Mercurial > repos > jackcurragh > trips_viz_bam_to_sqlite
changeset 3:932cdd84d51a draft
Deleted selected files
| author | triasteran |
|---|---|
| date | Mon, 25 Apr 2022 12:38:47 +0000 |
| parents | c8d8675697c6 |
| children | 4ee95ba271a5 |
| files | trips_bam_to_sqlite/bam_to_sqlite.py trips_bam_to_sqlite/test-data/test.bam trips_bam_to_sqlite/test-data/test.bamv2.sqlite trips_bam_to_sqlite/test-data/test_n_sorted.bam trips_bam_to_sqlite/test-data/test_n_sorted.bam_n_sorted.bam trips_bam_to_sqlite/test-data/test_n_sorted.bamv2.sqlite trips_bam_to_sqlite/test-data/test_org.sqlite trips_bam_to_sqlite/test-data/test_organism.sqlite trips_bam_to_sqlite/test-data/test_sorted.bam trips_bam_to_sqlite/test-data/test_sorted.bamv2.sqlite trips_bam_to_sqlite/trips_bam_to_sqlite.xml |
| diffstat | 10 files changed, 0 insertions(+), 768 deletions(-) [+] |
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--- a/trips_bam_to_sqlite/bam_to_sqlite.py Wed Apr 20 15:18:00 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,731 +0,0 @@ -import sys -import pysam -import operator -import os -import time -import sqlite3 -from sqlitedict import SqliteDict - - -def tran_to_genome(tran, pos, transcriptome_info_dict): - # print ("tran",list(transcriptome_info_dict)) - traninfo = transcriptome_info_dict[tran] - chrom = traninfo["chrom"] - strand = traninfo["strand"] - exons = sorted(traninfo["exons"]) - # print exons - if strand == "+": - exon_start = 0 - for tup in exons: - exon_start = tup[0] - exonlen = tup[1] - tup[0] - if pos > exonlen: - pos = (pos - exonlen) - 1 - else: - break - genomic_pos = (exon_start + pos) - 1 - elif strand == "-": - exon_start = 0 - for tup in exons[::-1]: - exon_start = tup[1] - exonlen = tup[1] - tup[0] - if pos > exonlen: - pos = (pos - exonlen) - 1 - else: - break - genomic_pos = (exon_start - pos) + 1 - return (chrom, genomic_pos) - - -# Takes a dictionary with a readname as key and a list of lists as value, each sub list has consists of two elements a transcript and the position the read aligns to in the transcript -# This function will count the number of genes that the transcripts correspond to and if less than or equal to 3 will add the relevant value to transcript_counts_dict -def processor( - process_chunk, - master_read_dict, - transcriptome_info_dict, - master_dict, - readseq, - unambig_read_length_dict, -): - readlen = len(readseq) - ambiguously_mapped_reads = 0 - # get the read name - read = list(process_chunk)[0] - - read_list = process_chunk[ - read - ] # a list of lists of all transcripts the read aligns to and the positions - # used to store different genomic poistions - genomic_positions = [] - - # This section is just to get the different genomic positions the read aligns to - - for listname in process_chunk[read]: - - tran = listname[0].replace("-", "_").replace("(", "").replace(")", "") - - pos = int(listname[1]) - genomic_pos = tran_to_genome(tran, pos, transcriptome_info_dict) - # print ("genomic pos",genomic_pos) - if genomic_pos not in genomic_positions: - genomic_positions.append(genomic_pos) - - # If the read maps unambiguously - if len(genomic_positions) == 1: - if readlen not in unambig_read_length_dict: - unambig_read_length_dict[readlen] = 0 - unambig_read_length_dict[readlen] += 1 - # assume this read aligns to a noncoding position, if we find that it does align to a coding region change this to True - coding = False - - # For each transcript this read alings to - for listname in process_chunk[read]: - # get the transcript name - tran = listname[0].replace("-", "_").replace("(", "").replace(")", "") - # If we haven't come across this transcript already then add to master_read_dict - if tran not in master_read_dict: - master_read_dict[tran] = { - "ambig": {}, - "unambig": {}, - "mismatches": {}, - "seq": {}, - } - # get