Warning!This tool is under active development. Its capabilities and usage may change without warning. |

This workflow implements a method for extracting effective beneficial mutation rates (*μ*) and selection coefficients (*s*) from marker divergence experiments [1]. This is a way of parameterizing the evolvability of a bacterial strain.

- Marker Divergence Experiments

The current version of the md package is available from our public Mercurial repository:

Download Current Source Archive | Browse Mercurial Repository |

The Perl scripts require the module Math::Random::MT::Auto and its prerequisites for random number generation. They can be installed from CPAN. The Math::Random::MT::Auto module has a code component that must be compiled. If you have root access on a system you can probably install these from the command line as follows:

```
>sudo perl -MCPAN -e shell
```

>Password: ********

>install Math::Random::MT::Auto

*answer yes to any prompts about installing prerequisites*

MATLAB is required for calculating establishment probabilities.

R is required for fitting marker divergence curves. It should be present in your $PATH, so that the Perl scripts can invoke it. You must also have the R package nortest installed for the Lilliefors test employed by the curve fitting script. This can be done from the command line within R by issuing the following command:

```
>install.packages("nortest")
```

You may want to add the location of the perl scripts to your $PATH. You may need to change the first line of each script to the correct path to your Perl executable if it is not located at:

```
#!/usr/bin/perl
```

Help for each individual Perl script can be obtained as follows

```
>perldoc <script>
```

The basic command is:

```
>marker_divergence_fit.pl -i input.tab -o output.fit -p output.fit.pdf
```

`output.fit`

is a tab-delimited file containing information about each curve fit. `output.fit.pdf`

shows plots of each fit, so that you can judge whether they accurately reflect the data.

The input file is **tab-delimited**. The header row begins with "transfer", and the other columns are labels indicating the name of an experimental time series of marker ratio measurements. Each following row begins with the number of the transfer followed by the marker ratio measurements for that series at that time point. Marker ratios may be given in a variety of formats. Pass the `-m`

option followed by `ratio`

, `fraction`

, `log_ratio`

, or `log10_ratio`

to this script depending on the format of you data values. The default mode is `ratio`

.

**Portion of an example marker ratio input file:**

```
transfer exp-1 exp-2 exp-3
```

0 0.5087 0.5068 0.4990

3 0.5000 0.4844 0.5174

6 0.4853 0.5393 0.5115

9 0.4802 0.4862 0.4522

12 0.4884 0.4431 0.5170

15 0.5277 0.5196 0.5266

18 0.4983 0.4638 0.4607

21 0.5221 0.5361 0.5000

The output file is tab-delimited, with columns containing data as labeled.

If some of your experimental curves do not start at a 1:1 ratio of the neutral marker states, you will also want to pass the `-b`

option followed by the number of initial points (not transfers) that you would like to fit as a baseline. The script corrects for the initial marker imbalance by fitting τ and α to a modified equation that accounts for the fact that for a population diverging toward marker state *A*, where *A* was initially present in less than 50% of the population, the marker ratio will be shifted sooner than in a population where it was initially present in 50% of the population.

**Example:**

```
>marker_divergence_fit.pl -m log_ratio -i input.tab -o output.fit -p output.fit.pdf
```

The population genetics model assumes that each new beneficial mutation that is generated has a certain probability of establishing (escaping loss due to dilution) during the serial transfer regime of the experiment. A table of these probabilities must be calculated with the MATLAB script.

First, add the directory containing the two ".m" files that come with the distribution to the MATLAB path.

**In MATLAB:**

```
>>establishment_probability_table(6.64, 5E6, 0.001, 1, 'pr_establishment_T_6.64_No_5E6.tab')
```

The arguments are the number of generations per transfer, the initial population size immediately after each transfer, the precision of the file to be generated, the maximum selection coefficient to consider, and the output filename. Output is a tab-delimited list of selection coefficient and probability of establishment when a new mutant has this advantage relative to the population average [2].

