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Single end read handling #1

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9be25c1
Add single-end sequencing logic to collect_fragments()
ivaldivi-fh May 12, 2025
7de9724
Add single-end sequencing logic to collect_reads()
ivaldivi-fh May 12, 2025
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43 changes: 36 additions & 7 deletions scripts/griffin_GC_counts.py
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Original file line number Diff line number Diff line change
Expand Up @@ -101,6 +101,11 @@


def collect_reads(sublist):
"""
Function to create a dictionary of dictionaries that holds the frequency of each read length and GC content combination.
Additional logic is included to handle single-end sequencing. This version uses sequence length in place of a read's template length attribute
to represent read length.
"""
#create a dict for holding the frequency of each read length and GC content
GC_dict = {}
for length in range(size_range[0],size_range[1]+1):
Expand All @@ -116,7 +121,6 @@ def collect_reads(sublist):
#this might also need to be in the loop
#import the ref_seq
ref_seq=pysam.FastaFile(ref_seq_path)

for i in range(len(sublist)):
chrom = sublist.iloc[i][0]
start = sublist.iloc[i][1]
Expand All @@ -128,7 +132,7 @@ def collect_reads(sublist):
fetched = bam_file.fetch(chrom,start,end)
for read in fetched:
#use both fw (positive template length) and rv (negative template length) reads
if (read.is_reverse==False and read.template_length>=size_range[0] and read.template_length<=size_range[1]) or (read.is_reverse==True and -read.template_length>=size_range[0] and -read.template_length<=size_range[1]):
if (read.is_reverse==False and read.template_length>=size_range[0] and read.template_length<=size_range[1]) or (read.is_reverse==True and -read.template_length>=size_range[0] and -read.template_length<=size_range[1]):
#qc filters, some longer fragments are considered 'improper pairs' but I would like to keep these
if read.is_paired==True and read.mapping_quality>=map_q and read.is_duplicate==False and read.is_qcfail==False:
if read.is_reverse==False:
Expand All @@ -146,18 +150,42 @@ def collect_reads(sublist):
rng = np.random.default_rng(fragment_start)
fragment_seq[np.isin(fragment_seq, ['N','R','Y','K','M','B','D','H','V'])] = rng.integers(2, size=len(fragment_seq[np.isin(fragment_seq, ['N','R','Y','K','M','B','D','H','V'])])) #random integer in range(2) (i.e. 0 or 1)
fragment_seq = fragment_seq.astype(float)


num_GC = int(fragment_seq.sum())
GC_dict[abs(read.template_length)][num_GC]+=1

# Additional logic to handle single-end reads.
elif read.is_paired==False and read.mapping_quality>= map_q and read.is_duplicate==False and read.is_qcfail==False:
if read.is_reverse:
rl = len(read.seq)
fragment_end = read.reference_end + rl
fragment_start= read.reference_end
if not read.is_reverse:
rl = len(read.seq)
fragment_end = read.reference_start + rl
fragment_start= read.reference_start
# check the read length is within the designated size range
if rl >=size_range[0] and rl <= size_range[1]:
fragment_seq = ref_seq.fetch(read.reference_name,fragment_start,fragment_end)
fragment_seq = np.array(list(fragment_seq.upper()))
fragment_seq[np.isin(fragment_seq, ['A','T','W'])] = 0
fragment_seq[np.isin(fragment_seq, ['C','G','S'])] = 1
rng = np.random.default_rng(fragment_start)
fragment_seq[np.isin(fragment_seq, ['N','R','Y','K','M','B','D','H','V'])] = rng.integers(2, size=len(fragment_seq[np.isin(fragment_seq, ['N','R','Y','K','M','B','D','H','V'])])) #random integer in range(2) (i.e. 0 or 1)
fragment_seq = fragment_seq.astype(float)

num_GC = int(fragment_seq.sum())
breakpoint()
GC_dict[abs(rl)][num_GC]+=1

print('done')
return(GC_dict)


