in_folder, o_file, gff_file = command_line()
# Open GFF file and make dictionary containing gene names & their positions
# Then add the per-library gene read counts to a dictionary
gff_genes_dict = GFF.Parse(gff_file, create_nuc_dict=True)
library_gene_counts = {}
for (gene, [chromosome, spos, epos]) in gff_genes_dict:
if not gff_genes_dict.is_transposon(
gene) and not gff_genes_dict.is_common_rna(gene):
library_gene_counts[gene] = []
for root, subfolders, files in os.walk(in_folder):
for f_name in files:
print("Working on", str(f_name))
f_in = os.path.join(root, f_name)
parse_sam = SAM.Parse(f_in)
parse_sam.reads_per_gene(gff_genes_dict)
parse_sam.start()
temp_gene_counts = parse_sam.get_reads_per_gene()
# Count up the total number of reads in the library
gzipped = False
total_reads = 0
if f_in.endswith(".gz"):
infile = gzip.open(f_in, 'rb')
gzipped = True
else:
infile = open(f_in)
for line in infile:
if gzipped == True: line = line.decode('utf-8')
line = line.split()
return(in_folder, o_file, gff_file)
in_folder, o_file, gff_file = command_line()
# Open GFF file and make dictionary containing gene names & their positions
# Then add the per-library gene read counts to a dictionary
gff_genes_dict = GFF.Parse_genes(gff_file, create_nuc_dict=True)
library_gene_counts = {}
for (gene, [chromosome, spos, epos]) in gff_genes_dict:
library_gene_counts[gene] = []
for root, subfolders, files in os.walk(in_folder):
for f_name in files:
print("Working on", str(f_name))
f_in = os.path.join(root,f_name)
temp_gene_counts = SAM.reads_per_gene(f_in, gff_genes_dict)
# Count up the total number of reads in the library
gzipped = False
total_reads = 0
if f_in.endswith(".gz"):
infile = gzip.open(f_in, 'rb')
gzipped = True
else:
infile = open(f_in)
for line in infile:
if gzipped == True: line = str(line, encoding='utf8')
line = line.split()
if line[0][:1] != "@" and int(line[4]) >= 20: total_reads += 1
# Change read counts to RPKM value
def simulate_reads(ref, chr, readlen, readcov, dupnum, duplen, sam, chr_len):
final_SAM_fns = [] # List containing all SAM files to be combined together. First contains regular read simulation SAM file, with regional duplication SAM files added later
chr_list = []
pattern = "Chr\d{1,}"
recomp = re.compile(pattern)
if chr == "All":
for chr_temp in chr_len:
match = recomp.match(chr_temp)
if match:
chr_list.append(chr_temp)
else:
chr_list = [entry for entry in chr.split(',')]
for chr in chr_list:
for i in range(0,len(chr_len[chr]),10000):
out_fn = str(sam[:-3]) + "_temp_" + str(chr) + "_" + str(i)
new_fasta = ">" + str(chr) + "\n" + chr_len[chr][i:i+10000]
temp_fasta = "regtemp_" + str(i) + ".fasta"
with open(temp_fasta, 'w') as outfile:
outfile.write(new_fasta)
params = ' '.join(["art_illumina.exe", "-i", str(temp_fasta), "-l", str(readlen), "-f", str(readcov), "-o", out_fn, "-sam", "-q"])
#Example usage: art_illumina.exe -i ../at9_chr3.fasta -l 34 -f 2 -o Output/RandomAtReads -sam
simulation = subprocess.Popen(params)
simulation.wait()
new_out_fn = out_fn + ".sam"
if i > 0:
reg_SAM = SAM.parse(new_out_fn, True)
for j in range(0,len(reg_SAM.sam_list)):
reg_SAM.sam_list[j][3] = str(int(reg_SAM.sam_list[j][3]) + i) # Changing SAM file read numbers
reg_SAM.output(new_out_fn)
os.remove(temp_fasta)
os.remove(out_fn + ".fq")
os.remove(out_fn + ".aln")
final_SAM_fns.append(new_out_fn) # Regular read profile files assigned here
prev_sel = []
for i in range(1,dupnum+1):
spos = 0
undupped_region = False
ok_regions = 0
while undupped_region == False:
chr = ''.join(sample(chr_list,1))
spos = randrange(0,len(chr_len[chr]))
epos = spos + duplen
if epos > len(chr_len[chr]): continue
for entry in prev_sel:
(c, start, end) = entry
if chr == c:
