def main(recom):
number_of_slims = 0
reader = slim_reader_gzip
rec = pd.DataFrame([[1, -99]] + recom, columns=['pos', 'rate'])
# rec['cumulative'] = rec['rate']* ( rec['pos'] - rec['pos'].shift(1))
# freq_mean = 4000. * rec['cumulative'].sum() / rec['pos'].max()
interval = [[173800, 193200], [1, 428]]
for i in interval:
print recombination_rates(rec, i[0], i[1])
sys.exit()
for i in reader(sys.argv[1]):
x = slim(i)
check_point = sanity_checks(x)
if check_point == "insane":
continue
else:
pass
print x.recomb_intervals
recombination = pd.DataFrame([[1, -99]] + x.recomb_intervals,
columns=['pos', 'rate'])
recombination['cumulative'] = recombination['rate'] * (
recombination['pos'] - recombination['pos'].shift(1))
freq_mean = 4. * x.N * recombination['cumulative'].sum(
) / recombination['pos'].max()
break
def generate_seq_dicts(slim_in,
mat_dict,
number_of_sequences,
all_individuals=True,
singletons=False):
slim = tom_slim.slim(slim_in,
fixed=True,
give_genomes=True,
all_individuals=True)
mut_mat = mat_dict["mut_mat"]
mut_freqs = mat_dict["mut_freqs"]
reference = tuple(
[get_ref(mut_mat) for i in xrange(slim.length)]
) ## This defines the ancestral sequence as a string of random letters (ATCG) with the length of the SLiM chromosome
# return
print slim.name
#### LEFT IT HERE JANUARY 20th. Need to make a dict of the mutation frequencies so that the mutate function can do the same thing as the get_ref function, to choose the base to mutate to based upon the matrix
if singletons == True:
min_frequency = 0
elif singletons == False:
min_frequency = 1
mut_dict_raw = slim.mutations_dict(minFreq=min_frequency)
for i in mut_dict_raw:
print i, mut_dict_raw[i]
mut_dict = {}
for i in mut_dict_raw.keys():
pos = mut_dict_raw[i] - 1
ref_at_pos = reference[pos]
alt_at_pos = mutate(ref_at_pos, mut_freqs)
mut_dict[i] = [pos, alt_at_pos]
samples = slim.genome_dict()
genomes = samples
seqs = {}
individuals_chosen = []
while len(individuals_chosen) < number_of_sequences:
### Add a loop here to get multiple individuals
individual_to_choose = random.randint(1, (len(genomes.keys()) / 2))
if individual_to_choose in individuals_chosen: continue
else: individuals_chosen.append(individual_to_choose)
seq_1 = "p1:" + str(individual_to_choose * 2)
seq_2 = "p1:" + str(individual_to_choose * 2 - 1)
# brace()
for p in [seq_1, seq_2]:
seqs[p] = list(reference)
for allele in genomes[p]:
try:
mutation = mut_dict[int(allele)]
seqs[p][mutation[0]] = mutation[1]
except KeyError:
pass
return seqs, slim.name
def generate_seq_dicts(slim_in,
mat_dict,
number_of_sequences,
all_individuals=True):
slim = tom_slim.slim(slim_in,
fixed=False,
give_genomes=True,
all_individuals=True)
mut_mat = mat_dict["mut_mat"]
mut_freqs = mat_dict["mut_freqs"]
reference = tuple(
[get_ref(mut_mat) for i in xrange(slim.length)]
) ## This defines the ancestral sequence as a string of random letters (ATCG) with the length of the SLiM chromosome
# return
#### LEFT IT HERE JANUARY 20th. Need to make a dict of the mutation frequencies so that the mutate function can do the same thing as the get_ref function, to choose the base to mutate to based upon the matrix
mut_dict_raw = slim.mutations_dict(minFreq=1)
# for i in mut_dict_raw:
# print i, mut_dict_raw[i]
mut_dict = {}
for i in mut_dict_raw.keys():
pos = mut_dict_raw[i] - 1
ref_at_pos = reference[pos]
alt_at_pos = mutate(ref_at_pos, mut_freqs)
mut_dict[i] = [pos, ref_at_pos, alt_at_pos]
samples = slim.genome_dict()
#haps = {}
haps = collections.OrderedDict()
individuals_chosen = []
while len(individuals_chosen) < number_of_sequences:
### Add a loop here to get multiple individuals
individual_to_choose = random.randint(1, (len(samples.keys()) / 2))
