# Analysis of the libraries HJJ7JBCX2_ZIKV

Here we plot the major base frequency for different time points. We also detect positions that consistently rise in frequency through time, i.e. positions that rise monotonously in frequency with time. Positions that only rise in frequency between the first and the last time points have not been investigated.

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In [1]:

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
from pylab import rcParams
import seaborn as sns
from array import array
import numpy as np
from scipy.stats import ttest_ind
from scipy.stats import linregress
from scipy.stats import mannwhitneyu
%matplotlib inline

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# Obtaining the sequence annotation

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In [2]:

begins=[]
ends=[]
names =[]
with open ("sequence.gb") as f:
in_pep = False
for l in f:
if "mat_peptide" in l:
begins.append(int(l.split()[1].split("..")[0]))
ends.append(int(l.split()[1].split("..")[1]))
in_pep = True
elif in_pep :
names.append(l.split("=")[1])
in_pep = False

print(begins)
print(ends)
print(names)

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[108, 474, 753, 978, 2490, 3546, 4224, 4614, 6465, 6846, 6915, 7668]
[473, 752, 977, 2489, 3545, 4223, 4613, 6464, 6845, 6914, 7667, 10376]
['"capsid"\n', '"propeptide"\n', '"membrane"\n', '"envelope"\n', '"NS1"\n', '"NS2A"\n', '"NS2B"\n', '"NS3"\n', '"NS4A"\n', '"2K"\n', '"NS4B"\n', '"NS5"\n']

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# Functions to plot interesting positions and gene boundaries

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In [3]:

# Interesting positions
positions=[316,1670,1785,2340,5935,7172,8449,9165]
def plot_positions():
for x in positions:
plt.axvline(x=x, linewidth=1, linestyle=':')

def plot_genes():
for i in range(len(begins)):
plt.plot([begins[i], begins[i]], [0.99,1.0], linewidth=2, linestyle='-', color="black")
if i%2==0:
plt.text (begins[i] + ((ends[i] - begins[i])/10), 1.005, (names[i].replace('"', ''))[0:3], size='xx-small')
else:
plt.text (begins[i] + ((ends[i] - begins[i])/10), 1.015, (names[i].replace('"', ''))[0:3], size='xx-small')
plt.plot([ends[-1], ends[-1]], [0.99,1.0], linewidth=2, linestyle='-', color="black")

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# Function to add useful columns to the tables

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In [4]:

def synonymous (row):
if row['null'] or (row['Consensus_aa']==row['Secondbase_aa'] ):
return "synonymous"
else:
return "non-synonymous"

table['null'] = (table['Secondbase_aa']).isnull()
table['is_synonymous'] = table.apply (lambda row: synonymous (row),axis=1)
table['1_major_variant_frequency'] = 1.0 - table['Major_variant_frequency_quality_corrected']

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# Functions to detect variants increasing in frequency

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In [5]:

def is_increasing(minor_frequencies):
#print(minor_frequencies)
previous = minor_frequencies[0]
for m in range(1,len(minor_frequencies)):
if previous < minor_frequencies[m]:
#print(str(previous) + " < " + str(minor_frequencies[m]))
previous = minor_frequencies[m]
else:
return False
return True

def get_variant_frequency(variant, table, i):
sum_of_bases = table['As_quality_corrected'][i]+table['Cs_quality_corrected'][i]+table['Gs_quality_corrected'][i]+table['Ts_quality_corrected'][i]+table['Ns_quality_corrected'][i]
if variant == "A":
return table["As_quality_corrected"][i] / sum_of_bases
elif variant == "C":
return table["Cs_quality_corrected"][i] / sum_of_bases
elif variant == "G":
return table["Gs_quality_corrected"][i] / sum_of_bases
elif variant == "T":
return table["Ts_quality_corrected"][i] / sum_of_bases
else:
return np.nan

def get_increasing_variants(tables):
num_tables = len(tables)
first = tables[0]
last = tables[num_tables-1]
major = ""
minor = ""
major_frequencies = array('d',[0.0]*num_tables)
minor_frequencies = array('d',[0.0]*num_tables)
increasingVariants = dict()
for i in first["Position"]:
major = first["Major_variant"][i]
#print(last['Major_variant_frequency_quality_corrected'][i])
major_frequencies[0] = first['Major_variant_frequency_quality_corrected'][i]
if major == last["Major_variant"][i]:
minor = last["Second_variant"][i]
else:
minor = last["Major_variant"][i]
minor_frequencies[0] = get_variant_frequency(minor, first, i)
for table_id in range(1, num_tables):
major_frequencies[table_id] = get_variant_frequency(major, tables[table_id], i)
minor_frequencies[table_id] = get_variant_frequency(minor, tables[table_id], i)
if is_increasing(minor_frequencies):
increasingVariants[i] = [major_frequencies.tolist(), minor_frequencies.tolist()]
return increasingVariants

