visits since March 2011

fast sequential Markov coalescent simulation of genomic data under complex evolutionary models

While preserving all the simulation flexibility of simcoal2, fastsimcoal is now implemented under a faster continous-time sequential Markovian coalescent approximation, allowing it to efficiently generate genetic diversity for different types of markers along large genomic regions, for both present or ancient samples. It includes a parameter sampler allowing its integration into Bayesian or likelihood parameter estimation procedure.

fastsimcoal can handle very complex evolutionary scenarios including an arbitrary migration matrix between samples, historical events allowing for population resize, population fusion and fission, admixture events, changes in migration matrix, or changes in population growth rates. The time of sampling can be specified independently for each sample, allowing for serial sampling in the same or in different populations.

Different markers, such as DNA sequences, SNPs, STRs (microsatellites) or multi-locus allelic data can be generated under a variety of mutation models (e.g. finite- and infinite-site models for DNA sequences, stepwise or generalized stepwise mutation model for STRs data, infinite-allele model for standard multi-allelic data).

fastsimcoal can simulate data in genomic regions with arbitrary recombination rates, thus allowing for recombination hotspots of different intensities at any position. fastsimcoal implements a new approximation to the ancestral recombination graph in the form of sequential Markov coalescent allowing it to very quickly generate genetic diversity for >100 Mb genomic segments.

fastsimcoal2 now allows one to estimate demographic parameters from the (joint) site frequency spectrum (SFS) using simulations to compute the expected SFS and a robust method for the maximization of the composite likelihood.

new version of fastsimcoal2 : fsc27 (.09) (October 2022)

fsc27 introduces several improvements for the simulation of large recombining segments, highly subsidivided population, a new output file format, new syntax of .est files facilitating the description of complex scenarios. and corrects several bugs.
fsc2705 corrects a bug occurring in presence of exponential growth an migration, which has been introduced in released version 2.7. Previous version 2.6 did not have this bug. People who have been simulating scenarios with both migration and exponential growth are encouraged to redo their computations with the new version, even if the program did not crash.
fsc2709 corrects several bugs (detected by Kyle Lewald):

Lhood estimated from par files was badly estimated in case of population growth
Problems when simulating arrays of demes using a single migration matrix after some demes were explicitly killed with historical events

A new version of fsc27 for Linux is now available (fsc27093) for download on October 13th 2022. It corrects a bug, preventing fsc to get stuck randomly at the beginning of the computations. Problem was due to gcc compiler, and not to code. Windows and Mac version were unaffected. Results of runs that did not get stuck are ok.

downloads (fsc27)

Windows 64 bits

Linux 64 bits

MacOSX

manual

what's new in fastsimcoal2709 (compared to ver 2.6)

New features

New syntax in the .est files. It is now possible to include previously defined simple parameters as search range delimiters. The keyword paramInRange needs to be specified at the end of lines containing such parameters.
New keyword in .par or .tpl file: absoluteResize. It allows a given sink population to take a new absolute size, independently of its previous size. It eliminates the need to compute this resize as a complex parameter in the .est file
The [RULES] section has been suppressed from input files. It is simply not read anymore. These rules have become obsolete given the new syntax described in point 1.
SNP data types are not considered anymore, as they led to biased simulations. Use short segments of DNA and the -sX option to generate X SNPs instead
Simulations of large and sparsely occupied structured populations has been optimized and can be up to10 times faster than the previous version. There is very little gain for simulations with a small number of migration-connected demes, though.
Simulations of large recombining chromosomes has been optimized, when using large values of the -k options
Generation of genotype table (.gen file) as an alternative output to Arlequin (-G option). The additional -g option allows one to generate diploid genotypes (coded as 0, 1 or 2) instead of haploid genotypes (coded as 0 or 1)
Possibility to “kill” demes, such as to make them inaccessible to migration. Setting a sink deme size to zero (using a sink resize of zero in a historical event) will now prevent further migration to this deme. This is useful as one can keep the same migration matrix after the disappearance of some demes (e.g. due to population fusion backward in time).
Comments are now possible at the end of any line of .est files.
Other changes and bug corrections:
- When a deme size goes to zero (e.g. due to negative growth), a warning is only produced if the deme is occupied (thanks to David Marques for requesting this change).
- Bug corrected when computing likelihood with ghost populations and a single sampled deme.
- Corrected bug (found by David Marques) with options --noSingleton and --foldedSFS in the presence of ghost populations (the max est lhood was larger than the max obs lhood).
- Corrected bug occurring when computing the position of the next recombination position in case of very small recombination rates (thanks to Silvert Martin)
- Corrected important bug (thanks to David Marques) in case of the introduction of population growth at a given point in a population of initial constant size. The population size was adjusted as if there had been growth since generation zero.
- Corrected bug (thanks to Yu Sugihara) when generating diversity based on random parameters and using -Ex option when x >1.
- Corrected bug (thank to Jason Weir) when simulations scenarios with both migration and exponential growth. It led to program crashes and incorrect migration patterns.

See this page for a complete list of changes since first fastsimcoal release

benchmarks

Comparisons with other coalescent simulations programs such as ms, simcoal2 or MaCS can be found here

getting started

A quick overview of how to get started with fastsimcoal can be found here (but it is better to read the manual first)

additional scripts for the preparation of input files and the analysis of results

A series of R and bash scripts decribed and made available here have been developed by several people from our group to facilitate the analysis of the results of fsc27 as well as for preparing input file

running fsc27 on a mac

I have realized (thanks to Melissa Wilson Sayre) that the plain version of fsc26 and following will not run on mac osX unless you have installed a recent version of gcc.

This is because fsc26 and above is multithreaded and it uses intel's libraries based on openMP, which are not distributed anymore with recent versions of mac OSX.

So to be able to run fsc27 on your mac, you need to first install a recent version of gcc.

To do so, follow these steps:

Download gcc-10.2-bin.tar.gz
Open a terminal
cd to the download folder
Extract the tar archive with the command
gunzip gcc-10.2-bin.tar.gz
Install gcc ver 5.1 in /usr/local with the command
sudo tar -xvf gcc-10.2-bin.tar -C /.

problems running fsc27 on old versions of linux (kernel too old)

It seems that fsc27 is not able to run on old linux version with an old kernel, potentially due to the need of openmp libraries that need to be dynamically linked to the program.

bug reports

Send an email to Laurent Excoffier or use the Google group list

discussion list

A Google group on fastsimcoal (https://groups.google.com/forum/#!forum/fastsimcoal) has been created to promote discussion or allow queries on any aspect of fastsimcoal. Please use it!

citation

fastsimcoal2:
Excoffier, L., Marchi,N., Marques, D. A., Matthey-Doret, R., Gouy, A., Sousa, V. C. (2021) fastsimcoal2: demographic inference under complex evolutionary scenarios. Bioinformatics. 37:4882-4885.

Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V.C., and M. Foll (2013) Robust demographic inference from genomic and SNP data. PLOS Genetics, 9(10):e1003905.

fatsimcoal:
Excoffier, L. and Foll, M (2011) fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios Bioinformatics 27: 1332-1334.

Last updated by L. Excoffier on 14..04.2022