if individual_to_choose in individuals_chosen: continue
else: individuals_chosen.append(individual_to_choose)
seq_1 = "p1:" + str(individual_to_choose * 2)
seq_2 = "p1:" + str(individual_to_choose * 2 - 1)
# brace()
for p in [seq_1, seq_2]:
haps[p] = samples[p]
return haps, slim.name, mut_dict
def get_sfs_dict_from_sample(slim_input):
data = [i.strip() for i in gzip.open(slim_input).readlines()]
x = ts.slim(data, fixed=True, give_genomes=True)
if not x.sanity:
return [None, None]
# print x.name
genomes = x.genome_dict()
mutations = x.mutations_dict()
lengthDict = parseLengths(x.organ_lengths())
individuals = [random.choice(genomes.keys()) for i in range(20)]
# print individuals
# if x.name == '/exports/csce/eddie/biology/groups/eddie_biology_ieb_keightley/toms_simulations/updated_DFE/longRuns/full_usfs/configs/3381.temp.slim':
# individuals = ['p1:1398', 'p1:1646', 'p1:297', 'p1:165', 'p1:999', 'p1:1451', 'p1:982', 'p1:973', 'p1:615', 'p1:832', 'p1:12', 'p1:1109', 'p1:1137', 'p1:496', 'p1:164', 'p1:412', 'p1:1687', 'p1:1373', 'p1:72', 'p1:39']
muts_by_organ = x.organ_mutations()
new_muts = Counter()
for g in individuals:
for m in genomes[g]:
new_muts[m] += 1
polyDict = {}
for h in muts_by_organ.keys():
mTypeDict = {}
for m in muts_by_organ[h]:
if new_muts[m[0]] == 0: continue
if m[1] not in mTypeDict.keys():
mTypeDict[m[1]] = [new_muts[m[0]]]
else:
mTypeDict[m[1]].append(new_muts[m[0]])
# print h, mTypeDict
mPoly = {}
for k in mTypeDict.keys():
mPoly[k] = sfs_tools.SFS_from_all_frequencies(mTypeDict[k], 20)
polyDict[h] = mPoly
# print '!', x.name
fixedDict = x.organ_fixed(threshold=int(x.N) * 20)
fixD = orgFixDict(fixedDict)
polyfix = combinePolyFix(polyDict, fixD)
elDict = combineElements(polyfix, lengthDict)
print 'processed ' + x.name
return [x.name, elDict]
def generate_seq_dicts(slim_in,mat_dict,number_of_sequences,all_individuals=True):
slim = tom_slim.slim(slim_in,fixed=False,give_genomes=True,all_individuals=True)
mut_mat = mat_dict["mut_mat"]
mut_freqs = mat_dict["mut_freqs"]
reference = tuple([get_ref(mut_mat) for i in xrange(slim.length)]) ## This defines the ancestral sequence as a string of random letters (ATCG) with the length of the SLiM chromosome
# return
print slim.name
#### LEFT IT HERE JANUARY 20th. Need to make a dict of the mutation frequencies so that the mutate function can do the same thing as the get_ref function, to choose the base to mutate to based upon the matrix
mut_dict_raw = slim.mutations_dict(minFreq=1)
# for i in mut_dict_raw:
# print i, mut_dict_raw[i]
mut_dict = {}
for i in mut_dict_raw.keys():
pos = mut_dict_raw[i]-1
ref_at_pos = reference[pos]
alt_at_pos = mutate(ref_at_pos,mut_freqs)
mut_dict[i] = [pos,alt_at_pos]
samples = slim.genome_dict()
genomes = samples
seqs = {}
individuals_chosen = []
while len(individuals_chosen) < number_of_sequences:
### Add a loop here to get multiple individuals
individual_to_choose = random.randint(1,(len(genomes.keys())/2))
if individual_to_choose in individuals_chosen: continue
else: individuals_chosen.append(individual_to_choose)
seq_1 = "p1:"+str(individual_to_choose *2)
seq_2 = "p1:"+str(individual_to_choose *2 -1)
# brace()
for p in [seq_1,seq_2]:
seqs[p] = list(reference)
for allele in genomes[p]:
try:
mutation = mut_dict[int(allele)]
seqs[p][mutation[0]] = mutation[1]
except KeyError:pass
return seqs,slim.name
def get_sfs_dict(slim_input, num=-1):
x = ts.slim(slim_input, fixed=True, give_genomes=True)
# print x.genomes
if not x.sanity:
return [None, None]
thresh = x.N * 10
polyDict = orgPolyDict(x.organ_mutations(), x.sampleN)
lengthDict = parseLengths(x.organ_lengths())
