Download building trees

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.
hy-phy
Results of an anaylsis using the SLAC approach
more output might still be here
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/
Example testing for dN/dS in two partitions of the data -John’s dataset
Set up two partitions, define model for each, optimize likeliho
Example testing for dN/dS in two partitions of the data -John’s dataset
Save Likelihood Function
then
select as alternative
The dN/dS ratios for
the two partitions are
different.
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)
Example testing for dN/dS in two partitions of the data -John’s dataset
Example testing for dN/dS in two partitions of the data -John’s dataset
Nam
e and
save
as
Nullhyp.
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.
Example testing for dN/dS in two partitions of the data –John’s dataset
Alternatively, especially if the the two models are not nested,
one can set up two different windows with the same dataset:
Model 1
Model 2
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
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
LGT3State Method
Simulated under
"loss-only" model;
likelihood under HGT model
120
Frequency
100
80
Real data
under HGT
model
60
40
20
0
Likelihood values
• Generated 1000
bootstrap trees
under loss-only
model
Niket Shah
Important characteristic
 Hundreds of node
 Robust against accidental failures
 Coordinated attacks
Networks are everywhere
 Brain and nerve cells
 Tiny Cells
 Food webs and eco-systems
Birth of Scale Free Network
 Selection of new node
 Winner takes all
Random versus scale free networks
 Randomly placed connections
 Bell shaped curve
 Same number of links
 Hubs (red)
 Power law
 less connection but more links
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
Potential implications in Medicine
 Vaccination campaigns against serious viruses
 Mapping of human cell
 Prediction of virus or disease if they are dangerous
 Threshold zero
 Spreading virus
 Example of Measles
 90% of total population
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)
Usually "Scale free" only within limits
Human proteome, and its binding
interactions. Depiction of the data
as a hairball, an increasingly
familiar image in the biology
literature.
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.
Bio Layout – can draw hairballs from all against all blast searches and MCl clustering