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where to get help

where to get help. read the manuals and help files check out the discussion boards at http://www.rannala.org/phpBB2/. else. there is a new program on the block called hy-phy (=hypothesis testing using phylogenetics). The easiest is probably to run the analyses on the authors datamonkey.

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where to get help

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  1. where to get help read the manuals and help files check out the discussion boards at http://www.rannala.org/phpBB2/ else there is a new program on the block called hy-phy(=hypothesis testing using phylogenetics). The easiest is probably to run the analyses on the authors datamonkey.

  2. hy-phy Results of an anaylsis using the SLAC approach more output might still be here

  3. Hy-Phy -Hypothesis Testing using Phylogenies. Using Batchfiles or GUI Information at http://www.hyphy.org/ Selected analyses also can be performed online at http://www.datamonkey.org/

  4. Example testing for dN/dS in two partitions of the data --John’s dataset Set up two partitions, define model for each, optimize likelihood

  5. Example testing for dN/dS in two partitions of the data --John’s dataset Save Likelihood Function thenselect as alternative The dN/dS ratios for the two partitions are different.

  6. Example testing for dN/dS in two partitions of the data --John’s dataset Set up null hypothesis, i.e.: The two dN/dS are equal (to do, select both rows and then click the define as equal button on top)

  7. Example testing for dN/dS in two partitions of the data --John’s dataset

  8. Name and save as Null-hyp. Example testing for dN/dS in two partitions of the data --John’s dataset

  9. Example testing for dN/dS in two partitions of the data --John’s dataset After selecting LRT (= Likelihood Ratio test), the console displays the result, i.e., the beginning and end of the sequence alignment have significantly different dN/dS ratios.

  10. Alternatively, especially if the the two models are not nested, one can set up two different windows with the same dataset: Example testing for dN/dS in two partitions of the data –John’s dataset Model 1 Model 2

  11. Example testing for dN/dS in two partitions of the data --John’s dataset Simulation under model 2, evalutation under model 1, calculate LR Compare real LR to distribution from simulated LR values. The result might look something like this or this

  12. 16S rRNA phylogeny colored according to tyrRS type Under the assumption that both types were present in the bacterial ancestor and explaining the observed distribution only through gene loss: 133 taxa and 58 gene loss events, 34 losses of type A, 23 of type B Green - Type A tyrRS Red - Type B tyrRS Blue - Both types of tyrRS Andam, Williams, Gogarten 2010 PNAS

  13. LGT3State Method Simulated under "loss-only" model; likelihood under HGT model • Generated 1000 bootstrap trees under loss-only model Real data under HGT model

  14. Scale Free Network Niket Shah

  15. Important characteristic • Hundreds of node • Robust against accidental failures • Coordinated attacks

  16. Networks are everywhere • Brain and nerve cells • Tiny Cells • Food webs and eco-systems

  17. Birth of Scale Free Network

  18. Selection of new node • Winner takes all

  19. Random versus scale free networks • Randomly placed connections • Bell shaped curve • Same number of links • Hubs (red) • Power law • less connection but more links

  20. Growth and preferential attachment • Andreas Wagner of University of New Mexico • David Fell of Oxford Brookes University of England • Escherichia coli experiment • Most connected molecules have early evolutionary history

  21. Potential implications in Medicine • Vaccination campaigns against serious viruses • Mapping of human cell • Prediction of virus or disease if they are dangerous

  22. Threshold zero • Spreading virus • Example of Measles • 90% of total population

  23. EVERYTHING CAN BE TURNED INTP A HAIRBALL! The hairball is the icon of systems biology (as the DNA double helix was the icon for molecular biology)

  24. Human proteome, and its binding interactions. Depiction of the data as a hairball, an increasingly familiar image in the biology literature. Usually "Scale free" only within limits

  25. Réka Albert: Scale-free networks in cell biology. Journal of Cell Science 118, 4947-4957 &Albert-László Barabási and Eric Bonabeau: Scale-Free Networks. Scientific American, April 14, 2003 Comparison between the degree distribution of scale-free networks (O) and random graphs (☐) having the same number of nodes and edges.

  26. Bio Layout – can draw hairballs from all against all blast searches and MCl clustering

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