# print lengthDict
# print polyDict
fixedDict = x.organ_fixed(threshold=int(x.N) * 10)
fixD = orgFixDict(fixedDict)
polyfix = combinePolyFix(polyDict, fixD)
elDict = combineElements(polyfix, lengthDict)
print 'processed ' + x.name
return [x.name, elDict]
def SFS_by_organ(raw_slim):
x = tom_slim.slim(raw_slim)
org_dict = x.organ_mutations()
org_lengths = x.organ_lengths()
sfs_dict = {}
for j in org_dict.keys():
freq_sel = []
freq_neu = []
for mut in org_dict[j]:
if float(mut[3]) != 0.0:
freq_sel.append(int(mut[7]))
else:
freq_neu.append(int(mut[7]))
sel_sites = sel_prop_dict[j] * org_lengths[j]
neu_sites = (1.0-sel_prop_dict[j]) * org_lengths[j]
## Get the SFS for neutral and selected sites using the lengths
## of the different genomic elements
sel_sfs = SFS(freq_sel,sel_sites,x.sampleN)
neu_sfs = SFS(freq_neu,neu_sites,x.sampleN)
sfs_dict[j] = [neu_sfs,sel_sfs]
return [x.name, sfs_dict]
def get_sfs_dict_from_sample(slim_input):
x = ts.slim(slim_input, fixed=True, give_genomes=True)
if not x.sanity:
return [None, None]
genomes = x.genome_dict()
mutations = x.mutations_dict()
lengthDict = parseLengths(x.organ_lengths())
individuals = [random.choice(genomes.keys()) for i in range(20)]
muts_by_organ = x.organ_mutations()
new_muts = Counter()
for g in individuals:
for m in genomes[g]:
new_muts[m] += 1
polyDict = {}
for h in muts_by_organ.keys():
mTypeDict = {}
for m in muts_by_organ[h]:
if new_muts[m[0]] == 0: continue
if m[1] not in mTypeDict.keys():
mTypeDict[m[1]] = [new_muts[m[0]]]
else:
mTypeDict[m[1]].append(new_muts[m[0]])
# print h, mTypeDict
mPoly = {}
for k in mTypeDict.keys():
mPoly[k] = sfs_tools.SFS_from_all_frequencies(mTypeDict[k], 20)
polyDict[h] = mPoly
thresh = x.N * 10
fixedDict = x.organ_fixed(threshold=int(x.N) * 10)
fixD = orgFixDict(fixedDict)
polyfix = combinePolyFix(polyDict, fixD)
elDict = combineElements(polyfix, lengthDict)
print 'processed ' + x.name
return [x.name, elDict]
def get_both_stats(input_args):
index = input_args[0]
args = input_args[1]
boundary = args.boundary
window = args.window
file_name = args.input
output_raw = args.output
output = args.output + '.sfs'
### use class: slim instead... quite a bit faster
number = 0
test_number = 0
if args.gz:
reader = slim_reader_gzip
else:
reader = slim_reader
for i in reader(file_name):
number = number + 1
name = "non"
x = slim(i)
all_rates = pd.DataFrame(x.recomb_intervals, columns=['pos', 'rate'])
## This nex little snippet gets a dict of exon staring positions and the strand of those exons
fixed = False
check_point = sanity_checks(x)
if check_point == "insane":
continue
else:
pass
if args.orientation != 'No':
# tem = x.name.split('/')[-1]
# print args.orientation
for i in open(args.orientation):
head = i.strip('/')
if i.startswith('/'): break
# print head
element_look_up = pysam.Tabixfile(
"/home/booker/mouse_genome/all_elements/combined_elements/combined_elements_sorted.bed.gz"
)
els1 = [
m.strip().split() for m in element_look_up.fetch(
head.split(':')[0], int(head.split(':')[1].split('-')[0]),
int(head.split(':')[1].split('-')[1]))
]
els = [
ment for ment in els1
if not (int(ment[2]) -
int(ment[1]) == 1 and ment[3] == 'INTERGENIC')
]
strandDict = {}
for g, h in zip(els, x.organs):
# print g, h
if g[3] == 'CDS':
strandDict[h[1]] = g[4]
# print g,h, g[4]
name2 = x.name.split('/')[-1]
#print name2
individuals = x.sampleN
length = x.length
# print length
element_positions = []
temp = open(output, "w")
temp.close()
temp_out = open(output, "a")
### this bit here gets the selected site lists from
### the slim object and then gets all of the sites
### for all selected sites in the simulation
sites_dict = x.sites_dict() ## ALSO RETURN A KEY OF SITE TYPES?