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In [6]:

# Clone at days 12, 15, 19
clone12 = pd.read_csv ("HJJ7JBCX2_ZIKV-s-and-c_18s004258-1-1_DREUX_lane1cloneD12_1_sequence.txt.assembled.fastq_mapped_AA.csv", na_values=" -nan")
clone15 = pd.read_csv ("HJJ7JBCX2_ZIKV-s-and-c_18s004258-1-1_DREUX_lane1cloneD15_1_sequence.txt.assembled.fastq_mapped_AA.csv", na_values=" -nan")
clone19 = pd.read_csv ("HJJ7JBCX2_ZIKV-s-and-c_18s004258-1-1_DREUX_lane1cloneD19_1_sequence.txt.assembled.fastq_mapped_AA.csv", na_values=" -nan")

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In [7]:

# Control runs, replicate A
DD18_A = pd.read_csv ("HJJ7JBCX2_ZIKV-s-and-c_18s004258-1-1_DREUX_lane1DD18A_1_sequence.txt.assembled.fastq_mapped_AA.csv", na_values=" -nan")

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In [8]:

# Control runs, replicate D

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In [9]:

# Control runs, replicate E

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In [10]:

# TLR3 activation runs, replicate A

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In [11]:

# TLR3 activation runs, replicate D

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In [12]:

# TLR3 activation runs, replicate E

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# Analysis of the clone samples

### Coverage

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In [13]:

variable = 'Coverage'
sample = len(clone12 [variable])*["Clone 12"]+len(clone15 [variable])*["Clone 15"]+len(clone19 [variable])*["Clone 19"]

overlay_table_concat = pd.DataFrame ({'Position':pd.concat([clone12['Position'],clone15['Position'],clone19['Position']]), variable:pd.concat([clone12 [variable],clone15 [variable], clone19 [variable] ]), 'sample':sample})

sns.lmplot( x="Position", y=variable, data=overlay_table_concat, fit_reg=False, hue='sample', legend=False, size=7, aspect=2, lowess=True,scatter_kws={"s": 20})
plt.legend(loc='lower right')
plot_positions()
plot_genes()

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Basically, we find the same areas of low and high coverage.

### Analysis of the frequency of the major variant

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In [14]:

variable = 'Major_variant_frequency'
sample = len(clone12 [variable])*["Clone 12"]+len(clone15 [variable])*["Clone 15"]+len(clone19 [variable])*["Clone 19"]

overlay_table_concat = pd.DataFrame ({'Position':pd.concat([clone12['Position'],clone15['Position'],clone19['Position']]), variable:pd.concat([clone12 [variable],clone15 [variable], clone19 [variable] ]), 'sample':sample})

sns.lmplot( x="Position", y=variable, data=overlay_table_concat, fit_reg=False, hue='sample', legend=False, size=7, aspect=2, lowess=True,scatter_kws={"s": 20})
plt.legend(loc='lower right')
plot_positions()
plot_genes()

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### Variants that increase in frequency

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In [15]:

tables_clone = [clone12, clone15, clone19]
increasing_clone = get_increasing_variants(tables_clone)
print("There are "+str(len(increasing_clone))+" positions that rise in frequency.")
print("Those are:")
print(increasing_clone.keys())

