Gain Loss Mapping Engine |
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**Type your MSA **(FASTA format only)

Required input: The presence and absence matrix. ("1" and "0" or missing data "-")

**Type your phylogenetic tree (Optional) **(Newick format only)

Optional and recommended input: the assumed tree topology (with or without branch lengths);
Note: tree format must not include names for inner nodes

(+) Evolutionary model

gain & loss rates
Fixed gain/loss ratio
Equal gain and loss
Variable gain/loss ratio (Mixture)

Rate distribution
Equal
Gamma
( show more rate distribution)

Allow root freq to differ from stationary ones

Loss only model (gain rate ~ 0)

(+) Correction for un-observable data

Minimum number of ones
0
1
2
3

Minimum number of zeros
0
1

The probabilistic model describing the gain and loss dynamics

gain & loss rates

- "Fixed gain/loss ratio": gain and loss probabilities may be different but the gain/loss ratio is identical across all sites.
- "Equal gain and loss": the probability of a gain event is assumed to be equal to that of a loss event.
- "Variable gain/loss ratio (mixture)": gain/loss ratio varies among sites.

Rate distribution

- "Equal": assume that a single evolutionary rate characterizes all sites.
- "Gamma": among site rate variation, assuming that the rate is gamma distributed

In stationary processes the character frequencies are equal across the entire tree. Use this option to analyze the data using non-stationary models.

(+) Correction for un-observable data

Minimum number of ones

If zero is selected the model allows sites with only zeros to appear (do not account for un-observable data). Select more than one if singletons are also un-observable.

Minimum number of zeros

If zero is selected the model allows sites with only ones to appear. Select one if variable sites are required (e.g., for indel data).

(+) Results
(+) Stochastic mapping

Expected number of events per site

Expected number of events per branch

Probability and expectation of events per site per branch

(+) Parsimony

Parsimony cost of gain
1
2
4
8

Parsimony count of events per site

Parsimony count of events per branch

Parsimony count of events per site per branch

(+) Additional features

Rate per site

Calculate gain and loss per site

Likelihood per site

Estimated tree

Log verbosity level
Normal
High

The stochastic mapping approach infers for each branch and each site the probability and expected number of both gain and loss events. This information is provided both textually (with additional files for sum over branches or over sites) and visually (color-coded phyletic pattern)

Under the parsimonious assumptions for each branch and each site the number of both gain and loss events is computed

Parsimony cost of gain

The relative costs of gain and loss events can be determined by the user. E.g., select: cost of gain=2, if the gain events should twice as costly as loss events

Likelihood based computations.

The posterior estimation of the relative rate of each site

Separate estimation of the gain and loss rates for each site (mixture model only)

The log-likelihood for each site

The tree and its associated branch lengths estimated from the phyletic pattern. A Java applet is available for tree visualization and manipulation.

Log verbosity level

(+) Advanced
Estimate all model parameters using likelihood
Yes
No

Estimate branch lengths using likelihood Yes
No
Yes
No
Lowest
Very Low
Low
Medium
High
Very High

Number of rate categories
2
3
4
5
6
Compute ancestral reconstuction of states

Likelihood estimation of parameters can be avoided by setting their values based on character counts directly from the phyletic pattern.

Estimate branch lengths using likelihood

Likelihood estimation of branch lengths can be avoided and the input branch lengths or initial estimation is used.

Estimate parameters with multiple random starting points
Choose this option to avoid local maxima in maximum likelihood estimation of parameters

Optimization level
Running times can be modified by changing the optimization level.

Number of rate categories

The continues gamma distribution is approximated with discrete number of categories. For number of categories > 4 the computation time may be long.

(+) Simulate phyletic patterns

Choose this option to perform phyletic patterns simulation with stochastic mapping and maximum parsimony detection of events for simulated data.

The phyletic patterns are simulated along the input tree with number of organisms corresponding the input MSA (input phyletic pattern)

The phyletic patterns are simulated along the input tree with number of organisms corresponding the input MSA (input phyletic pattern)

Simulated evolutionary scenario
Rate equal among sites; gain=loss
Rate equal among sites; gain/loss variability among sites
Rate variability among sites; gain=loss
Rate variability among sites; gain/loss variability among sites
Stochastic mapping estimation of simulated evolutionary rates based on input data
Maximum parsimony estimation of simulated evolutionary rates based on input data
loss/gain ratio
1
2
4
8
16
Number of site to simulate
1000
2000
5000
10000
Number of replication for each simulation scheme
1
2
5
10

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