# In[ ]:


## Parallel processing applied to the collect_reads function
start_time = time.time()
p = Pool(processes=CPU) #use the available CPU
sublists = np.array_split(mappable_intervals,CPU) #split the list into sublists, one per CPU
Expand All @@ -169,10 +197,11 @@ def collect_reads(sublist):


all_GC_df = pd.DataFrame()
for i,GC_dict in enumerate(GC_dict_list):
for i, GC_dict in enumerate(GC_dict_list):
GC_df = pd.DataFrame()
for length in GC_dict.keys():
current = pd.Series(GC_dict[length]).reset_index()
curr_dict = GC_dict_list.get(GC_dict)
for length in curr_dict.keys():
current = pd.Series(curr_dict[length]).reset_index()
current = current.rename(columns={'index':'num_GC',0:'number_of_fragments'})
current['length']=length
current = current[['length','num_GC','number_of_fragments']]
Expand Down
102 changes: 83 additions & 19 deletions scripts/griffin_coverage.py
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Original file line number Diff line number Diff line change
Expand Up @@ -314,6 +314,7 @@ def closest_key(dictionary, i):
start_time = time.time()
all_sites_bed = pybedtools.BedTool.from_dataframe(all_sites[[chrom_column,'fetch_start','fetch_end']])
all_sites_bed = all_sites_bed.sort()
# Merges any overlapping sites
all_sites_bed = all_sites_bed.merge()
print('Intervals to fetch:\t'+str(len(all_sites_bed)))
print('Total bp to fetch:\t'+str(all_sites_bed.total_coverage()))
Expand All @@ -340,6 +341,13 @@ def closest_key(dictionary, i):


def collect_fragments(input_list):
"""
Collects fragments for a single site.
Added logic to handle single end reads. The fragment length is calculated using sequence length.
'Regular' (paired-end read) data uses the template length attribute to get fragment length.
params:
input list = [index, chrom, start, end]
"""
i,chrom,start,end = input_list
#open the bam file for each pool worker (otherwise individual pool workers can close it)
bam_file = pysam.AlignmentFile(bam_path)
Expand All @@ -364,14 +372,70 @@ def collect_fragments(input_list):
########################
for read in fetched:
#filter out reads
if abs(read.template_length)>=sz_range[0] and abs(read.template_length)<=sz_range[1] and read.is_paired==True and read.mapping_quality>=map_q and read.is_duplicate==False and read.is_qcfail==False:
#only use fw reads with positive fragment lengths (negative indicates an abnormal pair)
#all paired end reads have a fw and rv read so we don't need the rv read to find the midpoint.
if read.is_reverse==False and read.template_length>0:
fragment_start = read.reference_start #for fw read, read start is fragment start
fragment_end = read.reference_start+read.template_length
midpoint = int(np.floor((fragment_start+fragment_end)/2))

if read.is_paired:
if abs(read.template_length)>=sz_range[0] and abs(read.template_length)<=sz_range[1] and read.mapping_quality>=map_q and read.is_duplicate==False and read.is_qcfail==False:
#only use fw reads with positive fragment lengths (negative indicates an abnormal pair)
#all paired end reads have a fw and rv read so we don't need the rv read to find the midpoint.
if read.is_reverse==False and read.template_length>0:
fragment_start = read.reference_start #for fw read, read start is fragment start
fragment_end = read.reference_start+read.template_length
midpoint = int(np.floor((fragment_start+fragment_end)/2))

#count the GC content
fragment_seq = ref_seq.fetch(read.reference_name,fragment_start,fragment_end)
fragment_seq = np.array(list(fragment_seq.upper()))
fragment_seq[np.isin(fragment_seq, ['A','T','W'])] = 0
fragment_seq[np.isin(fragment_seq, ['C','G','S'])] = 1
rng = np.random.default_rng(fragment_start)
fragment_seq[np.isin(fragment_seq, ['N','R','Y','K','M','B','D','H','V'])] = rng.integers(2, size=len(fragment_seq[np.isin(fragment_seq, ['N','R','Y','K','M','B','D','H','V'])])) #random integer in range(2) (i.e. 0 or 1)
fragment_seq = fragment_seq.astype(float)

if mappability_correction.lower() == 'true':
#find the two read locations for mappability correction
fw_read_map = mappability.values(chrom,read.reference_start,read.reference_start+read.reference_length)
fw_read_map = np.mean(np.nan_to_num(fw_read_map)) #replace any nan with zero and take the mean

rv_read_map = mappability.values(chrom,read.reference_start+read.template_length-read.reference_length,read.reference_start+read.template_length)
rv_read_map = np.mean(np.nan_to_num(rv_read_map)) #replace any nan with zero and take the mean