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There are 4300 positions that rise in frequency.
Those are:
dict_keys([27, 29, 34, 35, 36, 43, 49, 53, 56, 57, 59, 61, 65, 66, 68, 70, 72, 73, 78, 80, 88, 97, 101, 112, 115, 118, 120, 129, 131, 133, 155, 159, 161, 167, 169, 170, 171, 172, 173, 175, 178, 184, 192, 193, 195, 200, 201, 203, 206, 207, 211, 214, 218, 222, 224, 225, 229, 232, 234, 243, 245, 246, 249, 252, 253, 257, 262, 265, 267, 269, 275, 276, 282, 283, 293, 302, 303, 320, 325, 335, 344, 349, 356, 358, 368, 374, 375, 376, 377, 379, 380, 382, 388, 391, 392, 395, 396, 397, 401, 405, 406, 410, 419, 420, 421, 422, 424, 425, 426, 427, 429, 430, 435, 442, 444, 445, 446, 448, 449, 450, 451, 453, 454, 456, 459, 460, 462, 463, 465, 467, 470, 471, 476, 477, 479, 480, 481, 484, 485, 487, 488, 489, 493, 494, 496, 498, 501, 502, 503, 509, 514, 517, 518, 520, 522, 523, 524, 526, 527, 528, 529, 533, 534, 536, 537, 539, 543, 544, 548, 549, 550, 553, 554, 563, 568, 572, 575, 581, 586, 588, 592, 593, 595, 597, 600, 601, 602, 612, 613, 615, 616, 618, 619, 622, 628, 630, 632, 634, 635, 636, 639, 643, 650, 653, 656, 657, 661, 662, 664, 665, 666, 667, 671, 676, 679, 681, 686, 690, 693, 694, 699, 702, 710, 712, 715, 716, 718, 719, 722, 723, 727, 729, 731, 735, 737, 740, 743, 745, 747, 748, 749, 752, 753, 756, 759, 763, 766, 767, 769, 771, 774, 775, 776, 777, 778, 780, 782, 784, 785, 788, 789, 791, 792, 793, 794, 795, 798, 806, 812, 814, 815, 816, 820, 823, 826, 827, 828, 831, 835, 840, 843, 847, 853, 854, 858, 859, 861, 862, 868, 870, 874, 875, 876, 877, 881, 887, 888, 891, 892, 893, 894, 895, 898, 900, 902, 903, 905, 915, 916, 918, 920, 925, 926, 929, 931, 932, 934, 936, 937, 938, 942, 944, 946, 948, 950, 952, 953, 955, 957, 963, 964, 966, 968, 970, 973, 974, 978, 979, 980, 983, 985, 992, 995, 996, 1004, 1006, 1008, 1013, 1033, 1044, 1047, 1048, 1051, 1054, 1060, 1061, 1064, 1067, 1068, 1069, 1072, 1074, 1077, 1078, 1079, 1080, 1081, 1082, 1085, 1090, 1093, 1094, 1098, 1099, 1100, 1101, 1102, 1103, 1105, 1106, 1109, 1110, 1115, 1116, 1119, 1120, 1122, 1123, 1126, 1127, 1129, 1130, 1131, 1132, 1134, 1139, 1142, 1147, 1148, 1149, 1150, 1151, 1153, 1154, 1155, 1157, 1159, 1160, 1162, 1163, 1164, 1166, 1167, 1171, 1172, 1176, 1177, 1178, 1179, 1182, 1183, 1184, 1185, 1188, 1189, 1190, 1191, 1192, 1193, 1196, 1197, 1198, 1199, 1200, 1203, 1204, 1205, 1206, 1209, 1210, 1213, 1215, 1216, 1217, 1219, 1220, 1224, 1225, 1229, 1230, 1233, 1236, 1237, 1239, 1241, 1247, 1248, 1249, 1251, 1252, 1253, 1254, 1256, 1259, 1260, 1264, 1266, 1269, 1270, 1271, 1274, 1275, 1276, 1282, 1284, 1285, 1287, 1288, 1290, 1291, 1294, 1295, 1296, 1299, 1303, 1305, 1308, 1310, 1315, 1316, 1320, 1321, 1323, 1324, 1326, 1327, 1328, 1330, 1335, 1338, 1339, 1344, 1348, 1352, 1353, 1363, 1365, 1366, 1367, 1371, 1376, 1379, 1383, 1384, 1385, 1386, 1387, 1388, 1391, 1392, 1395, 1397, 1401, 1402, 1405, 1406, 1407, 1408, 1413, 1414, 1416, 1418, 1419, 1420, 1422, 1427, 1430, 1431, 1432, 1437, 1441, 1442, 1447, 1448, 1449, 1450, 1455, 1458, 1462, 1463, 1464, 1466, 1467, 1487, 1489, 1494, 1495, 1497, 1500, 1501, 1503, 1504, 1512, 1514, 1515, 1523, 1524, 1528, 1530, 1531, 1537, 1538, 1540, 1542, 1545, 1546, 1550, 1553, 1554, 1556, 1557, 1560, 1561, 1562, 1563, 1564, 1567, 1568, 1570, 1572, 1576, 1581, 1583, 1585, 1589, 1592, 1593, 1600, 1604, 1605, 1606, 1607, 1608, 1609, 1610, 1614, 1615, 1616, 1617, 1618, 1629, 1630, 1631, 1636, 1640, 1646, 1648, 1650, 1652, 1654, 1662, 1665, 1667, 1668, 1669, 1670, 1674, 1675, 1676, 1679, 1681, 1687, 1689, 1690, 1692, 1694, 1695, 1696, 1698, 1700, 1707, 1708, 1710, 1711, 1719, 1720, 1721, 1722, 1723, 1725, 1726, 1727, 1728, 1730, 1732, 1733, 1734, 1741, 1744, 1749, 1750, 1757, 1761, 1762, 1767, 1770, 1771, 1772, 1775, 1776, 1779, 1780, 1781, 1782, 1785, 1786, 1788, 1790, 1791, 1792, 1793, 1794, 1803, 1805, 1806, 1808, 1811, 1812, 1813, 1814, 1815, 1816, 1817, 1821, 1822, 1823, 1824, 1826, 1829, 1830, 1831, 1832, 1838, 1839, 1841, 1843, 1847, 