pos_set = set()

with open(pos_list,'r') as fi:

for line in fi:

if len(line) > 3:

pos_set.add(tuple(line.strip().split('\t')[:4]))

if len(locus_list) >= depth_thresh:

if not control and np.mean(locus_list) >= mod_thresh:

return True

elif control and np.mean(locus_list) < mod_thresh:

return True

else:

#scf7180000000004|quiver kinModCall m4C 6644 6644 20 + . coverage=16;context=CGAAATCATTTCTCAGCAGGCGGTAAAAATGGCGGTTTTCG;IPDRatio=3.13;frac=1.000;fracLow=1.000;fracUp=1.000;identificationQv=7

csome,nextpos,strand,deets = line_info

line = '\t'.join([csome,"kinModCall","m6A",nextpos,nextpos,'10',strand,'.',deets])+'\n' #10 for all positions, giving a "log10 score" of 1 per read for motif calling with MotifMaker

ref_dict = {}

pos_context_dict = {}

for contig in SeqIO.parse(ref,"fasta"):

ref_dict[contig.id] = str(contig.seq)

for pos in pos_dict:

if pos[0] in ref_dict:

cx = ref_dict[pos[0]][int(pos[1])-20:int(pos[1])+21].upper()

if pos[4] == '-':

cx = revcomp(cx)

pos_context_dict[pos] = cx #make adjustable length of context?

ref_dict = {}

colours = {'m6A':'#B4656F','A':'#55B196'} #TODO update for other labels

if len(currents) > 1 and cluster :

pdistance = ssd.pdist(currents,metric='correlation')

dm = ssd.squareform(pdistance)

link = linkage(dm,method='complete',metric='correlation')

klabels = fcluster(link,2,'maxclust') #1,'inconsistent') #2,'maxclust')

#klabels = [1 if x == 1 else 0 for x in klabels]

#labels = ['m6A']*len(klabels)

strategy = 'correlation'

else:

klabels = [1 if x==1 else 0 for x in original_labels]

strategy = 'classifierProb'

if plot:

plot_w_labels(klabels,original_labels,currents,strategy,context,'chrom.'+chrom+'.pos.'+pos1,plotdir,colours)

pos_dict = {}

if verbose_results:

pos_dict_verbose = {}

if pos_list:

pos_set = make_pos_set(pos_list)

values_dict = {}

for line in open(meth_fi,'r'):

#try:

#print line

try:

csome,read,pos,context,values,strand,label,prob = tuple(line.split('\t'))

except: #for backwards compatibility; does not work with verbose results

csome,read,pos,context,values,strand,label = tuple(line.split('\t'))

nextpos = str(int(pos)+1)

if (pos_list and (csome,pos,nextpos,strand) not in pos_set) or (context[int(len(context)/2)] != 'M'):

continue

if (csome,pos,nextpos,context,strand) not in pos_dict:

pos_dict[(csome,pos,nextpos,context,strand)] = []

values_dict[(csome,pos,nextpos,context,strand)] = []

if verbose_results:

pos_dict_verbose[(csome,pos,nextpos,context,strand)] = []

if (pos_list and (csome,pos,nextpos,strand) in pos_set) or (not pos_list and plot):

values_dict[(csome,pos,nextpos,context,strand)].append([float(v) for v in values.split(',')][:-1])

if label[0] == 'm':

pos_dict[(csome,pos,nextpos,context,strand)].append(1)

else:

pos_dict[(csome,pos,nextpos,context,strand)].append(0)

if verbose_results:

pos_dict_verbose[(csome,pos,nextpos,context,strand)].append(prob.strip())

#except:

# pass

print(values_dict)

if plotsummary:

print('plotting all current deviations...')

num2lab = {0:'A',1:'m6A'}

curlab = [(val,num2lab[lab],lab) for pos_tup in values_dict for val,lab in zip(values_dict[pos_tup],pos_dict[pos_tup])]

currents,labels,klabels = zip(*curlab)

colours = {'m6A':'#B4656F','A':'#55B196'}

plot_w_labels(klabels,labels,currents,'classifierProb','allpos','allpos',plotdir,colours,alpha=0.3)

print('finished plotting.')

if plot:

for locus in values_dict:

cluster(values_dict[locus],locus[3],['m6A' if x == 1 else 'A' for x in pos_dict[locus]],locus[0],locus[1],plot,plotdir)

if pos_list:

for locus in values_dict:

values_df = pd.DataFrame(values_dict[locus])

tvals = []

pvals = []

for i in values_df.columns: #[:-1]:

#for j in values_df.columns[i+1:]:

ttest = stats.ttest_1samp(values_df[i],0)

#ttest = stats.ttest_rel(values_df[i],values_df[i+1])

#tvals.append(ttest[0])

pvals.append((ttest[1],ttest[0]))

pval = (sum([-np.log10(x[0]) for x in pvals]), max([x[1] for x in pvals])) #min(pvals)

values_dict[locus] = [np.round(x,3) for x in [pval[1],pval[0]]]

if ref:

context_dict = ref2context(ref,pos_dict)

count = 0

outfi = open(aggfi,'w')

for locus in pos_dict.keys():

a = (not pos_list) and check_thresh(pos_dict[locus],mod_thresh,depth_thresh,control)

b = pos_list and (locus[0],locus[1],locus[2],locus[4]) in pos_set #and #'A' not in set(locus[4])

if ref:

cx = context_dict[locus]

else:

cx = locus[3]

if a or b:

count+=1

frac = np.mean(pos_dict[locus])

if gff:

deets = 'coverage='+str(len(pos_dict[locus]))+';context='+cx+';IPDRatio=5;frac='+str(frac)

if verbose_results:

probs = [float(x) for x in pos_dict_verbose[locus]]

se_95 = 2*stats.sem(probs)

deets = deets + ';fracLow='+str(frac-se_95)+';fracUp='+str(frac+se_95)+';identificationQv='+str(int(100*np.mean([float(x) for x in pos_dict_verbose[locus]])))

gff_info = (locus[0],locus[2],locus[4],deets)

write_gff(outfi,gff_info)

else:

print(aggfi)

out_line = '\t'.join(list(locus)[:-1]+[str(np.mean(pos_dict[locus]))]+[locus[-1]]+[str(len(pos_dict[locus]))]) #+[str(x) for x in values_dict[locus]])

if pos_list:

out_line = out_line + '\t' + '\t'.join([str(x) for x in values_dict[locus]])

if verbose_results:

out_line = out_line + '\t'+','.join(pos_dict_verbose[locus])

outfi.write(out_line+'\n')

if not pos_list:

if not control:

print(count, 'methylated loci found with min depth', depth_thresh, 'reads')

else:

#parse command line options

from argparse import ArgumentParser

parser = ArgumentParser(description='Produce bed file of methylated positions based on mCaller output')

parser.add_argument('-d','--min_read_depth',type=int,required=False,help='minimum coverage of position to determine methylation (default = 15)',default=15)

parser.add_argument('-t','--mod_threshold',type=float,required=False,help='minimum fraction of observations with probability of methylation >=50%% at a position to include in report (default = 0.5)',default=0.5)

parser.add_argument('-f','--mCaller_file',type=str,required=True,help='the output file from mCaller to summarize')

parser.add_argument('-p','--positions',type=str,required=False,help='~bed file of positions for which to calculate %% of methylated reads (chromosome,start,end,strand); ignores other thresholds')

parser.add_argument('--control',action='store_true',required=False,help='take unmethylated positions as a control for motif detection')

parser.add_argument('--gff',action='store_true',required=False,help='output PacBio-style gff instead of bed ("identificationQv" score will be average probability of methylation)')

parser.add_argument('--ref',type=str,required=False,help='use reference fasta to output longer contexts surrounding a base, from -20 to +20')

parser.add_argument('--plot',action='store_true',required=False,help='plot currents deviations at the positions included (not recommended for many positions)')

parser.add_argument('--plotsummary',action='store_true',required=False,help='plot currents deviations summarized across the positions included')

parser.add_argument('--plotdir',type=str,required=False,default='mCaller_position_plots',help='output directory for plots, default=mCaller_position_plots')

parser.add_argument('--vo',action='store_true',required=False,help='verbose output including probabilities for each position')

parser.add_argument('-v','--version',action='version',help='print version',version='%(prog)s v1.0')

args = parser.parse_args()

assert os.path.isfile(args.mCaller_file), 'file not found at '+args.mCaller_file

if args.positions:

output_file = args.mCaller_file.split('.')[0]+'.methylation.positions.summary'

elif not args.control:

output_file = args.mCaller_file.split('.')[0]+'.methylation.summary'

else:

output_file = args.mCaller_file.split('.')[0]+'.methylation.control.summary'

if args.gff:

output_file = output_file+'.gff'

else:

output_file = output_file+'.bed'

if not os.path.isdir(args.plotdir):

os.mkdir(args.plotdir)

print(args.mCaller_file)