#check that the site is in the window
if midpoint>=start and midpoint<end:
#count the fragment
cov_dict[midpoint]+=1

##get the GC bias
read_GC_content = sum(fragment_seq)
read_GC_bias = GC_bias[abs(read.template_length)][read_GC_content]

#count the fragment weighted by GC bias
if not np.isnan(read_GC_bias):
GC_cov_dict[midpoint]+=(1/read_GC_bias)

if mappability_correction.lower() == 'true':
#get the mappability bias
read_map = np.int32(np.round(100*(fw_read_map+rv_read_map)/2))
read_map_bias = mappability_bias[read_map]
GC_map_cov_dict[midpoint]+=(1/read_GC_bias)*(1/read_map_bias)

#print(read_GC_bias,read_map,read_map_bias)

else: #if fragment doesn't fully overlap
continue

del(read,midpoint,fragment_seq)

else: # handling the single-end reads
if len(read.seq)>=sz_range[0] and len(read.seq)<=sz_range[1] and read.mapping_quality>=map_q and read.is_duplicate==False and read.is_qcfail==False:
rl = len(read.seq)
if read.is_reverse == False:
fragment_start = read.reference_start #for fw read, read start is fragment start
fragment_end = read.reference_start+rl
midpoint = int(np.floor((fragment_start+fragment_end)/2))
else:
fragment_start = read.reference_end #for fw read, read start is fragment start
fragment_end = read.reference_end+rl
midpoint = int(np.floor((fragment_start+fragment_end)/2))

#count the GC content
fragment_seq = ref_seq.fetch(read.reference_name,fragment_start,fragment_end)
fragment_seq = np.array(list(fragment_seq.upper()))
Expand All @@ -386,17 +450,17 @@ def collect_fragments(input_list):
fw_read_map = mappability.values(chrom,read.reference_start,read.reference_start+read.reference_length)
fw_read_map = np.mean(np.nan_to_num(fw_read_map)) #replace any nan with zero and take the mean

rv_read_map = mappability.values(chrom,read.reference_start+read.template_length-read.reference_length,read.reference_start+read.template_length)
rv_read_map = mappability.values(chrom,read.reference_start+rl-read.reference_length,read.reference_start+rl)
rv_read_map = np.mean(np.nan_to_num(rv_read_map)) #replace any nan with zero and take the mean

#check that the site is in the window
#check that the site is in the window
if midpoint>=start and midpoint<end:
#count the fragment
cov_dict[midpoint]+=1

##get the GC bias
read_GC_content = sum(fragment_seq)
read_GC_bias = GC_bias[abs(read.template_length)][read_GC_content]
read_GC_bias = GC_bias[abs(rl)][read_GC_content]

#count the fragment weighted by GC bias
if not np.isnan(read_GC_bias):
Expand All @@ -409,15 +473,11 @@ def collect_fragments(input_list):
GC_map_cov_dict[midpoint]+=(1/read_GC_bias)*(1/read_map_bias)

#print(read_GC_bias,read_map,read_map_bias)

else: #if fragment doesn't fully overlap
continue

continue
del(read,midpoint,fragment_seq)

else:
#print('reverse',read.is_reverse)
continue


output = pd.DataFrame(pd.Series(cov_dict, name = 'uncorrected'))
output['GC_corrected'] = pd.Series(GC_cov_dict)
Expand Down Expand Up @@ -452,9 +512,13 @@ def collect_fragments(input_list):
sys.stdout.flush()
start_time = time.time()

## Comment out to run debugging mode:
p = Pool(processes=CPU) #use the specified number of processes
results = p.map(collect_fragments, to_fetch.values, 1) #Send only one interval to each processor at a time.

## Uncomment for debugging mode:
# results = collect_fragments(to_fetch.values[0])

elapsed_time = time.time()-overall_start_time
print('Done with fetch '+str(int(np.floor(elapsed_time/60)))+' min '+str(int(np.round(elapsed_time%60)))+' sec')
del(elapsed_time)
Expand Down