1849, 1850, 1852, 1853, 1854, 1859, 1860, 1867, 1868, 1869, 1871, 1875, 1876, 1881, 1882, 1887, 1889, 1890, 1891, 1892, 1894, 1895, 1896, 1899, 1902, 1905, 1908, 1909, 1910, 1911, 1912, 1913, 1918, 1920, 1921, 1925, 1926, 1927, 1928, 1929, 1931, 1932, 1936, 1938, 1942, 1944, 1945, 1949, 1950, 1951, 1954, 1956, 1957, 1958, 1959, 1961, 1962, 1967, 1971, 1972, 1973, 1974, 1975, 1977, 1978, 1980, 1983, 1984, 1988, 1989, 1995, 2000, 2001, 2003, 2005, 2006, 2007, 2009, 2013, 2014, 2015, 2016, 2019, 2024, 2029, 2030, 2033, 2034, 2036, 2038, 2040, 2041, 2045, 2047, 2055, 2056, 2058, 2059, 2062, 2063, 2064, 2065, 2069, 2071, 2073, 2074, 2077, 2080, 2081, 2087, 2089, 2091, 2092, 2094, 2095, 2099, 2101, 2102, 2103, 2108, 2109, 2110, 2115, 2116, 2118, 2119, 2120, 2121, 2122, 2126, 2128, 2129, 2130, 2132, 2134, 2142, 2146, 2148, 2153, 2154, 2164, 2167, 2168, 2169, 2171, 2172, 2173, 2177, 2178, 2179, 2183, 2184, 2185, 2186, 2189, 2191, 2192, 2194, 2202, 2208, 2211, 2215, 2217, 2220, 2224, 2226, 2227, 2230, 2231, 2244, 2249, 2250, 2256, 2259, 2265, 2273, 2274, 2276, 2280, 2281, 2284, 2291, 2292, 2301, 2302, 2303, 2306, 2307, 2308, 2311, 2315, 2318, 2319, 2320, 2321, 2328, 2330, 2339, 2345, 2360, 2364, 2365, 2366, 2367, 2369, 2370, 2371, 2377, 2389, 2399, 2402, 2405, 2415, 2417, 2419, 2421, 2422, 2424, 2431, 2436, 2438, 2440, 2441, 2446, 2447, 2448, 2456, 2458, 2459, 2460, 2461, 2463, 2464, 2467, 2468, 2470, 2472, 2476, 2477, 2478, 2479, 2481, 2487, 2489, 2491, 2497, 2499, 2502, 2505, 2509, 2511, 2514, 2515, 2516, 2518, 2524, 2526, 2530, 2533, 2536, 2538, 2544, 2545, 2551, 2555, 2557, 2558, 2560, 2562, 2565, 2566, 2568, 2569, 2572, 2576, 2578, 2581, 2584, 2587, 2589, 2590, 2591, 2593, 2594, 2595, 2596, 2599, 2602, 2603, 2604, 2606, 2607, 2608, 2612, 2613, 2615, 2616, 2622, 2624, 2627, 2628, 2630, 2634, 2635, 2638, 2639, 2647, 2651, 2653, 2658, 2659, 2661, 2662, 2664, 2668, 2669, 2670, 2671, 2682, 2683, 2688, 2695, 2696, 2702, 2703, 2708, 2709, 2711, 2713, 2718, 2719, 2722, 2723, 2726, 2727, 2732, 2741, 2743, 2744, 2748, 2751, 2752, 2759, 2761, 2762, 2764, 2767, 2769, 2771, 2773, 2774, 2775, 2776, 2777, 2780, 2782, 2789, 2796, 2798, 2799, 2801, 2802, 2803, 2807, 2808, 2815, 2816, 2819, 2822, 2823, 2824, 2827, 2830, 2845, 2847, 2850, 2851, 2856, 2858, 2860, 2863, 2864, 2865, 2866, 2868, 2870, 2874, 2875, 2878, 2880, 2881, 2884, 2887, 2888, 2903, 2904, 2906, 2907, 2910, 2911, 2917, 2923, 2925, 2928, 2934, 2940, 2947, 2951, 2952, 2955, 2960, 2964, 2966, 2968, 2973, 2980, 2983, 2984, 2985, 2989, 2991, 3000, 3001, 3005, 3007, 3014, 3015, 3019, 3021, 3030, 3034, 3036, 3037, 3044, 3045, 3047, 3052, 3054, 3059, 3060, 3062, 3066, 3070, 3074, 3085, 3092, 3118, 3122, 3125, 3130, 3131, 3134, 3135, 3142, 3148, 3153, 3162, 3166, 3171, 3174, 3178, 3189, 3190, 3193, 3200, 3201, 3203, 3207, 3209, 3212, 3215, 3218, 3221, 3236, 3237, 3238, 3239, 3242, 3243, 3244, 3245, 3249, 3250, 3256, 3257, 3259, 3265, 3268, 3273, 3274, 3275, 3276, 3279, 3281, 3283, 3286, 3287, 3288, 3289, 3290, 3293, 3295, 3305, 3306, 3308, 3310, 3313, 3314, 3318, 3324, 3326, 3328, 3330, 3336, 3342, 3343, 3344, 3360, 3362, 3366, 3374, 3376, 3377, 3378, 3380, 3382, 3384, 3386, 3387, 3388, 3393, 3394, 3395, 3396, 3400, 3401, 3403, 3408, 3409, 3411, 3412, 3413, 3414, 3424, 3432, 3435, 3436, 3438, 3442, 3443, 3446, 3447, 3449, 3451, 3453, 3456, 3458, 3462, 3466, 3470, 3472, 3474, 3476, 3478, 3479, 3480, 3483, 3487, 3490, 3492, 3496, 3498, 3505, 3507, 3509, 3510, 3513, 3515, 3517, 3520, 3521, 3524, 3528, 3530, 3531, 3537, 3538, 3539, 3540, 3541, 3542, 3547, 3548, 3549, 3551, 3555, 3556, 3557, 3558, 3564, 3566, 3570, 3572, 3573, 3574, 3575, 3576, 3577, 3578, 3581, 3584, 3585, 3586, 3587, 3590, 3591, 3596, 3598, 3599, 3605, 3610, 3612, 3614, 3617, 3618, 3620, 3622, 3624, 3625, 3626, 3627, 3630, 3633, 3634, 3640, 3644, 3645, 3649, 3651, 3660, 3661, 3663, 3665, 3669, 3673, 3675, 3678, 3679, 3680, 3685, 3692, 3693, 3695, 3700, 3702, 3703, 3704, 3708, 3710, 3711, 3713, 3714, 3717, 3720, 3722, 3724, 