def mutate_member(exons, config, params, mut=True):
leaf_node = {}
total_mutations = 0
total_length = 0
log_string = ""
for ex_nr, seq in misc_functions.iteritems(exons):
if mut:
new_seq, mutation_log, exon_mutations, exon_indels, total_del_length = misc_functions.mutate_sequence(
seq, config["mut"], config["ins"], config["del"])
# print(new_seq, mutation_log, exon_mutations)
leaf_node[ex_nr] = new_seq
else:
new_seq = seq
leaf_node[ex_nr] = seq
mutation_log, exon_mutations = "-", 0
log_string += "mutations in exon: {0}, mutation places: {1}\n".format(
exon_mutations, mutation_log)
# params.logfile.write("mutations in exon: {0}, mutation places: {1}\n".format(exon_mutations, mutation_log))
total_mutations += exon_mutations
total_length += len(new_seq)
if total_mutations > 0 and mut:
params.logfile.write(log_string)
params.logfile.write("Total mutations: {0}\n".format(total_mutations))
params.logfile.write("mutation rate all exons: {0}\n".format(
total_mutations / float(total_length)))
return leaf_node
elif not mut:
params.logfile.write(log_string)
params.logfile.write("Total mutations: {0}\n".format(total_mutations))
params.logfile.write("mutation rate all exons: {0}\n".format(
total_mutations / float(total_length)))
return leaf_node
else:
print("NO mutations!")
return False
def main(params):
# config = misc_functions.read_config(params.config)
sequence_transcripts = {}
# for acc, seq in misc_functions.read_fasta(open(params.sequence_material, "r")):
# sequence_transcripts[acc] = seq
sequence_transcripts = dict(
misc_functions.read_fasta(open(params.sequence_material, "r")))
# print(sequence_transcripts)
# read lengths ~ according to P6-C4 chemistry histogram from here
# http://www.slideshare.net/GenomeInABottle/jan2016-pac-bio-giab slide 13
# this looks like it can be well approximated by triangiular distributions with parameters
# 0 (base start), 10000 (peak), ~45000 (base end)
# http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.triangular.html
# pacbios own distribution is here:
# http://www.pacb.com/blog/new-chemistry-boosts-average-read/
# read_lengths = np.random.triangular(0, 10000, 45000, config["read_count"])
# Get average quality based on subread length and the length of the transcript
# while read count is less than the red count we want:
# 1. Draw read length from distribution for each read length in simulated read lengths
# 2. Randomly select a transcript from pool
# 3. Get average quality based on the number of passes = floor(read_length/transcript_length)
# Avg quality is derived from this plot: https://speakerdeck.com/pacbio/specifics-of-smrt-sequencing-data
# slide 21, the P4 C2 chemistry, in case of one pass we chose 13 percent error rate from here : http://www.sciencedirect.com/science/article/pii/S1672022915001345.
# Out of the errors we follow the data here: http://bib.oxfordjournals.org/content/17/1/154.full.pdf
# and here http://www.homolog.us/Tutorials/index.php?p=2.8&s=2
# that indicates that for a pacbio genomic read, we have roughly 13-15 percent error rate (older chemistry)
# we choose 13. Out of the total errors, we let 11/16 = 68.75 be insertions
# 4/16= 25% be deletions and 1/16 = 6.25% be substitutions (given here http://www.homolog.us/Tutorials/index.php?p=2.8&s=2 and http://bib.oxfordjournals.org/content/17/1/154.full.pdf)
# 4. generate the read
quality_function = {
1: 0.87,
2: 0.95,
3: 0.957,
4: 0.969,
5: 0.981,
6: 0.985,
7: 0.99,
8: 0.992,
9: 0.994,
10: 0.995,
11: 0.995,
12: 0.995,
13: 0.996,
14: 0.996,
15: 0.996,
16: 0.999,
17: 0.999,
18: 0.999
} # passes : quality
read_count = 1
# just generate all numbers at once and draw from this 5x should be enough
it = 0
lengths = np.random.triangular(0, 10000, 45000, 5 * params.read_count)
pacbio_reads = {}
reads_generated_log = defaultdict(int)
errors = []
while read_count <= params.read_count:
if it >= len(lengths):
lengths = np.random.triangular(0, 10000, 45000,
5 * params.read_count)
it = 0
read_len = lengths[it]
acc = random.choice(list(sequence_transcripts.keys()))
transcript = sequence_transcripts[acc]
passes = int(read_len / len(transcript))
# print(passes, read_len, len(transcript))
if passes > 0:
if passes < 18:
quality = quality_function[passes]
else:
quality = 0.999
subs_rate = (1.0 - quality) * 0.0625
ins_rate = (1.0 - quality) * 0.6875
del_rate = (1.0 - quality) * 0.25
read, error_log, total_error_length, total_indel_length, total_del_length = misc_functions.mutate_sequence(
transcript, subs_rate, ins_rate, del_rate)
read_acc = "{0}_read_{1}_error_rate_{2}_total_errors_{3}".format(
acc, str(read_count),
total_error_length / float(len(read) + total_del_length),
total_error_length)
# params.logfile.write("{0}, error places: {1}\n".format(read_acc, error_log))
reads_generated_log[acc.split(":copy")[0]] += 1
errors.append(total_error_length)
pacbio_reads[read_acc] = read
read_count += 1
it += 1
for acc, abundance in misc_functions.iteritems(reads_generated_log):
params.logfile.write("{0}\t{1}\n".format(acc, abundance))
n = float(len(errors))
mu = sum(errors) / n
sigma = (sum(list(map(
(lambda x: x**2 - 2 * x * mu + mu**2), errors))) / (n - 1))**0.5
min_error = min(errors)
max_error = max(errors)
errors.sort()
if len(errors) % 2 == 0:
median_error = (errors[int(len(errors) / 2) - 1] +
errors[int(len(errors) / 2)]) / 2.0
else:
median_error = errors[int(len(errors) / 2)]
params.logfile.write(
"mean error: {0}, sd error:{1}, min_error:{2}, max_error:{3}, median_error:{4}\n"
.format(mu, sigma, min_error, max_error, median_error))
out_file = open(params.outfile, "w")
for acc, seq in misc_functions.iteritems(pacbio_reads):
out_file.write(">{0}\n{1}\n".format(acc, seq))