Command line interface¶
Two command_line applications are provided with msprime
: msp and
mspms. The msp program is an experimental interface for
interacting with the library, and is a POSIX compliant command line
interface. The mspms program is a fully-ms compatible
interface. This is useful for those who wish to get started quickly with using
the library, and also as a means of plugging msprime
into existing work
flows. However, there is a substantial overhead involved in translating data
from msprime
’s native history file into legacy formats, and so new code
should use the Python API where possible.
msp¶
The msp
program provides a convenient interface to the msprime API. It is based on subcommands that either generate or consume a
tree sequence file. The simulate
subcommand runs a
simulation storing the results in a file. The other commands are concerned with
converting this file into other formats.
Warning
This tool is very new, and the interface may need to change over time. This should be considered an alpha feature!
msp simulate¶
msp simulate provides a command line interface to the
msprime.simulate()
API function. Using the parameters provided at the
command line, we run a simulation and then save the resulting tree sequence
to the file provided as an argument.
usage: msp simulate [-h] [--length LENGTH]
[--recombination-rate RECOMBINATION_RATE]
[--mutation-rate MUTATION_RATE]
[--effective-population-size EFFECTIVE_POPULATION_SIZE]
[--random-seed RANDOM_SEED] [--compress]
sample_size tree_sequence
Positional Arguments¶
sample_size | The number of genomes in the sample |
tree_sequence | The msprime tree sequence file |
Named Arguments¶
--length, -L | The length of the simulated region in base pairs. |
--recombination-rate, -r | |
The recombination rate per base per generation | |
--mutation-rate, -u | |
The mutation rate per base per generation | |
--effective-population-size, -N | |
The diploid effective population size Ne | |
--random-seed, -s | |
The random seed. If not specified one is chosen randomly | |
--compress, -z | Enable zlib compression |
Note
The way in which recombination and mutation rates are specified is different to ms. In ms these rates are scaled by the length of the simulated region, whereas we use rates per unit distance. The rationale for this change is to simplify running simulations on a variety of sequence lengths, so that we need to change only one parameter and not three simultaneously. See API Documentation for more on this point.
mspms¶
The mspms program is an ms-compatible
command line interface to the msprime
library. This interface should
be useful for legacy applications, where it can be used as a drop-in
replacement for ms. This interface is not recommended for new applications,
particularly if the simulated trees are required as part of the output
as Newick is very inefficient. The Python API is the recommended interface,
providing direct access to the structures used within msprime
.
Supported Features¶
mspms supports a subset of ms’s functionality. Please open an issue on GitHub if there is a feature of ms that you would like to see added. We currently support:
- Basic functionality (sample size, replicates, tree and haplotype output);
- Recombination (via the
-r
option); - Spatial structure with arbitrary migration matrices;
- Support for ms demographic events. (The implementation of the
-es
option is limited, and has restrictions on how it may be combined with other options.)
Gene-conversion is not currently supported, but is planned for a future release.
Argument details¶
This section provides the detailed listing of the arguments to
mspms (also available via mspms --help
). See
the documentation for ms
for details on how these values should be interpreted.
mspms is an ms-compatible interface to the msprime library. It simulates the coalescent with recombination for a variety of demographic models and outputs the results in a text-based format. It supports a subset of the functionality available in ms and aims for full compatibility.
usage: mspms [-h] [--mutation-rate theta] [--trees]
[--recombination rho num_loci] [--structure value [value ...]]
[--migration-matrix-entry dest source rate]
[--migration-matrix entry [entry ...]]
[--migration-rate-change t x]
[--migration-matrix-entry-change time dest source rate]
[--migration-matrix-change entry [entry ...]]
[--growth-rate alpha]
[--population-growth-rate population_id alpha]
[--population-size population_id size]
[--growth-rate-change t alpha]
[--population-growth-rate-change t population_id alpha]
[--size-change t x] [--population-size-change t population_id x]
[--population-split t dest source]
[--admixture t population_id proportion]
[--random-seeds x1 x2 x3] [--precision PRECISION] [-V]
[-f FILENAME]
sample_size num_replicates
Positional Arguments¶
sample_size | The number of genomes in the sample |
num_replicates | Number of independent replicates |
Named Arguments¶
-V, --version | show program’s version number and exit |
-f, --filename | Insert commands from a file at this point in the command line. |
Behaviour¶
--mutation-rate, -t | |
Mutation rate theta=4*N0*mu | |
--trees, -T | Print out trees in Newick format |
--recombination, -r | |
Recombination at rate rho=4*N0*r where r is the rate of recombination between the ends of the region being simulated; num_loci is the number of sites between which recombination can occur |
Structure and migration¶
--structure, -I | |
Sample from populations with the specified deme structure. The arguments are of the form ‘num_populations n1 n2 … [4N0m]’, specifying the number of populations, the sample configuration, and optionally, the migration rate for a symmetric island model | |
--migration-matrix-entry, -m | |
Sets an entry M[dest, source] in the migration matrix to the specified rate. source and dest are (1-indexed) population IDs. Multiple options can be specified. | |
--migration-matrix, -ma | |
Sets the migration matrix to the specified value. The entries are in the order M[1,1], M[1, 2], …, M[2, 1],M[2, 2], …, M[N, N], where N is the number of populations. | |
--migration-rate-change, -eM | |
Set the symmetric island model migration rate to x / (npop - 1) at time t | |
--migration-matrix-entry-change, -em | |
Sets an entry M[dest, source] in the migration matrix to the specified rate at the specified time. source and dest are (1-indexed) population IDs. | |
--migration-matrix-change, -ema | |
Sets the migration matrix to the specified value at time t.The entries are in the order M[1,1], M[1, 2], …, M[2, 1],M[2, 2], …, M[N, N], where N is the number of populations. |
Demography¶
--growth-rate, -G | |
Set the growth rate to alpha for all populations. | |
--population-growth-rate, -g | |
Set the growth rate to alpha for a specific population. | |
--population-size, -n | |
Set the size of a specific population to size*N0. | |
--growth-rate-change, -eG | |
Set the growth rate for all populations to alpha at time t | |
--population-growth-rate-change, -eg | |
Set the growth rate for a specific population to alpha at time t | |
--size-change, -eN | |
Set the population size for all populations to x * N0 at time t | |
--population-size-change, -en | |
Set the population size for a specific population to x * N0 at time t | |
--population-split, -ej | |
Move all lineages in population dest to source at time t. Forwards in time, this corresponds to a population split in which lineages in source split into dest. All migration rates for population source are set to zero. | |
--admixture, -es | |
Split the specified population into a new population, such that the specified proportion of lineages remains in the population population_id. Forwards in time this corresponds to an admixture event. The new population has ID num_populations + 1. Migration rates to and from the new population are set to 0, and growth rate is 0 and the population size for the new population is N0. |
Miscellaneous¶
--random-seeds, -seeds | |
Random seeds (must be three integers) | |
--precision, -p | |
Number of values after decimal place to print |
If you use msprime in your work, please cite the following paper: Jerome Kelleher, Alison M Etheridge and Gilean McVean (2016), “Efficient Coalescent Simulation and Genealogical Analysis for Large Sample Sizes”, PLoS Comput Biol 12(5): e1004842. doi: 10.1371/journal.pcbi.1004842