diff --git a/isoslam/all_introns_counts_and_info.py b/isoslam/all_introns_counts_and_info.py index 7c6a9e1..02850bb 100644 --- a/isoslam/all_introns_counts_and_info.py +++ b/isoslam/all_introns_counts_and_info.py @@ -118,7 +118,7 @@ def fragment_iterator(read_iterator): with open(argv_as_dictionary["outfile_tsv"], "w") as outfile: # Add column headers outfile.write( - "Read_UID\tTranscript_id\tStart\tEnd\tChr\tStrand\tAssignment\tConversions\tConvertable\tCoverage\n" + "Read_UID\tTranscript_id\tStart\tEnd\tChr\tStrand\tAssignment\tConversions\tConvertible\tCoverage\n" ) results = pd.DataFrame() @@ -143,7 +143,7 @@ def fragment_iterator(read_iterator): if i_progress == 10000: # E.debug(str(i_total_progress) + " pairs processed") - # E.debug(str(i) + "spliced/retained pairs proccessed") + # E.debug(str(i) + "spliced/retained pairs processed") i_progress = 0 read1_start = read1.reference_start @@ -268,7 +268,7 @@ def fragment_iterator(read_iterator): continue first_matched += 1 - # Create a set of tupples: (tx_id,(start,end)) + # Create a set of tuples: (tx_id,(start,end)) # Retained assign_conversions_to_retained = [] @@ -355,7 +355,7 @@ def fragment_iterator(read_iterator): # in the forward read. if strand == "+": # pass if mapped to +ve transcript - convertable = set() + convertible = set() # create a set (list that only allows unique values to be added) # we will add the genome_pos at each point for both reads # len(coverage) will be the # of uniquely covered positions @@ -376,7 +376,7 @@ def fragment_iterator(read_iterator): read_seq = forward_read.query_sequence[read_pos] if genome_seq.upper() == "T": - convertable.add(genome_pos) + convertible.add(genome_pos) if read_seq == "C" and genome_seq == "t": variants_at_position = list( @@ -400,7 +400,7 @@ def fragment_iterator(read_iterator): read_seq = reverse_read.query_sequence[read_pos] if genome_seq.upper() == "A": - convertable.add(genome_pos) + convertible.add(genome_pos) if read_seq == "G" and genome_seq == "a": variants_at_position = list( @@ -417,7 +417,7 @@ def fragment_iterator(read_iterator): elif strand == "-": # pass if mapped to -ve transcript - convertable = set() + convertible = set() coverage = set() converted_position = set() for base in forward_read.get_aligned_pairs(with_seq=True): @@ -430,7 +430,7 @@ def fragment_iterator(read_iterator): read_seq = forward_read.query_sequence[read_pos] if genome_seq.upper() == "A": - convertable.add(genome_pos) + convertible.add(genome_pos) if read_seq == "G" and genome_seq == "a": variants_at_position = list( @@ -455,7 +455,7 @@ def fragment_iterator(read_iterator): read_seq = reverse_read.query_sequence[read_pos] if genome_seq.upper() == "T": - convertable.add(genome_pos) + convertible.add(genome_pos) if read_seq == "C" and genome_seq == "t": variants_at_position = list( @@ -476,7 +476,7 @@ def fragment_iterator(read_iterator): i_output += 1 # Stream output as a tsv - # Format: read_uid, transcript_id, start, end, ret/spl, conversions, convertable, coverage + # Format: read_uid, transcript_id, start, end, ret/spl, conversions, convertible, coverage # A read pair will cover multiple lines if it matches multiple events (but metadata will be same) # ns-rse : Add in building Pandas dataframe so the function can return something that is testable for transcript_id, position in assign_conversions_to_retained: @@ -484,7 +484,7 @@ def fragment_iterator(read_iterator): outfile.write( f"{i_output}\t{transcript_id}\t" f"{start}\t{end}\t{chr}\t{strand}\tRet\t{len(converted_position)}\t" - f"{len(convertable)}\t{len(coverage)}\n" + f"{len(convertible)}\t{len(coverage)}\n" ) row = pd.DataFrame( [ @@ -497,7 +497,7 @@ def fragment_iterator(read_iterator): "Strand": strand, "Assignment": "Ret", "Conversions": len(converted_position), - "Convertable": len(convertable), + "Convertible": len(convertible), "Coverage": len(coverage), } ] @@ -509,7 +509,7 @@ def fragment_iterator(read_iterator): outfile.write( f"{i_output}\t{transcript_id}\t" f"{start}\t{end}\t{chr}\t{strand}\tSpl\t{len(converted_position)}\t" - f"{len(convertable)}\t{len(coverage)}\n" + f"{len(convertible)}\t{len(coverage)}\n" ) row = pd.DataFrame( [ @@ -522,7 +522,7 @@ def fragment_iterator(read_iterator): "Strand": strand, "Assignment": "Spl", "Conversions": len(converted_position), - "Convertable": len(convertable), + "Convertible": len(convertible), "Coverage": len(coverage), } ]