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annotate pairend_distro.py @ 13:02adb61c0246 draft default tip

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planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/lumpy commit 8ea053586c663715db915668ebe676a461c23e85
author drosofff
date Wed, 18 Jan 2017 10:02:01 -0500
parents 8b3daa745d9b
children
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8b3daa745d9b planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/lumpy commit c0bfc4b2215705e1b5fd1d4e60b1d72e5da13c92
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1 #!/usr/bin/env python
8b3daa745d9b planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/lumpy commit c0bfc4b2215705e1b5fd1d4e60b1d72e5da13c92
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2 # (c) 2012 - Ryan M. Layer
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3 # Hall Laboratory
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4 # Quinlan Laboratory
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5 # Department of Computer Science
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6 # Department of Biochemistry and Molecular Genetics
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7 # Department of Public Health Sciences and Center for Public Health Genomics,
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8 # University of Virginia
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9 # rl6sf@virginia.edu
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10
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11 import sys
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12 import numpy as np
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13 from operator import itemgetter
8b3daa745d9b planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/lumpy commit c0bfc4b2215705e1b5fd1d4e60b1d72e5da13c92
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14 from optparse import OptionParser
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16 # some constants for sam/bam field ids
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17 SAM_FLAG = 1
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18 SAM_REFNAME = 2
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19 SAM_MATE_REFNAME = 6
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20 SAM_ISIZE = 8
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22 parser = OptionParser()
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23
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24 parser.add_option("-r",
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25 "--read_length",
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26 type="int",
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27 dest="read_length",
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28 help="Read length")
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29
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30 parser.add_option("-X",
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31 dest="X",
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32 type="int",
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33 help="Number of stdevs from mean to extend")
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34
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35 parser.add_option("-N",
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36 dest="N",
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37 type="int",
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38 help="Number to sample")
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39
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40 parser.add_option("-o",
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41 dest="output_file",
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42 help="Output file")
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43
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44 parser.add_option("-m",
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45 dest="mads",
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46 type="int",
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47 default=10,
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48 help="Outlier cutoff in # of median absolute deviations (unscaled, upper only)")
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49
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50 def unscaled_upper_mad(xs):
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51 """Return a tuple consisting of the median of xs followed by the
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52 unscaled median absolute deviation of the values in xs that lie
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53 above the median.
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54 """
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55 med = np.median(xs)
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56 return med, np.median(xs[xs > med] - med)
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57
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58
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59 (options, args) = parser.parse_args()
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60
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61 if not options.read_length:
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62 parser.error('Read length not given')
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63
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64 if not options.X:
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65 parser.error('X not given')
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66
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67 if not options.N:
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68 parser.error('N not given')
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69
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70 if not options.output_file:
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71 parser.error('Output file not given')
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72
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73
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74 required = 97
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75 restricted = 3484
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76 flag_mask = required | restricted
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77
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78 L = []
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79 c = 0
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80
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81 for l in sys.stdin:
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82 if c >= options.N:
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83 break
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84
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85 A = l.rstrip().split('\t')
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86 flag = int(A[SAM_FLAG])
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87 refname = A[SAM_REFNAME]
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88 mate_refname = A[SAM_MATE_REFNAME]
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89 isize = int(A[SAM_ISIZE])
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90
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91 want = mate_refname == "=" and flag & flag_mask == required and isize >= 0
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92 if want:
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93 c += 1
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94 L.append(isize)
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95
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96 # warn if very few elements in distribution
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97 min_elements = 1000
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98 if len(L) < min_elements:
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99 sys.stderr.write("Warning: only %s elements in distribution (min: %s)\n" % (len(L), min_elements))
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100 mean = "NA"
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101 stdev = "NA"
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102
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103 else:
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104 # Remove outliers
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105 L = np.array(L)
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106 L.sort()
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107 med, umad = unscaled_upper_mad(L)
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108 upper_cutoff = med + options.mads * umad
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109 L = L[L < upper_cutoff]
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110 new_len = len(L)
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111 removed = c - new_len
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112 sys.stderr.write("Removed %d outliers with isize >= %d\n" %
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113 (removed, upper_cutoff))
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114 c = new_len
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115
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116 mean = np.mean(L)
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117 stdev = np.std(L)
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118
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119 start = options.read_length
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120 end = int(mean + options.X*stdev)
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121
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122 H = [0] * (end - start + 1)
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123 s = 0
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124
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125 for x in L:
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126 if (x >= start) and (x <= end):
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127 j = int(x - start)
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128 H[j] = H[ int(x - start) ] + 1
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129 s += 1
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130
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131 f = open(options.output_file, 'w')
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132
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133 for i in range(end - start):
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134 o = str(i) + "\t" + str(float(H[i])/float(s)) + "\n"
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135 f.write(o)
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136
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137
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138 f.close()
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139
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140 print('mean:' + str(mean) + '\tstdev:' + str(stdev))