clonephytester_snakemake.py

import sys
import os
import shutil
import Functions
import glob
import pandas as pd

####Parse input file####
#SeqLs: list lines in "Data" file given in your input file. See SequenceList.txt for the format of "Data."
#Rpath: path to Rscript
#MLTEDpath: path to ./main (MLTED software)
#Summary: output errors in the input file
#InputParse: 'y' or 'n', in which 'y' indictes that there is errors in the input file and the clonephytester program terminate here.
Input=sys.argv[1] #*.input
SeqLs, Rpath, MLTEDpath, Summary, InputParse = Functions.parse_input(Input)

In0 = open(sys.argv[2]).readline().strip()
outf = open(sys.argv[3],'w')

####File of true clone sequences####
#Sim2TrueFile: a dictionary to assign a file of true clone sequences for each dataset.
#Sim2TrueFile: a key is "Sim" in your "Data" file. See SequenceList.txt for the format of "Data."
#Sim2TrueFile: "ID" is "Data" in you "Data" file.
Sim2TrueFile={'G7':'G7/True/G7-ID.meg','G12':'G12/True/G12-ID.meg','P10':'P10/True/ID_True.meg','MA':'MA/True/ID_True_NoRedun.meg','G7cna':'G7/True/G7-ID.meg','MA50':'MA/True/ID_True_NoRedun.meg','TGlinear':'TGlinear/True/IDCells_True.meg','TGstep':'TGstep/True/IDCells_True.meg','TGconst':'TGconst/True/IDCells_True.meg'}


#Scores are computed for each given inference in the "Data" file.

####Parse a file of inferred clone sequences####
#Sim: "Sim" in your "Data" file. See SequenceList.txt for the format of "Data."
#ID: "Data" in your "Data" file.
#TruMeg: A file (.meg format) with true clone sequences for a dataset.
#InfMeg: A file (.meg format) with inferred clone sequences for a dataset.
#Summary: if there are errors in files of true and inferred clone sequences, the errors are shown.
#InfParse: y' or 'n', in which 'y' indictes that there is errors in the input files and scores are not computed for this dataset.
logf = open("log.log",'w')
Sim,ID,TruMeg,InfMeg,Summary,InfParse = Functions.parse_inferred_file(In0,Summary,Sim2TrueFile)
intag = In0.strip().split('\t')[2][:-4]
nametag = os.path.basename(intag)
if InfParse!='y': print (Summary)
if InfParse=='y':
    ####Scores for this dataset are stored in ResAll####
    ResAll={}						  
    ####Copy mega files####
    #InfMegTMP: Copied file of inferred clone sequences. Redundant sequences are removed, if any.
    #TruMegTMP: Copied file of true clone sequences.
    InfMegTMP=InfMeg.split('/')[-1]
    TruMegTMP=TruMeg.split('/')[-1]
    print ("INFMEG",InfMegTMP)
    print ("TRUMEG",TruMegTMP)
    shutil.copy2(InfMeg, InfMegTMP)
    shutil.copy2(TruMeg, TruMegTMP)
    Functions.RmRedunSeq(InfMegTMP)
    InfMegTMP=InfMegTMP[:-4]+'_NoRedun.meg'
                      
    ####Annotate inferred clones####
    #inferred clone sequences are paired with the most similar true clone sequences.
    #This clone annotation is output in the same directory of the mega file of inferred clone sequences.
    #PairList: list the line of clone annotation file
    #Inf2TruClo: dictionary of inferred clones matched with true clones
    #TrueCloneLs: list of true clone
    #Tru2Inf2GE: the number of SNV assignment errors (GE) for each inferred clone
    #TrueClone_Ls: list of true clones.
    PairList,Inf2TruClo,TrueCloneLs,Tru2Inf2GE,TrueClone_Ls=Functions.CloneAnno(TruMegTMP,InfMegTMP)
    shutil.copy2(InfMegTMP[:-4]+'_TrueCloneAnnotation.txt',InfMeg[:-4]+'_TrueCloneAnnotation.txt')
                      
    ####infer true and inferred clone phylogeny####
    #use MEGACC to infer bifurcating phylogeny.
    #use phangorn pachage in R to infer multifurcating phylogeny from bifurcating phylogeny.
    #TrueMultiTree: nwk format true clone phylogeny.
    #InfMultiTree: nwk format inferred clne phylogeny. The number of clones was made to be the same as that in its true clone phylogeny.
    TrueMultiTree, InfMultiTree=Functions.Build_true_and_inferred_phylogeny(TrueClone_Ls,PairList,TruMegTMP,InfMegTMP,Rpath,intag)
                      
    ####Compute RF####
    #RF: RF distance calcuated by using PhyloNet_3.6.1.jar
    if TrueMultiTree!='NA' and InfMultiTree!='NA':
        RF=Functions.Compute_RF(TrueMultiTree, InfMultiTree, nametag)
        ResAll['RF']=RF
             
    ####Compute mutation order error####
    #Sequential_Error,Parallel_Error,Concurrent_Error: see ref [1] for th detail of thse error rates
    Sequential_Error,Parallel_Error,Concurrent_Error = Functions.Compute_mut_order_error(InfMegTMP,TruMegTMP)
    ResAll['AncError']=Sequential_Error
    ResAll['SibError']=Parallel_Error
    ResAll['CluError']=Concurrent_Error

    ####Compute MLTED####
    #MLTED: MLTED score computed by using the 'main' software.
    MLTED=Functions.Compute_MLTED(TrueMultiTree,InfMegTMP,TruMegTMP,Rpath,MLTEDpath,intag)
    ResAll['MLTED']=MLTED

    ####Compute TreeVec####
    #TreeVec: TreeVec distance by using treespace package in R.
    TreeVec=Functions.Compute_TreeVec(TrueMultiTree,InfMegTMP,Inf2TruClo,TrueCloneLs,Tru2Inf2GE,Rpath,intag)
    ResAll['TreeVec']=TreeVec


    ####Delete unnecessary files####
    #os.remove('test.out')
    #os.remove('test.nwk')
    #os.remove('test1.nwk')
    os.remove(InfMegTMP[:-4]+'_TrueCloneAnnotation.txt')
    os.remove(InfMegTMP)
    os.remove(TruMegTMP)
    outf.write(Sim+'\t'+ID+'\t'+str(ResAll)+'\n')
                     
#####MLTED, TreeVec, RF, and error rate of ordering mutations (scores) reported in ref [1] in README.txt####
#xl = pd.ExcelFile('Summary.xlsx')

####make output excel with score of "YournMethod"####
#Out: output file name
#Out=Input[:-6]+'_summary.xlsx'
#Functions.make_output_excel(Out,ID2Res,xl,Rpath)

#make plot
#os.system(Rpath+' Plots.r')
#shutil.copy2('plot.jpg',Out[:-5]+'.jpg')
#os.remove('plot.jpg')
#os.remove('All.xlsx')