It is important to allow a maximum selection coefficient value several fold **greater** than the expected effective selection coefficient (*s*) because multiple mutations may occur that give a large benefit relative to the population average. Reasonable values are typically 0.0001 to 0.001 for the precision and 1 to 5 for the maximum.

A file (`pr_establishment_T_6.64_N_5E6_LT.tab`

) is provided with the distribution that can be used for experiments conducted under the conditions of the long-term *E. coli* evolution experiment.

The next step is to simulate marker divergence curves generated by a simplified population genetics model where there is only one category of beneficial mutation with selection coefficient *s*. These beneficial mutations occur with a rate *μ*. Selection coefficients are defined such that w_{new}=w_{initial} (1+s). This model takes into account the population bottlenecks that occur during a serial transfer evolution experiment.

For each combination of *s* and *μ*, a distribution of the effective parameters α and &tau is determined from the idealized data. Comparing the effective parameters extracted from the experimental data to all of these distributions allows the maximum likelihood values of *s* and *μ* that best explain the experimental data to be determined.

```
>marker_divergence_pop_gen_simulation.pl -T 6.64 -N 5E6 -u 1E-8 -s 0.08 -p pr_establishment_T_6.64_No_5E6.tab -k 22 -i 3 -r 100 -o pop_gen_s_0.08_u_1E-8.tab
```

The generations per transfer (`-T`

), initial population size at the beginning of each growth cycle (`-N`

), per generation mutations rate (`-u`

), per generation selection coefficient (`-s`

), file of establishment probabilities produced by MATLAB (`-p`

), number of generations during outgrowth (before printing any data) (`-k`

), print out marker ratio each time this many transfers pass (`-i`

), number of simulation replicates to perform (`-r`

).

This step is the same as that used to fit the experimental data.

```
>marker_divergence_fit.pl -m log_ratio -i pop_gen_s_0.08_u_1E-8.tab -o pop_gen_s_0.08_u_1E-8.fit
```

Generally, many combinations of *μ* and *s* must be calculated to determine the maximum likelihood effective parameters. This script combines the two previous steps to serially create marker ratio and fit files over a range of these parameters.

```
>marker_divergence_background_model.pl -T 6.64 -N 5E6 -u -8:-6:0.5 -s 0.06:0.2:0.02 -p pr_establishment_T_6.64_No_5E6.tab -i 1 -r 100 -k 22
```

Parameters are the same as in `marker_divergence_pop_gen_simulation.pl`

, except `-u`

and `-s`

are supplied as `start:end:step_size`

combinations, and `-u`

is in log10 units, i.e. passing a value of -8 gives a mutation rate of 10^{-8}.

This procedure can be farmed out to a computer cluster. Consolidate all of the output files into a single directory before proceeding to the next step. (Its okay if they are scattered across subdirectories, as long as they are the only files ending in `.fit`

) .

Finally, we determine what values of *μ* and *s* produce α and τ distributions in the simulated data that are statistically indistinguishable from those fit from the experimental data.

```
>marker_divergence_significance.pl -i experimental.fit -d path/to/simulation/fits -o experimental.sig -p experimental.sig.pdf
```

The output file `experimental.sig`

has starred lines where the experimental data and simulations agree. Since α and τ are not independent, this represents a *greater than* 95% confidence contour. The output PDF file should have a black square, representing the best parameter combination, and a blue region, indicating the >95% confidence contour. (If the plot is *all blue*, then none of your simulated data agreed with the experimental data.)

Note that you can now pass the `-n`

flag, and the program will use a 2-dimensional version of the KS-test to determine the 95% confidence contour.

- Hegreness, M., Shoresh, N., Hartl, D., and Kishony, R. (2006) An equivalence principle for the incorporation of favorable mutations in asexual populations.
*Science***311**, 1615-1617. - Wahl, L.M., and Gerrish, P.J. (2001) The probability that beneficial mutations are lost in populations with periodic bottlenecks.
*Evolution***55**, 2606-2610.

Barrick Lab > ToolList > ToolsMarkerDivergence

Contributors to this topic JeffreyBarrick

Topic revision: r10 - 2010-09-28 - 14:00:25 - Main.JeffreyBarrick