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analyze_supported_splice_sites.py
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__usage__ = """
python analyze_supported_splice_sites.py
--data <FULL_PATH_TO_DATA_FOLDER>
--sssd <FULL_PATH_TO_RNA_SEQ_SUPPORT_FILES> #path to all species (not one particular)
--cov_rep <FULL_PATH_TO_RNA_SEQ_COVERAGE_REPORT_FILE> #names have to be the same as in NCBI folder; RNA-Seq amount
--out <FULL_PATH_TO_OUTPUT_FOLDER>
"""
import glob, re, sys, os
import matplotlib.pyplot as plt
from scipy import stats
import numpy as np
import warnings
def load_splice_site_counts( filename ):
"""! @brief load counts for all splice sites """
gtag, gcag, atac, others = 0, 0, 0, 0
with open( filename, "r" ) as f:
line = f.readline()
while line:
if 'GT..AG' in line:
gtag = int( re.findall( "\d+", line )[0] )
elif 'GC..AG' in line:
gcag = int( re.findall( "\d+", line )[0] )
elif 'AT..AC' in line:
atac = int( re.findall( "\d+", line )[0] )
elif not 'N' in line:
others += int( re.findall( "\d+", line )[0] )
line = f.readline()
return gtag, gcag, atac, others
def generate_brief_overview( input_dir, output_file ):
"""! @brief generate overview based on top (non-canonical) splice sites """
overview_files = glob.glob( input_dir + "*/*_overview.txt" )
print(overview_files)
specs = []
gtags = []
gcags = []
atacs = []
all_others = []
for filename in sorted( overview_files ):
spec = filename.split('/')[-2]
gtag, gcag, atac, others = load_splice_site_counts( filename )
specs.append( spec )
gtags.append( gtag )
gcags.append( gcag )
atacs.append( atac )
all_others.append( others )
with open( output_file, "w" ) as out:
out.write( "splice_site\t" + "\t".join( specs ) + '\n' )
out.write( "GT-AG\t" + "\t".join( map( str, gtags ) ) + '\n' )
out.write( "GC-AG\t" + "\t".join( map( str, gcags ) ) + '\n' )
out.write( "AT-AC\t" + "\t".join( map( str, atacs ) ) + '\n' )
out.write( "others\t" + "\t".join( map( str, all_others ) ) + '\n' )
def correlate_support_and_coverage( data_dir, splice_site_support_dir, cov_report_file, output_dir ):
"""! @brief correlate support of splice sites with available RNA-Seq read coverage """
# --- load available RNA-Seq read coverage from file --- #
cov_per_spec = {}
with open( cov_report_file, "r" ) as f:
line = f.readline()
while line:
parts = line.strip().split('\t')
cov_per_spec.update( { parts[0].lower(): float( parts[-1] ) } )
line = f.readline()
# --- get total number of annotated splice sites per species --- #
total_splice_sites_per_spec = {}
total_ncss_per_spec = {}
total_splice_site_files = glob.glob( data_dir + "*.txt" )
for filename in total_splice_site_files:
counter = 0
ncss_counter = 0
ID = filename.split('/')[-1].split('.')[0].lower()
with open( filename, "r" ) as f:
line = f.readline()
while line:
parts = line.strip().split('\t')
if len( parts ) > 3:
counter += 1
if parts[3] == "ncss":
ncss_counter += 1
line = f.readline()
total_splice_sites_per_spec.update( { ID: counter } )
total_ncss_per_spec.update( { ID: ncss_counter } )
# --- get total number of supported splice sites per species --- #
supported_splice_sites_per_spec = {}
supported_ncss_per_spec = {}
supported_splice_site_files = glob.glob( splice_site_support_dir + "*/supported_ncss.txt" )
#name should be changed to "supported_splice_sites.txt"
for filename in supported_splice_site_files:
counter = 0
ncss_counter = 0
