Co-evolution of Presence-Absence Patterns |
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**Type your Presence-Absence data **(FASTA or NEXUS or PHYLIP formats only)

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

Note: presence/absence within a genomes is coded similarly to DNA or amino acids within a gene (e.g., as rows in the Fasta file)

Note: presence/absence within a genomes is coded similarly to DNA or amino acids within a gene (e.g., as rows in the Fasta file)

**Type your phylogenetic tree (Optional) **

Optional and recommended input: the assumed tree topology (with or without branch lengths)

**The format of the phylogenetic tree is:
**

**Type your characters annotation (e.g., genes description). (Optional) **

One line for each gene/position.

For format example Use Load an example.

(+) Co-evolution computation

Co-evolutionary interactions significance level (alpha)
0.05
0.01
0.001
1

Accuracy level of co-evolution inference
Fast and approximate
Normal speed and accuracy
Slow and accurate

Log verbosity level
Normal
High

(+) Advanced co-evolution parameters
Set manually the minimal number of events required in characters to look for co-evolution
Yes
No

Simulations-based inference of significant co-evolutionary interactions

Co-evolutionary interactions significance level (alpha)

Minimal value for reported co-evolutionary interactions after correction for multiple tests (i.e., the False Discovery Rate cutoff is a lower value derived from the number of tested interactions).

If '1' is selected, all possible co-evolutionary interactions are computed and reported.

If '1' is selected, all possible co-evolutionary interactions are computed and reported.

Accuracy level of co-evolution inference

Higher accuracy level allow better estimation of the statistical significance for reported co-evolutionary interactions, but result with longer running duration.

Log verbosity level

(+) Advanced co-evolution parameters

(+) Evolutionary model

Rate distribution
Equal rate for all characters
Gamma
Gamma plus invariant category

gain & loss rates
Variable rate among sites
Variable gain/loss ratio (Mixture)

gain rate defines 0 => 1 dynamics, while loss rate defines 1 => 0 dynamics.

(+) Correction for un-observable data

Minimum number of ones
0
1
2
3

Minimum number of zeros
0
1

(+) Estimation of model parameters

Estimate branch lengths using likelihood Yes
No

Optimization level
Very Low
Low
Medium
High

Number of rate categories
2
3
4

The probabilistic model describing the gain and loss dynamics

Rate distribution

The evolutionary model determines how variability among positions in evolutionary rates is modeled

- "Equal rate for all characters": assume that a single evolutionary rate characterizes all sites.
- "Gamma": among site rate variation, assuming that the rate is gamma distributed
- "Gamma plus invariant category": gamma distributed with an additional invariant rate category (rate~zero)

gain & loss rates

- "Variable rate among sites": gain and loss probabilities may be different but the gain/loss ratio is identical across all sites.
- "Variable gain/loss ratio (mixture)": gain/loss ratio varies among sites.

gain rate defines 0 => 1 dynamics, while loss rate defines 1 => 0 dynamics.

(+) Correction for un-observable data

Likelihood correction is used when specific data not included in the data, such as position absent from all species.

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).

(+) Estimation of model parameters

The parameters of the evolutionary model are estimated from the data.

Parameters include: gain/loss ratio, shape of the rate distributions, and branch lengths.

Parameters include: gain/loss ratio, shape of the rate distributions, and branch lengths.

Estimate branch lengths using likelihood

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

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.

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