--- a/trips_bam_to_sqlite/Dockerfile	Thu Mar 03 12:35:02 2022 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,13 +0,0 @@
-FROM ubuntu:20.04 
-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 apt-get update && apt-get install -y python3-pip
-RUN apt-get update
-RUN apt-get install python3.8
-RUN pip3 install pysam 
-RUN pip3 install sqlitedict
-RUN pip3 install pysqlite3

--- a/trips_bam_to_sqlite/bam_to_sqlite.py	Thu Mar 03 12:35:02 2022 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,580 +0,0 @@
-#!/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
-
-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:
-	bai_filepath_uns =  bam_filepath_uns.split('.bam')[0]+'.bai'
-	bai_filepath = bam_filepath_uns.split('.bam')[0]+'_sort.bai'
-	subprocess.call('touch %s' % bai_filepath_uns, shell=True)   
-	subprocess.call('touch %s' % bai_filepath, shell=True)    
-	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)
-

--- a/trips_bam_to_sqlite/trips_bam_to_sqlite.xml	Thu Mar 03 12:35:02 2022 +0000
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,36 +0,0 @@
-<tool id="trips_bam_to_sqlite" name="convert bam to sqlite format for upload to Trips-viz" version="1.1.0">
-    <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="res.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>