3725, 3729, 3732, 3736, 3737, 3738, 3739, 3740, 3741, 3743, 3745, 3751, 3752, 3753, 3754, 3755, 3756, 3760, 3761, 3762, 3763, 3766, 3767, 3768, 3769, 3770, 3771, 3772, 3775, 3777, 3778, 3784, 3786, 3787, 3789, 3791, 3793, 3796, 3797, 3800, 3804, 3805, 3806, 3807, 3812, 3816, 3818, 3819, 3823, 3824, 3828, 3829, 3832, 3833, 3834, 3841, 3844, 3846, 3847, 3848, 3849, 3852, 3856, 3858, 3859, 3860, 3862, 3863, 3865, 3873, 3874, 3876, 3880, 3881, 3882, 3883, 3884, 3887, 3891, 3893, 3897, 3898, 3900, 3901, 3903, 3904, 3906, 3909, 3911, 3913, 3914, 3917, 3918, 3919, 3920, 3925, 3926, 3928, 3931, 3936, 3939, 3940, 3942, 3945, 3948, 3949, 3950, 3952, 3957, 3959, 3961, 3962, 3964, 3974, 3975, 3976, 3977, 3978, 3979, 3982, 3984, 3985, 3991, 3992, 3994, 3995, 3997, 3999, 4000, 4002, 4005, 4006, 4007, 4011, 4012, 4014, 4015, 4016, 4019, 4020, 4022, 4024, 4025, 4027, 4029, 4031, 4035, 4036, 4038, 4040, 4042, 4045, 4050, 4053, 4063, 4064, 4066, 4068, 4069, 4071, 4074, 4075, 4077, 4082, 4088, 4090, 4091, 4092, 4093, 4094, 4095, 4096, 4097, 4098, 4100, 4101, 4105, 4107, 4109, 4110, 4111, 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9813, 9814, 9818, 9819, 9820, 9821, 9822, 9834, 9835, 9836, 9837, 9838, 9839, 9841, 9843, 9844, 9847, 9849, 9851, 9852, 9856, 9858, 9859, 9861, 9862, 9865, 9866, 9868, 9869, 9870, 9872, 9874, 9876, 9877, 9878, 9879, 9881, 9883, 9886, 9887, 9888, 9889, 9890, 9892, 9893, 9894, 9895, 9896, 9897, 9899, 9900, 9901, 9902, 9905, 9907, 9909, 9911, 9915, 9916, 9917, 9921, 9923, 9924, 9925, 9927, 9928, 9930, 9933, 9936, 9941, 9942, 9945, 9947, 9949, 9950, 9953, 9954, 9955, 9957, 9959, 9961, 9962, 9963, 9964, 9965, 9967, 9970, 9974, 9976, 9977, 9978, 9979, 9982, 9988, 9991, 9992, 9993, 9996, 9997, 10000, 10004, 10005, 10006, 10012, 10014, 10017, 10020, 10022, 10023, 10024, 10030, 10032, 10038, 10039, 10040, 10041, 10046, 10050, 10052, 10053, 10057, 10058, 10059, 10063, 10064, 10065, 10069, 10078, 10082, 10086, 10087, 10089, 10092, 10095, 10096, 10098, 10099, 10100, 10109, 10115, 10119, 10120, 10121, 10123, 10124, 10125, 10131, 10138, 10139, 10141, 10142, 10143, 10144, 10145, 10150, 10156, 10157, 10158, 10164, 10187, 10189, 10192, 10204, 10208, 10211, 10213, 10215, 10216, 10218, 10220, 10221, 10222, 10223, 10224, 10225, 10230, 10235, 10236, 10239, 10240, 10242, 10244, 10252, 10254, 10256, 10258, 10261, 10262, 10263, 10269, 10270, 10272, 10274, 10276, 10278, 10279, 10281, 10283, 10298, 10300, 10303, 10305, 10308, 10309, 10313, 10318, 10322, 10323, 10325, 10326, 10327, 10328, 10330, 10336, 10339, 10341, 10342, 10346, 10347, 10348, 10349, 10351, 10353, 10356, 10357, 10359, 10360, 10363, 10364, 10365, 10366, 10367, 10374, 10377, 10382, 10384, 10386, 10387, 10388, 10389, 10390, 10391, 10392, 10397, 10398, 10402, 10405, 10411, 10414, 10415, 10420, 10425, 10426, 10427, 10428, 10429, 10430, 10431, 10433, 10435, 10436, 10437, 10439, 10440, 10445, 10446, 10447, 10452, 10461, 10475, 10478, 10479, 10480, 10481, 10482, 10483, 10484, 10485, 10486, 10487, 10490, 10491, 10492, 10493, 10494, 10495, 10496, 10497, 10498, 10499, 10500, 10501, 10502, 10503, 10510, 10512, 10513, 10515, 10517, 10520, 10521, 10523, 10524, 10529, 10531, 10532, 10533, 10535, 10536, 10538, 10539, 10541, 10542, 10544, 10547, 10548, 10550, 10551, 10553, 10554, 10555, 10556, 10558, 10559, 10560, 10562, 10563, 10564, 10566, 10568, 10569, 10571, 10572, 10573, 10574, 10575, 10577, 10581, 10583, 10588, 10590, 10591, 10594, 10595, 10598, 10599, 10600, 10601, 10603, 10604, 10605, 10606, 10607, 10608, 10609, 10611, 10612, 10613, 10615, 10616, 10617, 10618, 10619, 10620, 10621, 10622, 10623, 10628, 10629, 10631, 10635, 10637, 10639, 10644, 10646, 10647, 10649, 10650, 10651, 10652, 10653, 10654, 10655, 10657, 10658, 10659, 10660, 10661, 10662, 10663, 10664, 10668, 10669, 10679, 10680, 10681, 10682, 10685, 10689, 10690, 10692, 10693, 10694, 10701, 10702, 10706, 10707, 10708, 10710, 10713, 10716, 10717, 10718, 10719, 10721, 10724, 10728, 10744, 10747, 10758, 10761, 10767])