ID = filename.split('/')[-2].lower()
with open( filename, "r" ) as f:
line = f.readline()
while line:
parts = line.strip().split('\t')
if len( parts ) == 3:
if ( ( parts[1] == "GT" ) + ( parts[2] == "AG" ) ) < 2:
ncss_counter += 1
counter += 1
line = f.readline()
supported_splice_sites_per_spec.update( { ID: counter } )
supported_ncss_per_spec.update( { ID: ncss_counter } )
# --- constructing general correlation figure --- #
fig_file = output_dir + "RNA_seq_cov_splice_site_support_correlation.png"
fig, ax = plt.subplots()
x_values = [] #canonical splice sites
y_values = [] #non-canonical splice sites
y_values2 = [] #percentage
for spec in cov_per_spec.keys():
try:
y_values.append( supported_splice_sites_per_spec[ spec ] )
percent = (100.0*supported_splice_sites_per_spec[ spec ] ) / total_splice_sites_per_spec[ spec ]
print spec + "\t" + str( percent ) + "%"
y_values2.append( percent )
x_values.append( cov_per_spec[ spec ]/ 1000000000.0 )
except KeyError:
print spec
ax.scatter( x_values, y_values, color="green", label="counts" )
ax.scatter( [], [], color="blue", label="percent" )
ax2 = ax.twinx()
ax2.scatter( x_values, y_values2, color="blue", label="percent" )
ax.set_xlabel( "total number of sequenced nt in RNA-Seq data [billion]" )
ax.set_ylabel( "number of supported splice sites" )
ax2.set_ylabel( "percent of supported splice sites" )
c1, p1 = stats.spearmanr( x_values, y_values )
print "Spearman correlation between number supported splice sites and nucleotides sequenced: " + str( c1 ) + " (p-value=" + str( p1 ) + ")"
ax.set_title( "RNA-Seq coverage and splice site support: r="+str(c1)+", p="+str( p1 ), fontsize=5 )
ax.legend( bbox_to_anchor=( 0.9, 0.9 ), fontsize=5 )
plt.subplots_adjust( left=0.15, top=0.95, right=0.9, bottom=0.12 )
fig.savefig( fig_file, dpi=300 )
# --- construct ncss correlation figure --- #
fig_file = output_dir + "RNA_seq_cov_ncss_support_correlation.png"
fig, ax = plt.subplots()
x_values = [] #canonical splice sites
y_values = [] #non-canonical splice sites
y_values2 = [] #percentage
for spec in cov_per_spec.keys():
try:
try:
percent = (100.0*supported_ncss_per_spec[ spec ] ) / total_ncss_per_spec[ spec ]
y_values.append( supported_ncss_per_spec[ spec ] )
print spec + "\t" + str( percent ) + "%"
y_values2.append( percent )
x_values.append( cov_per_spec[ spec ] / 1000000000.0 )
except ZeroDivisionError:
print spec + " ZeroDivisionError"
except KeyError:
print spec + " KeyError"
ax.scatter( x_values, y_values, color="green", label="counts" )
ax.scatter( [], [], color="blue", label="percent" )
ax2 = ax.twinx()
ax2.scatter( x_values, y_values2, color="blue", label="percent" )
ax.set_xlabel( "total number of sequenced nt in RNA-Seq data [billion]" )
ax.set_ylabel( "number of supported non-canonical splice sites" )
ax2.set_ylabel( "percent of supported non-canonical splice sites" )
c1, p1 = stats.spearmanr( x_values, y_values )
print "Spearman correlation between number supported ncss and nucleotides sequenced: " + str( c1 ) + " (p-value=" + str( p1 ) + ")"
ax.set_title( "RNA-Seq coverage and splice site support: r="+str(c1)+", p="+str( p1 ), fontsize=5 )
ax.legend( bbox_to_anchor=( 0.9, 0.9 ), fontsize=5 )
plt.subplots_adjust( left=0.15, top=0.95, right=0.9, bottom=0.12 )
fig.savefig( fig_file, dpi=300 )
def construct_overview_figure( overview_file, overview_figure ):