the raw unedited positon, and read tags - pos = int(listname[1]) - read_tags = listname[2] - # If there is mismatches in this line, then modify the postion and readlen (if mismatches at start or end) and add mismatches to dictionary - nm_tag = 0 - - for tag in read_tags: - if tag[0] == "NM": - nm_tag = int(tag[1]) - if nm_tag > 0: - md_tag = "" - for tag in read_tags: - if tag[0] == "MD": - md_tag = tag[1] - pos_modifier, readlen_modifier, mismatches = get_mismatch_pos( - md_tag, pos, readlen, master_read_dict, tran, readseq - ) - # Count the mismatches (we only do this for unambiguous) - for mismatch in mismatches: - # Ignore mismatches appearing in the first position (due to non templated addition) - if mismatch != 0: - char = mismatches[mismatch] - mismatch_pos = pos + mismatch - if mismatch_pos not in master_read_dict[tran]["seq"]: - master_read_dict[tran]["seq"][mismatch_pos] = {} - if char not in master_read_dict[tran]["seq"][mismatch_pos]: - master_read_dict[tran]["seq"][mismatch_pos][char] = 0 - master_read_dict[tran]["seq"][mismatch_pos][char] += 1 - # apply the modifiers - # pos = pos+pos_modifier - # readlen = readlen - readlen_modifier - - try: - cds_start = transcriptome_info_dict[tran]["cds_start"] - cds_stop = transcriptome_info_dict[tran]["cds_stop"] - - if pos >= cds_start and pos <= cds_stop: - coding = True - except: - pass - - if readlen in master_read_dict[tran]["unambig"]: - if pos in master_read_dict[tran]["unambig"][readlen]: - master_read_dict[tran]["unambig"][readlen][pos] += 1 - else: - master_read_dict[tran]["unambig"][readlen][pos] = 1 - else: - master_read_dict[tran]["unambig"][readlen] = {pos: 1} - - if coding == True: - master_dict["unambiguous_coding_count"] += 1 - elif coding == False: - master_dict["unambiguous_non_coding_count"] += 1 - - else: - ambiguously_mapped_reads += 1 - for listname in process_chunk[read]: - tran = listname[0].replace("-", "_").replace("(", "").replace(")", "") - if tran not in master_read_dict: - master_read_dict[tran] = { - "ambig": {}, - "unambig": {}, - "mismatches": {}, - "seq": {}, - } - pos = int(listname[1]) - read_tags = listname[2] - nm_tag = 0 - for tag in read_tags: - if tag[0] == "NM": - nm_tag = int(tag[1]) - if nm_tag > 0: - md_tag = "" - for tag in read_tags: - if tag[0] == "MD": - md_tag = tag[1] - pos_modifier, readlen_modifier, mismatches = get_mismatch_pos( - md_tag, pos, readlen, master_read_dict, tran, readseq - ) - # apply the modifiers - # pos = pos+pos_modifier - # readlen = readlen - readlen_modifier - if readlen in master_read_dict[tran]["ambig"]: - if pos in master_read_dict[tran]["ambig"][readlen]: - master_read_dict[tran]["ambig"][readlen][pos] += 1 - else: - master_read_dict[tran]["ambig"][readlen][pos] = 1 - else: - master_read_dict[tran]["ambig"][readlen] = {pos: 1} - return ambiguously_mapped_reads - - -def get_mismatch_pos(md_tag, pos, readlen, master_read_dict, tran, readseq): - nucs = ["A", "T", "G", "C"] - mismatches = {} - total_so_far = 0 - prev_char = "" - for char in md_tag: - if char in nucs: - if prev_char != "": - total_so_far += int(prev_char) - prev_char = "" - mismatches[total_so_far + len(mismatches)] = readseq[ - total_so_far + len(mismatches) - ] - else: - if char != "^" and char != "N": - if prev_char == "": - prev_char = char - else: - total_so_far += int(prev_char + char) - prev_char = "" - readlen_modifier = 0 - pos_modifier = 0 - five_ok = False - three_ok = False - while five_ok == False: - for i in range(0, readlen): - if i in mismatches: - pos_modifier += 1 - readlen_modifier += 1 - else: - five_ok = True - break - five_ok = True - - while three_ok == False: - for i in range(readlen - 1, 0, -1): - if i in mismatches: - readlen_modifier += 1 - else: - three_ok = True - break - three_ok = True - - return (pos_modifier, readlen_modifier, mismatches) - - -def process_bam(bam_filepath, transcriptome_info_dict_path, outputfile, desc): - desc = desc - start_time = time.time() - study_dict = {} - nuc_count_dict = {"mapped": {}, "unmapped": {}} - dinuc_count_dict = {} - threeprime_nuc_count_dict = {"mapped": {}, "unmapped": {}} - read_length_dict = {} - unambig_read_length_dict = {} - unmapped_dict = {} - master_dict = { - "unambiguous_non_coding_count": 0, - "unambiguous_coding_count": 0, - "current_dir": os.getcwd(), - } - - transcriptome_info_dict = {} - connection = sqlite3.connect(transcriptome_info_dict_path) - cursor = connection.cursor() - cursor.execute( - "SELECT transcript,cds_start,cds_stop,length,strand,chrom,tran_type from transcripts;" - ) - result = cursor.fetchall() - for row in result: - transcriptome_info_dict[str(row[0])] = { - "cds_start": row[1], - "cds_stop": row[2], - "length": row[3], - "strand": row[4], - "chrom": row[5], - "exons": [], - "tran_type": row[6], - } - # print list(transcriptome_info_dict)[:10] - - cursor.execute("SELECT * from exons;") - result = cursor.fetchall() - for row in result: - transcriptome_info_dict[str(row[0])]["exons"].append((row[1], row[2])) - - # it might be the case that there are no multimappers, so set this to 0 first to avoid an error, it will be overwritten later if there is multimappers - multimappers = 0 - unmapped_reads = 0 - unambiguous_coding_count = 0 - unambiguous_non_coding_count = 0 - trip_periodicity_reads = 0 - - final_offsets = { - "fiveprime": {"offsets": {}, "read_scores": {}}, - "threeprime": {"offsets": {}, "read_scores": {}}, - } - master_read_dict = {} - prev_seq = "" - process_chunk = {"read_name": [["placeholder_tran", "1", "28"]]} - mapped_reads = 0 - ambiguously_mapped_reads = 0 - master_trip_dict = {"fiveprime": {}, "threeprime": {}} - master_offset_dict = {"fiveprime": {}, "threeprime": {}} - master_metagene_stop_dict = {"fiveprime": {}, "threeprime": {}} - - os.system(f'samtools sort -n {bam_filepath} -o {bam_filepath}_n_sorted.bam') - pysam.set_verbosity(0) - infile = pysam.Samfile(f"{bam_filepath}_n_sorted.bam", "rb") - header = infile.header["HD"] - unsorted = False - if "SO" in header: - if header["SO"] != "queryname": - unsorted = True - else: - unsorted = True - if unsorted == True: - print( - "ERROR: Bam file appears to be unsorted or not sorted by read name. To sort by read name use the command: samtools sort -n input.bam output.bam" - ) - print(header, bam_filepath) - sys.exit() - total_bam_lines = 0 - all_ref_ids = infile.references - - for read in infile.fetch(until_eof=True): - total_bam_lines += 1 - if not read.is_unmapped: - ref = read.reference_id - tran = (all_ref_ids[ref]).split(".")[0] - mapped_reads += 1 - if mapped_reads % 1000000 == 0: - print( - "{} reads parsed at {}".format( - mapped_reads, (time.time() - start_time) - ) - ) - pos = read.reference_start - readname = read.query_name - read_tags = read.tags - if readname == list(process_chunk)[0]: - process_chunk[readname].append([tran, pos, read_tags]) - # if the current read is different from previous reads send 'process_chunk' to the 'processor' function, then start 'process_chunk' over using current read - else: - if list(process_chunk)[0] != "read_name": - - # At this point we work out readseq, we do this for multiple reasons, firstly so we don't count the sequence from a read multiple times, just because - # it aligns multiple times and secondly we only call read.seq once (read.seq is computationally expensive) - seq = read.seq - readlen = len(seq) - - # Note if a read maps ambiguously it will still be counted toward the read length distribution (however it will only be counted once, not each time it maps) - if readlen not in read_length_dict: - read_length_dict[readlen] = 0 - read_length_dict[readlen] += 1 - - if readlen not in nuc_count_dict["mapped"]: - nuc_count_dict["mapped"][readlen] = {} - if readlen not in threeprime_nuc_count_dict["mapped"]: - threeprime_nuc_count_dict["mapped"][readlen] = {} - if readlen not in dinuc_count_dict: - dinuc_count_dict[readlen] = { - "AA": 0, - "TA": 0, - "GA": 0, - "CA": 0, - "AT": 0, - "TT": 0, - "GT": 0, - "CT": 0, - "AG": 0, - "TG": 0, - "GG": 0, - "CG": 0, - "AC": 0, - "TC": 0, - "GC": 0, - "CC": 0, - } - - for i in range(0, len(seq)): - if i not in nuc_count_dict["mapped"][readlen]: - nuc_count_dict["mapped"][readlen][i] = { - "A": 0, - "T": 0, - "G": 0, - "C": 0, - "N": 0, - } - nuc_count_dict["mapped"][readlen][i][seq[i]] += 1 - - for i in range(0, len(seq)): - try: - dinuc_count_dict[readlen][seq[i : i + 2]] += 1 - except: - pass - - for i in range(len(seq), 0, -1): - dist = i - len(seq) - if dist not in threeprime_nuc_count_dict["mapped"][readlen]: - threeprime_nuc_count_dict["mapped"][readlen][dist] = { - "A": 0, - "T": 0, - "G": 0, - "C": 0, - "N": 0, - } - threeprime_nuc_count_dict["mapped"][readlen][dist][ - seq[dist] - ] += 1 - ambiguously_mapped_reads += processor( - process_chunk, - master_read_dict, - transcriptome_info_dict, - master_dict, - prev_seq, - unambig_read_length_dict, - ) - process_chunk = {readname: [[tran, pos, read_tags]]} - prev_seq = read.seq - else: - unmapped_reads += 1 - - # Add this unmapped read to unmapped_dict so we can see what the most frequent unmapped read is. - seq = read.seq - readlen = len(seq) - if seq in unmapped_dict: - unmapped_dict[seq] += 1 - else: - unmapped_dict[seq] = 1 - - # Populate the nuc_count_dict with this unmapped read - if readlen not in nuc_count_dict["unmapped"]: - nuc_count_dict["unmapped"][readlen] = {} - for i in range(0, len(seq)): - if i not in nuc_count_dict["unmapped"][readlen]: - nuc_count_dict["unmapped"][readlen][i] = { - "A": 0, - "T": 0, - "G": 0, - "C": 0, - "N": 0, - } - nuc_count_dict["unmapped"][readlen][i][seq[i]] += 1 - - if readlen not in threeprime_nuc_count_dict["unmapped"]: - threeprime_nuc_count_dict["unmapped"][readlen] = {} - - for i in range(len(seq), 0, -1): - dist = i - len(seq) - if dist not in threeprime_nuc_count_dict["unmapped"][readlen]: - threeprime_nuc_count_dict["unmapped"][readlen][dist] = { - "A": 0, - "T": 0, - "G": 0, - "C": 0, - "N": 0, - } - threeprime_nuc_count_dict["unmapped"][readlen][dist][seq[dist]] += 1 - - # add stats about mapped/unmapped reads to file dict which will be used for the final report - master_dict["total_bam_lines"] = total_bam_lines - master_dict["mapped_reads"] = mapped_reads - master_dict["unmapped_reads"] = unmapped_reads - master_read_dict["unmapped_reads"] = unmapped_reads - master_dict["ambiguously_mapped_reads"] = ambiguously_mapped_reads - - if "read_name" in master_read_dict: - del master_read_dict["read_name"] - print("BAM file processed") - print("Creating metagenes, triplet periodicity plots, etc.") - for tran in master_read_dict: - try: - cds_start = transcriptome_info_dict[tran]["cds_start"] - cds_stop = transcriptome_info_dict[tran]["cds_stop"] - except: - continue - - tranlen = transcriptome_info_dict[tran]["length"] - # Use this to discard transcripts with no 5' leader or 3' trailer - if ( - cds_start > 1 - and cds_stop < tranlen - and transcriptome_info_dict[tran]["tran_type"] == 1 - ): - for primetype in ["fiveprime", "threeprime"]: - # Create the triplet periodicity and metainfo plots based on both the 5' and 3' ends of reads - for readlength in master_read_dict[tran]["unambig"]: - # print "readlength", readlength - # for each fiveprime postion for this readlength within this transcript - for raw_pos in master_read_dict[tran]["unambig"][readlength]: - # print "raw pos", raw_pos - trip_periodicity_reads += 1 - if primetype == "fiveprime": - # get the five prime postion minus the cds start postion - real_pos = raw_pos - cds_start - rel_stop_pos = raw_pos - cds_stop - elif primetype == "threeprime": - real_pos = (raw_pos + readlength) - cds_start - rel_stop_pos = (raw_pos + readlength) - cds_stop - # get the readcount at the raw postion - readcount = master_read_dict[tran]["unambig"][readlength][ - raw_pos - ] - # print "readcount", readcount - frame = real_pos % 3 - if real_pos >= cds_start and real_pos <= cds_stop: - if readlength in master_trip_dict[primetype]: - master_trip_dict[primetype][readlength][ - str(frame) - ] += readcount - else: - master_trip_dict[primetype][readlength] = { - "0": 0.0, - "1": 0.0, - "2": 0.0, - } - master_trip_dict[primetype][readlength][ - str(frame) - ] += readcount - # now populate offset dict with the 'real_positions' upstream of cds_start, these will be used for metainfo dict - if real_pos > (-600) and real_pos < (601): - if readlength in master_offset_dict[primetype]: - if ( - real_pos - in master_offset_dict[primetype][readlength] - ): - # print "real pos in offset dict" - master_offset_dict[primetype][readlength][ - real_pos - ] += readcount - else: - # print "real pos not in offset dict" - master_offset_dict[primetype][readlength][ - real_pos - ] = readcount - else: - # initiliase with zero to avoid missing neighbours below - # print "initialising with zeros" - master_offset_dict[primetype][readlength] = {} - for i in range(-600, 601): - master_offset_dict[primetype][readlength][i] = 0 - master_offset_dict[primetype][readlength][ - real_pos - ] += readcount - - # now populate offset dict with the 'real_positions' upstream of cds_start, these will be used for metainfo dict - if rel_stop_pos > (-600) and rel_stop_pos < (601): - if readlength in master_metagene_stop_dict[primetype]: - if ( - rel_stop_pos - in master_metagene_stop_dict[primetype][readlength] - ): - master_metagene_stop_dict[primetype][readlength][ - rel_stop_pos - ] += readcount - else: - master_metagene_stop_dict[primetype][readlength][ - rel_stop_pos - ] = readcount - else: - # initiliase with zero to avoid missing neighbours below - master_metagene_stop_dict[primetype][readlength] = {} - for i in range(-600, 601): - master_metagene_stop_dict[primetype][readlength][ - i - ] = 0 - master_metagene_stop_dict[primetype][readlength][ - rel_stop_pos - ] += readcount - - # master trip dict is now made up of readlengths with 3 frames and a count associated with each frame - # create a 'score' for each readlength by putting the max frame count over the second highest frame count - for primetype in ["fiveprime", "threeprime"]: - for subreadlength in master_trip_dict[primetype]: - maxcount = 0 - secondmaxcount = 0 - for frame in master_trip_dict[primetype][subreadlength]: - if master_trip_dict[primetype][subreadlength][frame] > maxcount: - maxcount = master_trip_dict[primetype][subreadlength][frame] - for frame in master_trip_dict[primetype][subreadlength]: - if ( - master_trip_dict[primetype][subreadlength][frame] > secondmaxcount - and master_trip_dict[primetype][subreadlength][frame] != maxcount - ): - secondmaxcount = master_trip_dict[primetype][subreadlength][frame] - # a perfect score would be 0 meaning there is only a single peak, the worst score would be 1 meaning two highest peaks are the same height - master_trip_dict[primetype][subreadlength]["score"] = float( - secondmaxcount - ) / float(maxcount) - # This part is to determine what offsets to give each read length - print("Calculating offsets") - for primetype in ["fiveprime", "threeprime"]: - for readlen in master_offset_dict[primetype]: - accepted_len = False - max_relative_pos = 0 - max_relative_count = 0 - for relative_pos in