if not (int(spos) >= int(start) and int(spos) <= int(end)) or (int(epos) >= int(start) and int(epos) <= int(end)):
# Testing to make sure that newly selected duplicated region is not within a region already selected to be duplicated
ok_regions += 1
else:
break
else:
ok_regions += 1
if ok_regions == len(prev_sel):
undupped_region = True
prev_sel.append((chr,spos,epos))
new_fasta = ">" + str(chr) + "\n" + chr_len[chr][spos:epos]
temp_fasta = "duptemp_" + str(i) + ".fasta"
with open(temp_fasta, 'w') as outfile:
outfile.write(new_fasta)
params = ' '.join(["art_illumina.exe", "-i", str(temp_fasta), "-l", str(readlen), "-f", str(readcov), "-o", str(temp_fasta[:-6]), "-sam", "-q"])
simulation = subprocess.Popen(params)
simulation.wait()
dup_fn = temp_fasta[:-6] + ".sam"
dup_SAM = SAM.parse(dup_fn, True)
for i in range(0,len(dup_SAM.sam_list)):
dup_SAM.sam_list[i][3] = str(int(dup_SAM.sam_list[i][3]) + spos)
dup_SAM.output(dup_fn[:-4] + "_temp.sam")
os.remove(temp_fasta)
os.remove(temp_fasta[:-6] + ".fq")
os.remove(temp_fasta[:-6] + ".aln")
os.remove(dup_fn)
final_SAM_fns.append(dup_fn[:-4] + "_temp.sam")
final_SAM = SAM.parse(final_SAM_fns, True)
pattern2 = r"Chr\d{1}-(\d+)"
recomp2 = re.compile(pattern2)
max_read = final_SAM.sam_list[0][0]
match = recomp2.match(max_read)
max_num = int(match.group(1))
min_met = False
for i in range(0, len(final_SAM.sam_list)): # Ensure that read names from one SAM file do not coincide with read names from another SAM file
r_name = final_SAM.sam_list[i][0]
match = recomp2.match(r_name)
if match:
r_num = int(match.group(1))
if r_num == 1 and min_met == False:
min_met = True
continue
if min_met == True:
max_num += 1
line = final_SAM.sam_list[i]
line[0] = r_name[:5] + str(max_num)
final_SAM.sam_list[i] = line
final_SAM.header = sorted(final_SAM.header, key = lambda read: read[1][6:])
final_SAM.sam_list = sorted(final_SAM.sam_list, key = lambda read: int(read[0][5:]))
for i in range(0,len(final_SAM.header)): # Set chromosome length in header portion of SAM file to correct length (will be 10,000 in temporary simulation SAM files)
chr = final_SAM.header[i][1][3:]
final_SAM.header[i][2] = "LN:" + str(len(chr_len[chr]))
final_SAM.output(sam[:-3])
for file in final_SAM_fns:
os.remove(file)
params = ' '.join([str(pypath) + " FixARTSAMFile.py", "-i", sam[:-3], "-o", sam])
fixSAM = subprocess.Popen(params) # Replaces new CIGAR string format for matches, which uses = and X, to the old format M for both
fixSAM.wait()
print("Duplicated regions are located at:\n")
for entry in sorted(prev_sel, key = lambda entry:(entry[0], entry[1])):
(chr, spos, epos) = entry
print(str(chr), ":", str(spos),"-", str(epos), sep="")
#parser.add_argument('-o', default="Data/RNA-Seq/sue1-mRNA-Seq/set3/Thalyrata/sue_mRNA_set3_sw_aln_gene_count.tsv", type=str, help='Output file for gene-mapped read counts', metavar='OutputFile')
#parser.add_argument('-o', default="Data/RNA-Seq/Col_RNA-Seq/Thalyrata/SRR493036_aln_gene_count.tsv", type=str, help='Output file for gene-mapped read counts', metavar='OutputFile')
#parser.add_argument('-o', default="/home/mattchat/SuecicaDupSearch/Data/RNA-Seq/Col0_leaf_RNA-seq/libWhan_01nc_gene_count.tsv", type=str, help='Output file for gene-mapped read counts', metavar='OutputFile')
#parser.add_argument('-o', default="/home/mattchat/SuecicaDupSearch/Data/RNA-Seq/Col0_leaf_RNA-seq/libWhan_02nc_gene_count.tsv", type=str, help='Output file for gene-mapped read counts', metavar='OutputFile')
#parser.add_argument('-o', default="/home/mattchat/SuecicaDupSearch/Data/RNA-Seq/Col0_leaf_RNA-seq/libWhan_03nc_gene_count.tsv", type=str, help='Output file for gene-mapped read counts', metavar='OutputFile')