"""! @brief construct a figure to illustrate the ratio between differen splice site combinations """
with open( overview_file, "r" ) as f:
specs = f.readline().strip().split('\t')[1:]
gt_ag = map( int, f.readline().strip().split('\t')[1:] )
gc_ag = map( int, f.readline().strip().split('\t')[1:] )
at_ac = map( int, f.readline().strip().split('\t')[1:] )
all_others = map( int, f.readline().strip().split('\t')[1:] )
gtag = []
gcag = []
atac = []
others = []
for idx, each in enumerate( specs ):
gtag.append( float( gt_ag[ idx ] ) / ( gt_ag[ idx ] + gc_ag[ idx ] + at_ac[ idx ] +all_others[ idx ] ) )
gcag.append( float( gc_ag[ idx ] ) / ( gt_ag[ idx ] + gc_ag[ idx ] + at_ac[ idx ] +all_others[ idx ] ) )
atac.append( float( at_ac[ idx ] ) / ( gt_ag[ idx ] + gc_ag[ idx ] + at_ac[ idx ] +all_others[ idx ] ) )
others.append( float( all_others[ idx ] ) / ( gt_ag[ idx ] + gc_ag[ idx ] + at_ac[ idx ] +all_others[ idx ] ) )
fig, ax = plt.subplots()
ax.boxplot( [ gtag, gcag, atac, others ] )
ax.set_yscale('log')
ax.set_xticklabels( [ "GT-AG", "GC-AG", "AT-AC", "others" ] )
ax.set_ylabel( "proportion of total splice sites" )
fig.savefig( overview_figure, dpi=300 )
plt.close('all')
print "GT-AG (median): " + str( 100*np.median( gtag ) )[:5] + "%"
print "GC-AG (median): " + str( 100*np.median( gcag ) )[:5] + "%"
print "AT-AC (median): " + str( 100*np.median( atac ) )[:5] + "%"
print "others (median): " + str( 100*np.median( others ) )[:5] + "%"
def construct_combined_file_with_splice_site_support( splice_site_support_dir, output_dir ):
"""! @brief combine all files with splice site support values """
output_file = output_dir + "all_supported_splice_sites.txt"
input_files = sorted( glob.glob( splice_site_support_dir + "*/splice_site_coverage_check.txt" ) )
with open( output_file, "w" ) as out:
out.write( "Species\tGeneID\tExon3prime\tIntron5prime\tIntron3prime\tExon5prime\t5prime_splice_site\t3prime_splice_site\n" )
for filename in input_files:
ID = filename.split('/')[-2]
with open( filename, "r" ) as f:
f.readline() #header
line = f.readline()
while line:
out.write( ID + '\t' + line )
line = f.readline()
def main( arguments ):
"""! @brief assess correlation between RNA-Seq coverage and supported splice sites """
data_dir = arguments[ arguments.index( '--data' )+1 ] #data folder with all NCBI files after processing
splice_site_support_dir = arguments[ arguments.index( '--sssd' )+1 ] #RNA-Seq ncss support folder
cov_report_file = arguments[ arguments.index( '--cov_rep' )+1 ] #RNA-seq coverage overview file
output_dir = arguments[ arguments.index( '--out' )+1 ] #output folder
if output_dir[-1] != '/':
output_dir += "/"
if not os.path.exists( output_dir ):
os.makedirs( output_dir )
overview_file = output_dir + "overview.txt"
generate_brief_overview( splice_site_support_dir, overview_file )
overview_figure = output_dir + "overview.png"
construct_overview_figure( overview_file, overview_figure )
#analyze percentage of supported splice sites and correlate it with coverage
correlate_support_and_coverage( data_dir, splice_site_support_dir, cov_report_file, output_dir )
#construct combined splice site coverage file
construct_combined_file_with_splice_site_support( splice_site_support_dir, output_dir )
print "all done!"
if __name__ == "__main__":
if '--data' in sys.argv and '--sssd' in sys.argv and '--cov_rep' in sys.argv and '--out' in sys.argv:
main( sys.argv )
else:
sys.exit( __usage__ )