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1 =head1 LICENSE
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2
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3 Copyright [1999-2015] Wellcome Trust Sanger Institute and the EMBL-European Bioinformatics Institute
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4 Copyright [2016-2018] EMBL-European Bioinformatics Institute
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5
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6 Licensed under the Apache License, Version 2.0 (the "License");
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7 you may not use this file except in compliance with the License.
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8 You may obtain a copy of the License at
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9
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10 http://www.apache.org/licenses/LICENSE-2.0
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11
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12 Unless required by applicable law or agreed to in writing, software
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13 distributed under the License is distributed on an "AS IS" BASIS,
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14 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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15 See the License for the specific language governing permissions and
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16 limitations under the License.
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17
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18 =head1 CONTACT
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19
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20 Ensembl <http://www.ensembl.org/info/about/contact/index.html>
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21
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22 =cut
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23
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24 =head1 NAME
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25
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26 Carol
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27
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28 =head1 SYNOPSIS
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29
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30 mv Carol.pm ~/.vep/Plugins
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31 ./vep -i variations.vcf --plugin Carol
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32
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33 =head1 DESCRIPTION
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34
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35 This is a plugin for the Ensembl Variant Effect Predictor (VEP) that calculates
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36 the Combined Annotation scoRing toOL (CAROL) score (1) for a missense mutation
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37 based on the pre-calculated SIFT (2) and PolyPhen-2 (3) scores from the Ensembl
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38 API (4). It adds one new entry class to the VEP's Extra column, CAROL which is
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39 the calculated CAROL score. Note that this module is a perl reimplementation of
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40 the original R script, available at:
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41
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42 http://www.sanger.ac.uk/resources/software/carol/
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43
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44 I believe that both versions implement the same algorithm, but if there are any
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45 discrepancies the R version should be treated as the reference implementation.
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46 Bug reports are welcome.
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47
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48 References:
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49
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50 (1) Lopes MC, Joyce C, Ritchie GRS, John SL, Cunningham F, Asimit J, Zeggini E.
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51 A combined functional annotation score for non-synonymous variants
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52 Human Heredity (in press)
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53
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54 (2) Kumar P, Henikoff S, Ng PC.
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55 Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm
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56 Nature Protocols 4(8):1073-1081 (2009)
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57 doi:10.1038/nprot.2009.86
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58
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59 (3) Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR.
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60 A method and server for predicting damaging missense mutations
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61 Nature Methods 7(4):248-249 (2010)
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62 doi:10.1038/nmeth0410-248
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63
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64 (4) Flicek P, et al.
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65 Ensembl 2012
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66 Nucleic Acids Research (2011)
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67 doi: 10.1093/nar/gkr991
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68
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69 =cut
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70
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71 package Carol;
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72
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73 use strict;
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74 use warnings;
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75
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76 use Math::CDF qw(pnorm qnorm);
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77
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78 use base qw(Bio::EnsEMBL::Variation::Utils::BaseVepPlugin);
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79
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80 my $CAROL_CUTOFF = 0.98;
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81
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82 sub version {
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83 return '2.3';
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84 }
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85
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86 sub feature_types {
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87 return ['Transcript'];
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88 }
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89
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90 sub get_header_info {
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91 return {
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92 CAROL => "Combined Annotation scoRing toOL prediction",
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93 };
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94 }
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95
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96 sub run {
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97 my ($self, $tva) = @_;
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98
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99 my $pph_pred = $tva->polyphen_prediction;
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100 my $pph_score = $pph_pred ? ($pph_pred eq 'unknown' ? undef: $tva->polyphen_score) : undef;
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101 my $sift_score = $tva->sift_score;
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102
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103 my ($carol_pred, $carol_score) = compute_carol($pph_score, $sift_score);
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104
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105 my $results = {};
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106
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107 if (defined $carol_pred) {
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108
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109 $carol_score = sprintf "%.3f", $carol_score;
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110
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111 my $result = "$carol_pred($carol_score)";
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112
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113 if (@{ $self->params } > 0) {
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114 $result = $carol_pred if ($self->params->[0] =~ /^p/i);
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115 $result = $carol_score if ($self->params->[0] =~ /^s/i);
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116 }
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117
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118 $results = {
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119 CAROL => $result,
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120 };
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121 }
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122
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123 return $results;
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124 }
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125
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126 sub compute_carol {
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127
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128 my ($pph_score, $sift_score) = @_;
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129
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130 my $carol_score;
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131
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132 if (defined $pph_score) {
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133 $pph_score = 0.999 if $pph_score == 1;
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134 $pph_score = 0.0001 if $pph_score == 0;
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135 }
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136
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137 if (defined $sift_score) {
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138 $sift_score = 1 - $sift_score;
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139 $sift_score = 0.999 if $sift_score == 1;
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140 $sift_score = 0.0001 if $sift_score == 0;
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141 }
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142
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143 if (defined $pph_score && defined $sift_score) {
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144
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145 my $pph_weight = log(1/(1-$pph_score));
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146 my $sift_weight = log(1/(1-$sift_score));
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147
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148 # we take -qnorm, because the R script uses qnorm(..., lower.tail = FALSE)
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149
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150 my $pph_z = -qnorm($pph_score);
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151 my $sift_z = -qnorm($sift_score);
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152
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153 my $numerator = ($pph_weight * $pph_z) + ($sift_weight * $sift_z);
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154 my $denominator = sqrt( ($pph_weight ** 2) + ($sift_weight ** 2) );
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155
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156 # likewise we take 1 - pnorm
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157
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158 $carol_score = 1 - pnorm($numerator / $denominator);
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159 }
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160 elsif (defined $pph_score) {
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161 $carol_score = $pph_score;
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162 }
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163 else {
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164 $carol_score = $sift_score;
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165 }
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166
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167 if (defined $carol_score) {
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168 my $carol_pred = $carol_score < $CAROL_CUTOFF ? 'Neutral' : 'Deleterious';
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169 return ($carol_pred, $carol_score);
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170 }
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171 else {
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172 return undef;
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173 }
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174 }
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175
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176 1;
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177
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