--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/trips_bam_to_sqlite/Dockerfile	Wed Mar 09 15:17:02 2022 +0000
@@ -0,0 +1,20 @@
+FROM alpine 
+WORKDIR /tmp
+COPY bam_to_sqlite.py . 
+RUN chmod +x bam_to_sqlite.py
+RUN ln bam_to_sqlite.py /usr/local/bin/bam_to_sqlite
+RUN export PATH="$PATH:/usr/local/bin"
+ENV PATH="/usr/local/bin:${PATH}"
+RUN apk add bash
+RUN apk add build-base
+RUN apk add python3
+RUN apk add py3-pip
+RUN apk add gcc g++ make libffi-dev openssl-dev
+RUN apk add jpeg-dev zlib-dev
+ENV LIBRARY_PATH=/lib:/usr/lib
+RUN apk add libbz2 bzip2-dev xz-dev
+RUN apk add python3-dev
+RUN pip install --upgrade pip 
+RUN pip install --no-cache-dir 'pysqlite3==0.4.6'
+RUN pip install --no-cache-dir 'pysam==0.16.0.1'
+RUN pip install --no-cache-dir 'sqlitedict==1.7.0'

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/trips_bam_to_sqlite/bam_to_sqlite.py	Wed Mar 09 15:17:02 2022 +0000
@@ -0,0 +1,582 @@
+#!/usr/bin/env python3
+
+import sys
+import pysam
+import operator
+import os
+import time
+import sqlite3
+from sqlitedict import SqliteDict
+import subprocess
+from subprocess import call
+import pickle
+
+print(pickle.format_version)
+
+
+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 = 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.keys())[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 = "NULL"
+	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 transcriptome_info_dict.keys()[: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":{}}
+
+ 
+	infile = pysam.Samfile(bam_filepath, "rb")
+	header = infile.header["HD"]; print (header)
+	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, 'SO in header', header['SO'], 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.keys())[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.keys())[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 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
+						# 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 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)
+
+
+						# 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
+
+	#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
+			final_offsets[primetype]["read_scores"][readlen] = master_trip_dict[primetype][readlen]["score"]
+
+	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_uns = sys.argv[1] # name for unsorted file 
+	bam_filepath = bam_filepath_uns.split('.bam')[0]+'_sort.bam'# name for temp sorted by name file would be:
+	pysam.sort("-n", "-o", bam_filepath, bam_filepath_uns)
+	#subprocess.call('samtools sort -n %s > %s' % (bam_filepath_uns, bam_filepath), shell=True)
+	print ('bam_filepath', bam_filepath)
+	infile_test = pysam.Samfile(bam_filepath, "rb")
+	header_test = infile_test.header["HD"]; print ('before process bam:', header_test)
+	annotation_sqlite_filepath = sys.argv[2]
+	#try:
+	#	desc = sys.argv[3]
+	#except:
+	#	desc = bam_filepath.split("/")[-1]
+	outputfile = sys.argv[3]
+	process_bam(bam_filepath,annotation_sqlite_filepath,outputfile)
+	print ('pickle protocol that was used for generating sqlite file')   
+	print("highest protocol: ", pickle.HIGHEST_PROTOCOL)
+

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/trips_bam_to_sqlite/trips_bam_to_sqlite.xml	Wed Mar 09 15:17:02 2022 +0000
@@ -0,0 +1,36 @@
+<tool id="trips_bam_to_sqlite" name="convert bam to sqlite format for upload to Trips-viz" version="2.0">
+    <requirements>
+         <container type="docker">triasteran/bam_to_sqlite:latest</container>
+    </requirements>
+    <command detect_errors="exit_code"><![CDATA[
+        bam_to_sqlite $bamfile $org_sqlite $output
+    ]]></command>
+    <inputs>
+        <param format="unsorted.bam" name="bamfile" type="data" label="BAM file"/>
+        <param format="sqlite" name="org_sqlite" type="data" label="Trips-viz organism file"/>
+    </inputs>
+    <outputs>
+        <data format="sqlite" name="output" />
+    </outputs>
+    <tests>
+        <test>
+            <param name="bamfile" value="yeast_test.bam"/>
+            <param name="org_sqlite" value="organism.sqlite"/>
+            <output name="output" value="resX.sqlite"/>
+        </test>
+    </tests>
+    <help><![CDATA[
+        input: .bam, output: .sqlite. 
+    ]]></help>
+    <citations>
+        <citation type="bibtex">
+@misc{githubTrips-Viz,
+  author = {LastTODO, FirstTODO},
+  year = {TODO},
+  title = {Trips-Viz},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  url = {https://github.com/skiniry/Trips-Viz},
+}</citation>
+    </citations>
+</tool>