DISCLAIMER: MSMC2 is under development. Use at your own risk!
This program implements MSMC2, a method to infer population size history and population separation history from whole genome sequencing data. For a general guide, please refer to version 1: https://github.com/stschiff/msmc
Binaries are attached to the releases (under the "Releases" tab within github).
You can then install the program by typing
make in the directory.
The resulting executable will be in the
You have to manually adjust the Makefile if you have the GSL in a non-trivial location on your platform.
-i, --maxIterations=<size_t> : number of EM-iterations [default=20] -o, --outFilePrefix=<string> : file prefix to use for all output files -m, --theta=<double> : fix the scaled mutation rate, by default determined by the number of segregating sites. This option determines the exact placement of the time segment boundaries. For a cross-population analysis, you need three independent msmc runs, and you should use this option to ensure the same time boundaries in each run, see documentation. -r, --rhoOverMu=<double> : ratio of recombination over mutation rate (default: 0.25) -t, --nrThreads=<size_t> : nr of threads to use (defaults to nr of CPUs) -p, --timeSegmentPattern=<string> : pattern of fixed time segments [default=1*2+25*1+1*2+1*3] -R, --fixedRecombination : keep recombination rate fixed (rarely needed in MSMC2) -I, --indices: indices (comma-separated) of alleles in the data file to run over -P, --subpopLabels: comma-separated list of 0s and 1s to indicate subpopulations. If given, estimate coalescence rates only across populations. -s, --skipAmbiguous: skip sites with ambiguous phasing. Recommended for cross population analysis"
minimum command line:
build/release/msmc2 -o <out_prefix> <input_chr1> <input_chr2> ...
For population size estimation, you can basically use
msmc2 just as you used
msmc, using the same input files. Note there are some important changes to
msmc: First, it is not anymore necessary to fix the recombination size for more than two haplotypes. So I would run without the
-R switch (which was recommended in
msmc). Second, the time patterning has changed. We now use the same patterning as in (Li and Durbin, 2011), however with more and more resolution in recent times depending on the number of haplotypes you use.
For cross-population analysis, things are quite different now. While in
msmc, you could estimate all three coalescence rate functions (two within and one across populations) in one go, now you have to make three runs, corresponding to the three estimates. For example, say you have four diploid individuals, two from each population, you should generate a combined input file with eight haplotypes (see msmc and msmc-tools repositories), and then start three runs:
build/release/msmc2 -I 0,1,2,3 -o within1_msmc <input_chr1> <input_chr2> ...
build/release/msmc2 -I 4,5,6,7 -o within2_msmc <input_chr1> <input_chr2> ...
build/release/msmc2 -P 0,0,0,0,1,1,1,1 -o across_msmc <input_chr1> <input_chr2> ...
The first two runs just estimate coalescence rates within each population. The third run selects only the 16 cross-population pairs (using the
-P switch) to estimate the cross-coalescence rate.
Finally, you can generate a combined msmc output file using the combineCrossCoal.py script from the msmc-tools repository:
./combineCrossCoal.py across_msmc.final.txt within1_msmc.final.txt within2_msmc.final.txt > combined12_msmc.final.txt
This script handles the fact that the three coalescence rate estimates result in different time patternings, using interpolation of the within-coalescence rates.
While this approach seems to be more cumbersome at first, you get a speed-up because you can separate the run into three parallel runs, and you can reuse the population size estimates within each population.
Note also that with MSMC2 there is no numerical problem anymore for large number of haplotypes. Of course, the complexity still increases quite dramatically, and I don't think it's feasible to go beyond 16 haplotypes (even that will be a huge run), but the model itself is perfectly accurate up to arbitrary numbers of haplotypes. Of course, there may also be constraints by the phasing quality. With limited phasing you will get problems in very recent times, so a large number of haplotypes may not be helpful without good phasing.