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, SNP, STR (microsatellite) 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 : fsc25 ver 2.5.2.21 (November 2015 release)

fsc25 ver 2.5.2.21 mainly corrects a bug introduced in ver 2.5.2.8 preventing the implementation of exponential growth in the first simulated tree

downloads (fsc25)

Windows 64 bits

Linux 64 bits

MacOSX El Capitan (10.6-)

manual

what's new in fastsimcoal25

In addition to overall polishing and bug corrections, the main innovation of ver 2.5 of fastsimcoal2 is the introduction of multithreading (with the -c option). This option offers the possibility of doing parameter optimization on desktop machines, as most modern machines have multiple cores. Note that there is no strict linear increase in the performance of multithreaded runs and no. of threads (cores), so that it is not recommended to use more than one thread on a linux cluster.

Bug corrections and modifications in ver 2.5.2.21 (November 2015)

1. Bug corrections:

Non implementation of exponential growth at time zero for the first simulated tree. Initial population size therefore does not change for that tree. Note that specifications of exponential growth rates in historical events are correctly implemented even in the first tree. Exponential growth is then correctly implemented in the next simulated trees (thanks to Anand Bhaskar)
Crash in case of very large samples sizes (e.g. 60,000) (thanks to Anand Bhaskar)
Incorrect computation of the max lhood when non integers sre used in the observed sfs (thanks to Andi Knautt)
Reported expected SFS was that of the last iteration and not that associated to the max lhood parameter estimates
In case of crash due to bad tpl file, parameters reported in file called <generic name>_bad.par were not those leading to the crash

Speed optimization. Up to 30% speed gain.
Output of time to MRCA in file <generic name>_mrca.txt with new compiler directive --recordMRCA. Beware that this option really slows down computations. Note that we also output the he deme in which MRCA occurred.

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)

visualizing scenarios modeld in par files (new)

With Vitor Sousa, we have written an R script called ParFileInterpreter-v6.3.r that reads par files and plots the modeled scenario, which can be useful to check that the your modeled scenario corresponds to what you had in mind. It can also be useful to visualize the scenario obtained after some parameter estimation (use the *_maxL.par file). More information on the use of the this program can be found here.

running fsc25 on a mac

I have realized (thanks to MelissaWilson Sayre) that the plain version of fsc25 will not run on mac osX unless you have installed a recent version of gcc.

This is because fsc25 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 fsc25 on your mac, you need to first install a recent version of gcc.

To do so, follow these steps:

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

problems running fsc25 on linux (kernel too old)

It seems that fsc25 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 and fsc22:
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 16.11.2015