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Table of contents

  1. Snakemake
    1. info
    2. execution
    3. dependencies
  2. Scripts in python
    1. scripts
    2. python dependencies
  3. Scripts in R
    1. scripts
    2. R dependencies
    3. webinterface
  4. Codes in C
    1. msnsam (by Jeffrey Ross-Ibarra)
    2. RNAseqFGT (by Laurent Duret)
  5. Config files
    1. cluster.json
    2. config.yaml
  6. Workflow
    1. Two populations

1 - snakemake

info

The entire workflow is based on snakemake.
https://snakemake.readthedocs.io/en/stable/

execution

Please adapt the pathway to your system.

snakemake --snakefile /shared/mfs/data/home/croux/softwares/DILS/bin/Snakefile_2pop -p -j 50 --configfile /shared/home/croux/scratch/myProject/config.yaml --cluster-config /shared/home/croux/scratch/myProject/cluster.json --cluster "sbatch --nodes={cluster.node} --ntasks={cluster.n} --cpus-per-task={cluster.cpusPerTask} --time={cluster.time}"  

dependencies

Needs:
1 Snakefile
1 config.yaml file
1 cluster.json file

2 - python

Executables are all found in the bin/ subdirectory. The pathway of subdirectory has to be indicated in the Snakefiles. This is the only required file modification

scripts

bin/fasta2ABC_1pop.py
bin/fasta2ABC_2pops.py
bin/mscalc_1pop_observedDataset_SFS.py
bin/mscalc_1pop_SFS.py
bin/mscalc_2pop_observedDataset.py
bin/mscalc_2pop_observedDataset_SFS.py
bin/mscalc_2pop.py
bin/mscalc_2pop_SFS.py
bin/priorgen_1pop.py
bin/priorgen_2pop_popGrowth.py
bin/priorgen_2pop.py
bin/priorgen_gof_1pop.py
bin/priorgen_gof_2pop_popGrowth.py
bin/priorgen_gof_2pop.py
bin/priorgen_gof_2pop_test_monolocus.py
bin/submit_simulations_1pop.py
bin/submit_simulations_2pop_popGrowth.py
bin/submit_simulations_2pop.py
bin/submit_simulations_2pop_test_monolocus.py
bin/submit_simulations_gof_1pop.py
bin/submit_simulations_gof_2pop_popGrowth.py
bin/submit_simulations_gof_2pop.py

python dependencies

some scripts uses pypy as python interpreter
from math import ceil
from numpy import log
from numpy import median
from numpy.random import beta
from numpy.random import binomial
from numpy.random import randint
from numpy.random import uniform
from random import choice
from random import randint
from random import sample
from random import shuffle
import os
import random
import sys
import time

3 - R

scripts

uses Rscript from /usr/bin or elsewhere
bin/collaborative_plot.R
bin/estimates_1pop_best.R
bin/estimates_2pop_best.R
bin/estimates_2pop.R
bin/get_parameters_1pop_CV.R
bin/get_parameters_1pop.R
bin/get_parameters_2pop.R
bin/gof_1pop.R
bin/gof_2pop.R
bin/model_comp_1pop_allModels.R
bin/model_comp_2pop_allModels.R
bin/model_comp_2pop_locus.R
bin/model_comp_2pop.R
bin/PCA.R

R dependencies

library(abcrf)
library(data.table)
library(FactoMineR)
library(ggplot2)
library(ggpubr)
library(nnet)
library(plotly)
library(tidyverse)
library(viridis)

webinterface

Install R libraries for the user interface:

list_libraries = c('shiny', 'shinythemes', 'shinydashboard', 'shinydashboardPlus', 'DT', 'shinyWidgets', 'dashboardthemes', 'devtools', 'shinyhelper', 'plotly', 'viridis', 'tidyr', 'RColorBrewer', 'yaml', 'ggpubr', 'FactoMineR', 'shinycssloaders')  

for(lib_tmp in list_libraries){  
	install.packages(lib_tmp, dep=T)  
}  

library(devtools)  
install_github("nik01010/dashboardthemes")  

library(shiny)
library(shinythemes)
library(shinydashboard)
library(shinydashboardPlus)
library(DT)
library(shinyWidgets)
library(dashboardthemes) # library(devtools); install_github("nik01010/dashboardthemes")
library(shinyhelper)
library(plotly)
library(viridis)
library(tidyr)
library(RColorBrewer)
library(yaml)
library(ggpubr)
library(FactoMineR)
library(shinycssloaders)