master_offset_dict[primetype][readlen]: - # This line is to ensure we don't choose an offset greater than the readlength (in cases of a large peak far up/downstream) - if abs(relative_pos) < 10 or abs(relative_pos) > (readlen - 10): - continue - if ( - master_offset_dict[primetype][readlen][relative_pos] - > max_relative_count - ): - max_relative_pos = relative_pos - max_relative_count = master_offset_dict[primetype][readlen][ - relative_pos - ] - # print "for readlen {} the max_relative pos is {}".format(readlen, max_relative_pos) - if primetype == "fiveprime": - # -3 to get from p-site to a-site, +1 to account for 1 based co-ordinates, resulting in -2 overall - final_offsets[primetype]["offsets"][readlen] = abs(max_relative_pos - 2) - elif primetype == "threeprime": - # +3 to get from p-site to a-site, -1 to account for 1 based co-ordinates, resulting in +2 overall - final_offsets[primetype]["offsets"][readlen] = ( - max_relative_pos * (-1) - ) + 2 - # If there are no reads in CDS regions for a specific length, it may not be present in master_trip_dict - if readlen in master_trip_dict[primetype]: - final_offsets[primetype]["read_scores"][readlen] = master_trip_dict[ - primetype - ][readlen]["score"] - else: - final_offsets[primetype]["read_scores"][readlen] = 0.0 - master_read_dict["offsets"] = final_offsets - master_read_dict["trip_periodicity"] = master_trip_dict - master_read_dict["desc"] = "Null" - master_read_dict["mapped_reads"] = mapped_reads - master_read_dict["nuc_counts"] = nuc_count_dict - master_read_dict["dinuc_counts"] = dinuc_count_dict - master_read_dict["threeprime_nuc_counts"] = threeprime_nuc_count_dict - master_read_dict["metagene_counts"] = master_offset_dict - master_read_dict["stop_metagene_counts"] = master_metagene_stop_dict - master_read_dict["read_lengths"] = read_length_dict - master_read_dict["unambig_read_lengths"] = unambig_read_length_dict - master_read_dict["coding_counts"] = master_dict["unambiguous_coding_count"] - master_read_dict["noncoding_counts"] = master_dict["unambiguous_non_coding_count"] - master_read_dict["ambiguous_counts"] = master_dict["ambiguously_mapped_reads"] - master_read_dict["frequent_unmapped_reads"] = ( - sorted(unmapped_dict.items(), key=operator.itemgetter(1)) - )[-2000:] - master_read_dict["cutadapt_removed"] = 0 - master_read_dict["rrna_removed"] = 0 - # If no reads are removed by minus m there won't be an entry in the log file, so initiliase with 0 first and change if there is a line - master_read_dict["removed_minus_m"] = 0 - master_dict["removed_minus_m"] = 0 - # We work out the total counts for 5', cds 3' for differential translation here, would be better to do thisn in processor but need the offsets - master_read_dict["unambiguous_all_totals"] = {} - master_read_dict["unambiguous_fiveprime_totals"] = {} - master_read_dict["unambiguous_cds_totals"] = {} - master_read_dict["unambiguous_threeprime_totals"] = {} - - master_read_dict["ambiguous_all_totals"] = {} - master_read_dict["ambiguous_fiveprime_totals"] = {} - master_read_dict["ambiguous_cds_totals"] = {} - master_read_dict["ambiguous_threeprime_totals"] = {} - print("calculating transcript counts") - for tran in master_read_dict: - if tran in transcriptome_info_dict: - five_total = 0 - cds_total = 0 - three_total = 0 - - ambig_five_total = 0 - ambig_cds_total = 0 - ambig_three_total = 0 - - cds_start = transcriptome_info_dict[tran]["cds_start"] - cds_stop = transcriptome_info_dict[tran]["cds_stop"] - for readlen in master_read_dict[tran]["unambig"]: - if readlen in final_offsets["fiveprime"]["offsets"]: - offset = final_offsets["fiveprime"]["offsets"][readlen] - else: - offset = 15 - for pos in master_read_dict[tran]["unambig"][readlen]: - real_pos = pos + offset - if real_pos < cds_start: - five_total += master_read_dict[tran]["unambig"][readlen][pos] - elif real_pos >= cds_start and real_pos <= cds_stop: - cds_total += master_read_dict[tran]["unambig"][readlen][pos] - elif real_pos > cds_stop: - three_total += master_read_dict[tran]["unambig"][readlen][pos] - master_read_dict["unambiguous_all_totals"][tran] = ( - five_total + cds_total + three_total - ) - master_read_dict["unambiguous_fiveprime_totals"][tran] = five_total - master_read_dict["unambiguous_cds_totals"][tran] = cds_total - master_read_dict["unambiguous_threeprime_totals"][tran] = three_total - - for readlen in master_read_dict[tran]["ambig"]: - if readlen in final_offsets["fiveprime"]["offsets"]: - offset = final_offsets["fiveprime"]["offsets"][readlen] - else: - offset = 15 - for pos in master_read_dict[tran]["ambig"][readlen]: - real_pos = pos + offset - if real_pos < cds_start: - ambig_five_total += master_read_dict[tran]["ambig"][readlen][ - pos - ] - elif real_pos >= cds_start and real_pos <= cds_stop: - ambig_cds_total += master_read_dict[tran]["ambig"][readlen][pos] - elif real_pos > cds_stop: - ambig_three_total += master_read_dict[tran]["ambig"][readlen][ - pos - ] - - master_read_dict["ambiguous_all_totals"][tran] = ( - five_total - + cds_total - + three_total - + ambig_five_total - + ambig_cds_total - + ambig_three_total - ) - master_read_dict["ambiguous_fiveprime_totals"][tran] = ( - five_total + ambig_five_total - ) - master_read_dict["ambiguous_cds_totals"][tran] = cds_total + ambig_cds_total - master_read_dict["ambiguous_threeprime_totals"][tran] = ( - three_total + ambig_three_total - ) - print("Writing out to sqlite file") - sqlite_db = SqliteDict(outputfile, autocommit=False) - for key in master_read_dict: - sqlite_db[key] = master_read_dict[key] - sqlite_db["description"] = desc - sqlite_db.commit() - sqlite_db.close() - - -if __name__ == "__main__": - if len(sys.argv) <= 2: - print( - "Usage: python bam_to_sqlite.py <path_to_bam_file> <path_to_organism.sqlite> <file_description (optional)>" - ) - sys.exit() - bam_filepath = sys.argv[1] - annotation_sqlite_filepath = sys.argv[2] - try: - desc = sys.argv[3] - except: - desc = bam_filepath.split("/")[-1] - - outputfile = sys.argv[4] - process_bam(bam_filepath, annotation_sqlite_filepath, outputfile, desc)
--- a/trips_bam_to_sqlite/trips_bam_to_sqlite.xml Wed Apr 20 15:18:00 2022 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,37 +0,0 @@ -<tool id="bam_to_sqlite" name="BAM to Sqlite (TRIPS-Viz)" version="1.0"> - <description>Convert BAM file to SQLITE for TRIPS-Viz</description> - <requirements> - <requirement type="package" version="0.19.0">pysam</requirement> - <requirement type="package" version="1.7.0">sqlitedict</requirement> - <requirement type="package" version="3.37.1">sqlite</requirement> - </requirements> - <command><![CDATA[ - python $__tool_directory__/bam_to_sqlite.py $input1 $input2 $input3 $output1 - ]]></command> - <inputs> - <param name="input1" type="data" format="bam" label="Sorted (samtools -n) BAM file" /> - <param name="input2" type="data" format="sqlite" label="Path to organism SQLITE annotation file" /> - <param name="input3" type="text" label="Description of this sample" /> - </inputs> - <outputs> - <data name="output1" format="sqlite"/> - </outputs> - <tests> - <test> - <param name="input1" value="test_n_sorted.bam" ftype="bam"/> - <param name="input2" value="test_org.sqlite" ftype="sqlite"/> - <param name="input3" value="TEST DESCRIPTION"/> - <output name="output1" file="test_n_sorted.bamv2.sqlite" ftype="sqlite" lines_diff="4" /> - </test> - </tests> - <help> - **What it does** - - Process your transcriptome read alignments for TRIPS-Viz - - Prerequisites: - - name-sorted bam file (samtools sort -n) - - TRIPS-Viz annotation file in SQLITE format. - </help> - <citations/> -</tool>