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README (2).md

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+# SMCPP pipeline
+A python pipeline for implementing smcpp to decipher demographic history of populations of interest
+
+All the functions assume that the user has generated population population-specific chromosome file and stored it in a directory popname/ present in the current working directory. all the chromosome files should be bgzipped and tabix indexed and should be in format chr{i}.vcf.gz.
+
+If the user has a single genotype file, then user can make use of bcftools to generate population-specific bgzipped and tabix index VCFs.
+
+The original package can be found here
+https://github.com/popgenmethods/smcpp
+
+The original article can be found here
+
+Terhorst, J., Kamm, J. & Song, Y. Robust and scalable inference of population history from hundreds of unphased whole genomes. Nat Genet 49, 303–309 (2017). https://doi.org/10.1038/ng.3748
+
+The package can be installed through Pypi (https://pypi.org/project/smcpp-pipeline/)
+
+
+A tutorial has been provided in a blog (https://medium.com/@devashishtripathi697/population-history-inference-using-whole-genome-sequence-data-9cf99821cd1f) using the simulated data hosted at figshare (https://figshare.com/articles/dataset/msprime_single_population_simulated_dataset/25234849)
+
+```python
+pip install smcpp-pipeline
+
+
+from smcpp_pipeline.smcpp_pipeline import vcf2smc,estimate,plot
+
+1) Function to convert vcf file to smc format
+  def vcf2smc(popfile,chromosome_info,gaps_bed=None,num_workers=None):
+2) Function to estimate the demographic history of a single population
+   def estimate(popfile,mu = 1.25e-8,spline = "piecewise",start_time = 100, end_time=100000,knots = 8):
+3) Function to generate plots from the model.json file obtained using estimate function
+   def plot(popfile,time_start=100,time_end=15000):
+```
+popfile should be a tab separated file containing two columns
+
+```
+sample.id  population
+i1          pop1
+i2          pop1
+i3          pop2  
+