/home/boussau/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:20: RuntimeWarning: invalid value encountered in double_scalars
/home/boussau/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:22: RuntimeWarning: invalid value encountered in double_scalars
/home/boussau/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:18: RuntimeWarning: invalid value encountered in double_scalars

``````

# Analysis of control runs

### Coverage

``````

In [16]:

variable = 'Coverage'
sample = len(DD18_A [variable])*["DD18_A"]+len(DD18_D [variable])*["DD18_D"]+len(DD3_E [variable])*["DD3_E"]+len(DD12_E [variable])*["DD12_E"]+len(DD18_E [variable])*["DD18_E"]+len(DD24_E [variable])*["DD24_E"]+len(DD24crude_E [variable])*["DD24crude_E"]

overlay_table_concat = pd.DataFrame ({'Position':pd.concat([DD18_A['Position'],DD18_D['Position'],DD3_E['Position'],DD12_E['Position'],DD18_E['Position'],DD24_E['Position'],DD24crude_E['Position']]), variable:pd.concat([DD18_A [variable], DD18_D [variable], DD3_E [variable], DD12_E [variable], DD18_E [variable], DD24_E [variable], DD24crude_E [variable] ]), 'sample':sample})

sns.lmplot( x="Position", y=variable, data=overlay_table_concat, fit_reg=False, hue='sample', legend=False, size=7, aspect=2, lowess=True,scatter_kws={"s": 20})
plt.legend(loc='lower right')
plot_positions()
plot_genes()

``````
``````

``````

### Analysis of the frequency of the major variant