for f in sites_dict["selected"]:
if f[0][0] != "g0":
element_positions += f[1]
if fixed:
non_element_subs = [
v for v in x.fixed if int(v[2]) not in element_positions
]
mutations_dict = {}
for key in [
b for b in x.mutations if int(b[2]) not in element_positions
]:
mutations_dict[int(key[2])] = key
position_keys = sorted(set(mutations_dict.keys()))
print("Processing file: " + str(number) + "\n\t" + args.element +
" make up " +
str(round(len(element_positions) * 100.0 / x.length, 2)) +
"% of the " + str(int(x.length / 1000)) +
"Kb simulated chromosome\n")
if boundary >= x.length:
distances_raw = range(1, x.length + window, window)
elif boundary < x.length:
distances_raw = range(1, boundary + window, window)
distances = distances_raw + [i * -1 for i in distances_raw]
exons = [g for g in x.organs if g[0] == args.element]
#print strandDict
#print exons
for point in exons:
#print point
lower_lim, upper_lim = get_analysis_limits(
exons, point,
x.length) # Find the limits for the analysis of this exon
for k in distances:
low_bound, up_bound = get_window_bounds(point, window, k)
if up_bound > upper_lim or low_bound < lower_lim:
continue
# print
mid_window = (up_bound + low_bound) / 2
if k < 0:
dist_start = mid_window
dist_end = int(point[1])
if k > 0:
dist_end = mid_window
dist_start = int(point[2])
if up_bound > x.length - args.threshold:
# print 'check 2'
continue
elif low_bound < 0 + args.threshold:
# print 'check 3' #, low_bound
continue
else:
frequencies = [
int(mutations_dict[j][7]) for j in position_keys if
j >= low_bound and j <= up_bound and up_bound < length
]
#
# frequencies = [int(hh[7]) for hh in muts_in_window] # replace muts_in_windows with a condensed
# if fixed:
# subs_in_window = [j for j in non_element_subs if int(j[2]) > low_bound and int(j[2]) < up_bound and up_bound < length]
# print subs_in_window
# elements_in_window = [p for p in element_positions if int(p) > low_bound and int(p) < up_bound]
bin_width = window - len([
p for p in element_positions
if int(p) >= low_bound and int(p) <= up_bound
])
if bin_width == 0 or bin_width < 0:
continue
else:
sfs_window = SFS_from_frequencies(
frequencies, bin_width, individuals)
window_name = str(
abs(k)) + '-' + str(abs(k) + window - 1)
r_dist, p_dist = getDistance(dist_start, dist_end,
all_rates)
# print r_dist * x.N*4. , p_dist
if args.orientation != 'No':
try:
strand = strandDict[point[1]]
except KeyError:
print x.name
return
if strand == '+':
if k < 0:
y = "u." + window_name
elif k > 0:
y = "d." + window_name
elif strand == '-':
if k > 0:
y = "u." + window_name
elif k < 0:
y = "d." + window_name
else:
if k < 0:
y = "u." + window_name
elif k > 0:
y = "d." + window_name
temp_out.write(
str([y, r_dist * x.N * 4., p_dist, sfs_window]) +
"\n")
temp_out.close()