Modeling Bayesian Phylogenetic Inference in Protein

Data Analysis by Using Mr. Bayes, Proml, Consensus

Applications

Mr. Bayes vs. Proml (maximum likelihood)

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Diff. of Maximum Likelihood(Mr. Bayes – Proml)

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Diff. of Maximum Likelihood(Mr. Bayes – Proml)

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Linear Regression in Testing Datasets

linear regression

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Testing Datasets Plus One/Two Long Branch’s Datasets

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mrbayes vs proml (plus AB,CD data)

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Linear Regression After Bayesian Correction for Testing Datasets & One/Two Long Branch’s Datasets

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Phylogeny for All Testing Datasets

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Phylogeny for All Datasets

phylogeny for all datasets

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One Long Branch Datasets

one long branch

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Two Long Branches Datasets

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Phylogeny (sequence length from Proml)

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AB50J

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CD20J

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One/Two Long Branch’s Datasets(Maximum Likelihood)

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Data Analysis• Testing datasets: phy01 ~ phy21, nexus01

~ nexus21)

• Experimental datasets: one long branch (AB10J ~ AB70aj), two long branches (CD10J ~ CD70aj)

• Operation systems: Mac OS X ver. 10.3.9

• Dual 800 MHZ PowerPC G4

• 256 MB SDRAM• Mr. Bayes – 3.1.1

• Phylip 3.67 (Proml, Consensus)

continue• Testing sample size: 21x2• Experimental samples: 7x2• Degree of freedom: 20• Chi square: 283.1561 > 31.41(alpha=0.05)• Proml and Mr. Bayes are two dep val.• ANOVA Ssw=2669051, Ssb=24253093• Sstotal=50943143.71• Eta square= 0.476081596• Type I error=0.05• Type II error=1.83%• Power= 98.17%• Instrument threshold=1xE-8

Testing Datasets

y(Mr.Bayes)= 1.058351726x(Proml)+14.79771

0.999724correl

14.79771intercept

1.058352slope

Testing datasets in linear regression between Mr. Bayes and Proml)

104.6243131.7778878.5355sd

226.959296.33331576.467mean

diff(Mr.Bayes-Proml)characterCPU

Testing samples:

0.996717correl

0.109857f-test5343.856intercept

3.47E-05t-test0.492193slope

Linear regression between experimental samples:

364.0589179.5717sd

13122.8611802.88mean

CD(two long branches)AB(one long branch)

Experimental samples:

Linear Regression for All

y(Mr. Bayes)= 1.058352x(Proml)+14.79764

0.999959correl

14.79764intercept

1.058352slope

Linear regression for all datasets(including experimental and testing)

385.302190.05sd

13903.512506.4mean

CD(two long branches)AB(one long branch)

After Bayesian modeling

Tree Hierarchical Structure: AB10J• AB10J.JTT• +----------seq.7 • | • +-----5 +---------seq.4 • | | | • | +---2 +-------seq.6 • | | +----4 • | +----3 +----------seq.5 • | | • | +-----------seq.2 • | • 1------------------------------------------seq.3 • | • +--------seq.1 • AB10J.consensus• +--------------------seq.4• |• +--1.0-| +------seq.6• | | +--1.0-|• | +--1.0-| +------seq.5• +------| |• | | +-------------seq.2• | |• | | +------seq.1• | +----------------1.0-|• | +------seq.3• |• +----------------------------------seq.7

Histogram AB10J

AB10J

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Tree Hierarchical Structure:CD10J• CD10J.JTT• +----seq.7 • | • +--5 +----seq.4 • | | | • | +-2 +-------------------seq.6 • | | +-4 • | +--3 +-----seq.5 • | | • | +-----seq.2 • | • 1---seq.3 • | • +---------------------seq.1 • CD10J.consensus• +--------------------seq.4• |• +--1.0-| +------seq.6• | | +--1.0-|• | +--1.0-| +------seq.5• +------| |• | | +-------------seq.2• | |• | | +------seq.1• | +----------------1.0-|• | +------seq.3• |• +----------------------------------seq.7

Histogram CD10J

CD10J

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Discussion

• Bayesian modeling can be used to evaluate type I,II errors, eta square, power, Chi square X2, Anova, correlated coefficient, linear regression etc ..

• It is possible to design a 2x2 table in order to evaluate risk such as RD, RR, RO

• Proml and consensus features bring out a histogram’s profile including hierarchical tree structure and it is possible for peak area integration

Questions

• CPU time can be used to count all activities in hydrogen bonds through kinesthetic module in computer, and hydrogen bond’s configurations of DNA match from pairs of A-T, A-U. C-G, and/or DNA alignment from separate genetic codes of A, T, U, C, G.

• CPU time is possible to count all triggering by stem cell activity through functional proteins.

• CPU time has been already used in Forensic science to count pattern differentiation from suspect sample in judiciary investigations.