#parser.add_argument('-o', default="/home/mattchat/SuecicaDupSearch/Data/RNA-Seq/Col0_leaf_RNA-seq/libWhan_04nc_gene_count.tsv", type=str, help='Output file for gene-mapped read counts', metavar='OutputFile')
#parser.add_argument('-o', default="/home/mattchat/SuecicaDupSearch/Data/RNA-Seq/Col0_leaf_RNA-seq/libWhan_05nc_gene_count.tsv", type=str, help='Output file for gene-mapped read counts', metavar='OutputFile')
#parser.add_argument('-o', default="/home/mattchat/SuecicaDupSearch/Data/RNA-Seq/Col0_leaf_RNA-seq/libWhan_06nc_gene_count.tsv", type=str, help='Output file for gene-mapped read counts', metavar='OutputFile')
#parser.add_argument('-o', default="/home/mattchat/SuecicaDupSearch/Data/RNA-Seq/Col0_leaf_RNA-seq/libWhan_07nc_gene_count.tsv", type=str, help='Output file for gene-mapped read counts', metavar='OutputFile')
#parser.add_argument('-o', default="/home/mattchat/SuecicaDupSearch/Data/RNA-Seq/Col0_leaf_RNA-seq/libWhan_08nc_gene_count.tsv", type=str, help='Output file for gene-mapped read counts', metavar='OutputFile')
#parser.add_argument('-o', default="/home/mattchat/SuecicaDupSearch/Data/RNA-Seq/Col0_leaf_RNA-seq/libWhan_09nc_gene_count.tsv", type=str, help='Output file for gene-mapped read counts', metavar='OutputFile')
parser.add_argument('-o', default="/home/mattchat/SuecicaDupSearch/Data/RNA-Seq/Col0_leaf_RNA-seq/libWhan_10nc_gene_count.tsv", type=str, help='Output file for gene-mapped read counts', metavar='OutputFile')
args = parser.parse_args()
i_file = args.i
gff_file = args.gff
o_file = args.o
return(i_file, gff_file, o_file)
i_file, gff_file, o_file = command_line()
#gff_obj = GFF.Parse_Genes(gff_file, "AtChr1;AtChr2;AtChr3;AtChr4;AtChr5")
gff_obj = GFF.parse_genes(gff_file, "Chr1;Chr2;Chr3;Chr4;Chr5")
gene_counts = SAM.reads_per_gene(i_file, gff_obj)
with open(o_file, 'w') as outfile:
outline = "Gene\tReads_mapped\n"
outfile.write(outline)
for gene, count in sorted(gene_counts.items(), key = lambda gene_counts: gene_counts[1], reverse=True):
outline = str(gene) + "\t" + str(count) + "\n"
outfile.write(outline)
exp_file, column_num, sam_file, dup_file, gff_file, loop_count = command_line()
# Open GFF file and make dictionary containing gene names & their positions
gff_genes_dict = GFF.Parse_genes(gff_file, create_nuc_dict=True)
# Store A. suecica list of DupHMM duplicated genes
dup_list = []
with open(dup_file) as infile:
for line in infile:
line = line.strip()
dup_list.append(line)
dup_list_len = len(dup_list)
# Get total number of reads w/ quality >= 20
total_reads = SAM.total_reads(sam_file)
# Store A. suecica gene counts
gene_count_dict = Counter()
with open(exp_file) as infile:
for line in infile:
line = line.split()
gene_name = line[0]
if gene_name != "Gene":
gene_counts = int(line[column_num - 1])
(chromosome, s_pos, e_pos) = gff_genes_dict.loc(gene)
dist_kb = (e_pos - s_pos) / 1000
RPK = count / dist_kb
RPKM = RPK / (total_reads / 1000000)
gene_count_dict[gene_name] = gene_counts
'-o',
default=
"/home/mattchat/SuecicaDupSearch/Data/RNA-Seq/Col0_leaf_RNA-seq/libWhan_10nc_gene_count.tsv",
type=str,
help='Output file for gene-mapped read counts',
metavar='OutputFile')
args = parser.parse_args()
i_file = args.i
gff_file = args.gff
o_file = args.o
return (i_file, gff_file, o_file)
i_file, gff_file, o_file = command_line()
#gff_obj = GFF.Parse_Genes(gff_file, "AtChr1;AtChr2;AtChr3;AtChr4;AtChr5")
gff_obj = GFF.parse_genes(gff_file, "Chr1;Chr2;Chr3;Chr4;Chr5")
parse_sam = SAM.Parse(i_file)
parse_sam.reads_per_gene(gff_obj)
parse_sam.start()
gene_counts = parse_sam.get_reads_per_gene()
with open(o_file, 'w') as outfile:
outline = "Gene\tReads_mapped\n"
outfile.write(outline)
for gene, count in sorted(gene_counts.items(),
key=lambda gene_counts: gene_counts[1],
reverse=True):
outline = str(gene) + "\t" + str(count) + "\n"
outfile.write(outline)
