and weights wij for every pair of sequences.

• Weighted “sum of pairs” score for column r :

jji,i,ijij

jjiijji,i,

ijij

ww

]]rrPr[rPr[rww

Some Notes

• Improve Running Time– Sample a subset of pairs– Performance almost similar

• Using Confidence Scores– Cutoff Based Scheme (we use 0.5)– Weighted Sampling of columns according to

confidence scores.

Testing

The Balibase 3.0 Benchmark Database

Testing

• Realign sequences using MSA programs like Clustalw.

• Sensitivity: for all correctly aligned columns, the fraction that has been masked as good

• Specificity: for all incorrectly aligned columns, the fraction that has been masked as bad

Performance

Gblocks

ZORRO

Sensitivity Specificity

96.3% 95.1%

54.4% 94.7%

Effect on Phylogenetic Inference

• Gblocks data-set– Protein Sequences obtained by simulating

evolution on known trees– Diversity in data-set• Topology (Symmetric/Asymmetric)• Evolutionary Rates• Alignment Lengths (not tested yet)

Effect on Phylogenetic Inference

Protocol Symmetric Tree Inference Accuracy

Asymmetric Tree Inference Accuracy

No Masking 95.17% 91.95 %

Gblocks 84.14 % 86.44 %

Prob. Masking 93.56% 93.33 %

Clustalw alignments, PhyML tree

Effect on Phylogenetic Inference

Protocol

Symmetric Tree Inference Accuracy

Asymmetric Tree Inference Accuracy

All HighSupport All High

Support

No Masking 94.25 % 69.23% 91.95 % 57.44%

Gblocks 89.2 % 57.44% 90.80 % 51.88%

Prob. Masking 94.02% 68.21% 93.79 % 62.05%

MAFFT alignments, PhyML tree

Effect on Phylogenetic Inference

Clustalw alignments, PhyML tree

Protocol

Symmetric Tree Inference Accuracy

Asymmetric Tree Inference Accuracy

All HighSupport All High

Support

No Masking 95.17% 62.05% 91.95 % 55.38%

Gblocks 84.14 % 41.03% 86.44 % 37.95%

Prob. Masking 93.56% 72.31% 93.33 % 63.59%

Effect on Phylogenetic Inference

Muscle alignments, PhyML tree

Protocol

Symmetric Tree Inference Accuracy

Asymmetric Tree Inference Accuracy

All HighSupport All High

Support

No Masking 94.71% 71.28% 93.10 % 61.03%

Gblocks 89.43 % 57.95% 90.11 % 50.26%

Prob. Masking 93.56% 70.77% 95.17 % 64.62%

Conclusions/Future Work

• Technical Issues– What if a few sequences are “bad”/non-

homologous?– Incorporate reliability in likelihood equation and

Bayesian methods.• With Dr. Darling in July

• Testing– “Real” Data Sets?