``````

In [17]:

variable = 'Major_variant_frequency'
sample = len(DD18_A [variable])*["DD18_A"]+len(DD18_D [variable])*["DD18_D"]+len(DD3_E [variable])*["DD3_E"]+len(DD12_E [variable])*["DD12_E"]+len(DD18_E [variable])*["DD18_E"]+len(DD24_E [variable])*["DD24_E"]+len(DD24crude_E [variable])*["DD24crude_E"]

overlay_table_concat = pd.DataFrame ({'Position':pd.concat([DD18_A['Position'],DD18_D['Position'],DD3_E['Position'],DD12_E['Position'],DD18_E['Position'],DD24_E['Position'],DD24crude_E['Position']]), variable:pd.concat([DD18_A [variable], DD18_D [variable], DD3_E [variable], DD12_E [variable], DD18_E [variable], DD24_E [variable], DD24crude_E [variable] ]), 'sample':sample})

sns.lmplot( x="Position", y=variable, data=overlay_table_concat, fit_reg=False, hue='sample', legend=False, size=7, aspect=2, lowess=True,scatter_kws={"s": 20})
plt.legend(loc='lower right')
plot_positions()
plot_genes()

``````
``````

``````

### Focusing on DD*_E

``````

In [18]:

variable = 'Major_variant_frequency'
sample = len(DD3_E [variable])*["DD3_E"]+len(DD12_E [variable])*["DD12_E"]+len(DD18_E [variable])*["DD18_E"]+len(DD24_E [variable])*["DD24_E"]+len(DD24crude_E [variable])*["DD24crude_E"]

overlay_table_concat = pd.DataFrame ({'Position':pd.concat([DD3_E['Position'],DD12_E['Position'],DD18_E['Position'],DD24_E['Position'],DD24crude_E['Position']]), variable:pd.concat([DD3_E [variable], DD12_E [variable], DD18_E [variable], DD24_E [variable], DD24crude_E [variable] ]), 'sample':sample})

sns.lmplot( x="Position", y=variable, data=overlay_table_concat, fit_reg=False, hue='sample', legend=False, size=7, aspect=2, lowess=True,scatter_kws={"s": 20})
plt.legend(loc='lower right')
plot_positions()
plot_genes()

``````
``````

``````

### Variants that increase in frequency in DD*_E

``````

In [19]:

tables_control = [DD3_E, DD12_E, DD18_E, DD24_E]
increasing_control = get_increasing_variants(tables_control)
print("There are "+str(len(increasing_control))+" positions that rise in frequency.")
print("Those are:")
print(increasing_control.keys())

``````
``````

