Contains Script To perform Demographic inference using microsatelittes data: based on :

• coalescent simulation usgin ms
• ABC model selection and parameter estimation.
• RandomForest model selection

R scripts for simulating data and convertion into R were modified from previous developpement by Illera et al. (2014). Original script made by M. Nasvascues can be found here: https://www.researchgate.net/publication/273445598_ABCSylvia

The comparaison of 4 models of population divergence namely

• SI (Strict Isolation) ,
• Isolation with Migration (IM),
• Ancient Migration (AM) and
• Secondary Contact (SC)

Computation of other summary statistics:

• Allelic Richness (Ar)

• Expected Heterozygosity with the previous stats being include in function to reduce the complexity of the scripts

• inclusion of R script for:

1. model comparaison
2. parameter estimation
3. computation of robutness
4. goodness of fit
5. random forest (also I should update this script one day)

• inclusion in bash scripts for parallelisation and runs easily using any microstallite data. microstallite data that have to be stored in the 01-data folder together with a file containing the repeat_motif for each microsatellites markers.

I also extended the pipeline to perform :
3 pops colaescent derivation of our two population models for:

• Isolation w. Migration (IM)
• Strict Isolation (SI)

4 pops colaescent derivation for:

• Isolation w. Migration (IM) (with several possible configurations for directions and symetries of introgression)
• Strict Isolation (SI)

• Illera, J. C., A. M. Palmero, P. Laiolo, F. Rodríguez, Á. C. Moreno, and M. Navascués. 2014. Genetic, Morphological and Acoustic Evidence Reveals Lack of Diversification in the Colonization Process in an Island Bird. Evolution Volume 68, Issue 8, pages 2259-2274.DOI: 10.1111/evo.12429 available here: http://onlinelibrary.wiley.com/doi/10.1111/evo.12429/full

• Rougemont, Q., C. Roux, S. Neuenschwander, J. Goudet, S. Launey, G. Evanno. 2016. Reconstructing the demographic history of divergence between European river and brook lampreys using approximate Bayesian computations. PeerJ 4:e1910 https://doi.org/10.7717/peerj.1910

mscoalescent simulator from Hudson 2002 available here

• pegas package more info here
• abc package more info here
• RandomForest SRC package more info here

You'll need to provide a separate input file for each population. Each file contains one markers in raw, with one row for each gene copies (2 rows per individuals) and loci in columns
A file containing the length of the repeat_motif for each microsatellite markers is also needed.
One row per markers.
These input file are stored in the 01-data folder

then go to 00-scripts/models/ and edit the script model.\*.sh to provide the name of the input file
the script model.1 sh is for SI
model.2.sh for IM
model.3.sh for SC
model.4.sh for AM

the corresponding Rscripts are found in the 00-scripts/rscript/ with name Simul_*_parallel.R

• go in 00-scripts/rscript/ and edit the Simul\*.R scripts to set prior according to what you think will fit the data.
  I recommand to use large and uninformative priors first.
  Wou'll have to choose a fixed thetaRef, according to thetaRef=4*Nref*µ meaining that you need to have an idea of the mutation rate (ideally) then you have to choose priors for :
• effective population size (N1, N2, Nancestral, etc)
• migration rates (M1, M2, etc)
• Split time (T=Tsplit/4Nref)

beware that in ms from Hudson, all is scaled by 4Nref so that you need to thinks mostly in terms of ratios for the effective population size and Split times. I strongly advise reading of msdoc.pdf included in the 00-scripts/msdir

once the prior are set coalescents simulations can be run and needs ideally to be run in parallel if you want to save time.
To do so I provided a series of 4 scripts (one for each model) in 00-scripts/models/model_job_array\*.sh.
These scripts are rather 'cluster-specific' and depending on your clustering machine, you'll have to change that but this provides a clue.