Can be launched as follows from the webinterface subdirectory:

Rscript app.R host=127.0.0.9 port=8162  

4 - C

msnsam

info

C code, compiled by executing the command ./clms (calling gcc) in the msnsam/ directory

RNAseqFGT

info

C code compiled by: gcc -Wall -o RNAseqFGT RNAseqFGT.c RNAseqFGT_seq_reading.c RNAseqFGT_analysis.c -I RNAseqFGT.h

5 - config files

cluster.json

This file contains informations for Slurm about the submited jobs, in particular, the required resources (CPU, memory, duration).

{
    "__default__" :
    {
        "node" : 1,
        "ntasks" : 1,
        "n" : 1,
	"cpusPerTask" : 1,
	"memPerCpu" : 3000,
	"time" : "02:00:00"
    },
    "fasta2ABC_2pops" :
    {
	"cpusPerTask" : 8,
	"time" : "02:00:00",
	"memPerCpu" : 2000
    },
    "RNAseqFGT" :
    {
	"cpusPerTask" : 1,
	"time" : "01:00:00",
	"memPerCpu" : 10000
    },
    "modelComparison" :
    {
	"cpusPerTask" : 8,
	"time" : "01:30:00",
	"memPerCpu" : 4000
    },
    "estimation" :
    {
	"cpusPerTask" : 8,
	"time" : "01:30:00",
	"memPerCpu" : 3000
    },
    "estimation_best_model" :
    {
	"cpusPerTask" : 8,
	"time" : "01:30:00",
	"memPerCpu" : 3000
    },
    "estimation_best_model_2" :
    {
	"cpusPerTask" : 8,
	"time" : "01:30:00",
	"memPerCpu" : 3000
    },
    "estimation_best_model_3" :
    {
	"cpusPerTask" : 8,
	"time" : "01:30:00",
	"memPerCpu" : 3000
    },
    "estimation_best_model_4" :
    {
	"cpusPerTask" : 8,
	"time" : "01:30:00",
	"memPerCpu" : 3000
    },
    "estimation_best_model_5" :
    {
	"cpusPerTask" : 8,
	"time" : "01:30:00",
	"memPerCpu" : 3000
    },
    "locus_modelComp" :
    {
	"cpusPerTask" : 8,
	"time" : "01:30:00",
	"memPerCpu" : 3000
    },
    "PCA_SS" :
    {
	"cpusPerTask" : 1,
	"time" : "01:00:00",
	"memPerCpu" : 5000
    }
}

config.yaml

Configuration file used by Snakemake to adapt the workflow to a particular analysis. Contains information such as species names, genomic region (coding, noncoding), prior boundaries, etc...

mail_address: user@gmail.com   
infile: /shared/home/croux/scratch/moules/sequences.fas  
region: coding  
nspecies: 2  
nameA: Spring  
nameB: Sydney  
useSFS: 0
nameOutgroup: NA  
config_yaml: /shared/home/croux/scratch/moules/config.yaml  
timeStamp: SpringSydney  
population_growth: constant  
modeBarrier: bimodal  
max_N_tolerated: 0.2  
Lmin: 100  
nMin: 6  
mu: 0.00000002763  
rho_over_theta: 0.5  
N_min: 1000  
N_max: 500000  
Tsplit_min: 10000  
Tsplit_max: 1750000  
M_min: 1  
M_max: 40  

6 - workflow

two populations

DAG (directed acyclic graph)

7 - example

grey zone

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