There are 468 positions that rise in frequency.
Those are:
dict_keys([31, 43, 47, 53, 72, 74, 98, 100, 109, 187, 188, 200, 209, 216, 235, 240, 250, 270, 281, 302, 304, 320, 334, 367, 376, 384, 396, 430, 433, 499, 503, 518, 533, 534, 550, 561, 626, 643, 646, 647, 685, 696, 701, 709, 746, 801, 816, 820, 831, 855, 874, 878, 899, 903, 919, 965, 969, 1020, 1131, 1194, 1201, 1208, 1267, 1279, 1300, 1305, 1329, 1332, 1341, 1359, 1409, 1424, 1439, 1455, 1460, 1466, 1512, 1525, 1546, 1602, 1660, 1667, 1670, 1707, 1717, 1744, 1751, 1763, 1783, 1822, 1844, 1963, 1964, 1967, 1969, 1986, 2032, 2037, 2046, 2052, 2108, 2110, 2137, 2141, 2145, 2151, 2153, 2155, 2156, 2193, 2307, 2332, 2406, 2412, 2450, 2482, 2493, 2521, 2565, 2597, 2663, 2672, 2674, 2679, 2692, 2694, 2726, 2769, 2782, 2797, 2814, 2865, 2893, 2911, 2912, 2944, 2952, 2974, 3013, 3051, 3061, 3116, 3136, 3187, 3209, 3212, 3224, 3227, 3273, 3307, 3322, 3347, 3390, 3394, 3405, 3431, 3446, 3462, 3521, 3566, 3590, 3607, 3655, 3681, 3689, 3695, 3724, 3744, 3787, 3828, 3864, 3895, 3965, 4000, 4011, 4018, 4032, 4077, 4078, 4102, 4214, 4244, 4271, 4276, 4286, 4289, 4336, 4351, 4354, 4358, 4370, 4393, 4397, 4405, 4416, 4504, 4568, 4657, 4659, 4680, 4728, 4733, 4793, 4812, 4820, 4878, 4881, 4945, 4946, 4951, 4984, 4992, 5007, 5008, 5010, 5015, 5034, 5035, 5075, 5081, 5084, 5086, 5114, 5116, 5131, 5147, 5171, 5190, 5205, 5218, 5235, 5276, 5312, 5319, 5326, 5328, 5330, 5341, 5351, 5374, 5416, 5450, 5454, 5467, 5565, 5582, 5611, 5615, 5628, 5635, 5672, 5680, 5716, 5728, 5753, 5814, 5850, 5954, 5982, 5992, 6035, 6112, 6125, 6137, 6263, 6299, 6396, 6428, 6438, 6455, 6538, 6561, 6568, 6617, 6625, 6646, 6659, 6729, 6742, 6763, 6777, 6793, 6803, 6822, 6874, 6880, 6931, 6981, 7018, 7035, 7037, 7064, 7072, 7078, 7139, 7151, 7152, 7172, 7181, 7207, 7218, 7277, 7305, 7314, 7345, 7348, 7360, 7367, 7375, 7379, 7382, 7400, 7403, 7482, 7484, 7489, 7490, 7491, 7509, 7597, 7603, 7654, 7667, 7668, 7669, 7670, 7674, 7679, 7691, 7755, 7816, 7842, 7844, 7856, 7871, 7936, 7945, 8005, 8040, 8051, 8068, 8105, 8130, 8131, 8165, 8170, 8171, 8184, 8206, 8243, 8268, 8320, 8337, 8374, 8424, 8517, 8557, 8608, 8612, 8738, 8762, 8849, 8958, 8985, 9002, 9045, 9059, 9064, 9065, 9068, 9076, 9091, 9097, 9105, 9107, 9114, 9115, 9122, 9123, 9181, 9199, 9242, 9249, 9268, 9290, 9302, 9347, 9351, 9354, 9391, 9414, 9416, 9424, 9428, 9449, 9469, 9470, 9477, 9520, 9553, 9558, 9590, 9598, 9627, 9642, 9645, 9659, 9675, 9692, 9720, 9721, 9724, 9771, 9780, 9806, 9807, 9829, 9965, 9994, 10032, 10042, 10045, 10047, 10074, 10101, 10122, 10240, 10275, 10289, 10298, 10300, 10303, 10316, 10333, 10351, 10353, 10381, 10394, 10398, 10399, 10424, 10439, 10450, 10463, 10484, 10490, 10499, 10504, 10514, 10516, 10537, 10546, 10552, 10567, 10576, 10618, 10619, 10625, 10630, 10652, 10663, 10705, 10718, 10719, 10749, 10756, 10770, 10776])

``````

## Analysis of TLR3 treated runs

### Coverage

``````

In [20]:

variable = 'Coverage'
sample = len(TD18_A [variable])*["TD18_A"]+len(TD9_D [variable])*["TD9_D"]+len(TD12_D [variable])*["TD12_D"]+len(TD18_D [variable])*["TD18_D"]+len(TD24_D [variable])*["TD24_D"]+len(TD9_E [variable])*["TD9_E"]+len(TD12_E [variable])*["TD12_E"]+len(TD18_E [variable])*["TD18_E"]+len(TD24_E [variable])*["TD24_E"]

overlay_table_concat = pd.DataFrame ({'Position':pd.concat([TD18_A['Position'],TD9_D['Position'],TD12_D['Position'],TD18_D['Position'],TD24_D['Position'],TD9_E['Position'],TD12_E['Position'],TD18_E['Position'],TD24_E['Position']]), variable:pd.concat([TD18_A[variable],TD9_D[variable],TD12_D[variable],TD18_D[variable],TD24_D[variable],TD9_E[variable],TD12_E[variable],TD18_E[variable],TD24_E[variable] ]), 'sample':sample})

sns.lmplot( x="Position", y=variable, data=overlay_table_concat, fit_reg=False, hue='sample', legend=False, size=7, aspect=2, lowess=True,scatter_kws={"s": 20})
plt.legend(loc='lower right')
plot_positions()
plot_genes()

``````
``````

``````