/usr/share/perl5/Bio/Chado/Schema/Result/Companalysis/Analysisfeature.pm is in libbio-chado-schema-perl 0.20000-1.
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The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 | package Bio::Chado::Schema::Result::Companalysis::Analysisfeature;
BEGIN {
$Bio::Chado::Schema::Result::Companalysis::Analysisfeature::AUTHORITY = 'cpan:RBUELS';
}
{
$Bio::Chado::Schema::Result::Companalysis::Analysisfeature::VERSION = '0.20000';
}
# Created by DBIx::Class::Schema::Loader
# DO NOT MODIFY THE FIRST PART OF THIS FILE
use strict;
use warnings;
use base 'DBIx::Class::Core';
=head1 NAME
Bio::Chado::Schema::Result::Companalysis::Analysisfeature
=head1 DESCRIPTION
Computational analyses generate features (e.g. Genscan generates transcripts and exons; sim4 alignments generate similarity/match features). analysisfeatures are stored using the feature table from the sequence module. The analysisfeature table is used to decorate these features, with analysis specific attributes. A feature is an analysisfeature if and only if there is a corresponding entry in the analysisfeature table. analysisfeatures will have two or more featureloc entries,
with rank indicating query/subject
=cut
__PACKAGE__->table("analysisfeature");
=head1 ACCESSORS
=head2 analysisfeature_id
data_type: 'integer'
is_auto_increment: 1
is_nullable: 0
sequence: 'analysisfeature_analysisfeature_id_seq'
=head2 feature_id
data_type: 'integer'
is_foreign_key: 1
is_nullable: 0
=head2 analysis_id
data_type: 'integer'
is_foreign_key: 1
is_nullable: 0
=head2 rawscore
data_type: 'double precision'
is_nullable: 1
This is the native score generated by the program; for example, the bitscore generated by blast, sim4 or genscan scores. One should not assume that high is necessarily better than low.
=head2 normscore
data_type: 'double precision'
is_nullable: 1
This is the rawscore but
semi-normalized. Complete normalization to allow comparison of
features generated by different programs would be nice but too
difficult. Instead the normalization should strive to enforce the
following semantics: * normscores are floating point numbers >= 0,
* high normscores are better than low one. For most programs, it would be sufficient to make the normscore the same as this rawscore, providing these semantics are satisfied.
=head2 significance
data_type: 'double precision'
is_nullable: 1
This is some kind of expectation or probability metric, representing the probability that the analysis would appear randomly given the model. As such, any program or person querying this table can assume the following semantics:
* 0 <= significance <= n, where n is a positive number, theoretically unbounded but unlikely to be more than 10
* low numbers are better than high numbers.
=head2 identity
data_type: 'double precision'
is_nullable: 1
Percent identity between the locations compared. Note that these 4 metrics do not cover the full range of scores possible; it would be undesirable to list every score possible, as this should be kept extensible. instead, for non-standard scores, use the analysisprop table.
=cut
__PACKAGE__->add_columns(
"analysisfeature_id",
{
data_type => "integer",
is_auto_increment => 1,
is_nullable => 0,
sequence => "analysisfeature_analysisfeature_id_seq",
},
"feature_id",
{ data_type => "integer", is_foreign_key => 1, is_nullable => 0 },
"analysis_id",
{ data_type => "integer", is_foreign_key => 1, is_nullable => 0 },
"rawscore",
{ data_type => "double precision", is_nullable => 1 },
"normscore",
{ data_type => "double precision", is_nullable => 1 },
"significance",
{ data_type => "double precision", is_nullable => 1 },
"identity",
{ data_type => "double precision", is_nullable => 1 },
);
__PACKAGE__->set_primary_key("analysisfeature_id");
__PACKAGE__->add_unique_constraint("analysisfeature_c1", ["feature_id", "analysis_id"]);
=head1 RELATIONS
=head2 feature
Type: belongs_to
Related object: L<Bio::Chado::Schema::Result::Sequence::Feature>
=cut
__PACKAGE__->belongs_to(
"feature",
"Bio::Chado::Schema::Result::Sequence::Feature",
{ feature_id => "feature_id" },
{
cascade_copy => 0,
cascade_delete => 0,
is_deferrable => 1,
on_delete => "CASCADE",
on_update => "CASCADE",
},
);
=head2 analysis
Type: belongs_to
Related object: L<Bio::Chado::Schema::Result::Companalysis::Analysis>
=cut
__PACKAGE__->belongs_to(
"analysis",
"Bio::Chado::Schema::Result::Companalysis::Analysis",
{ analysis_id => "analysis_id" },
{
cascade_copy => 0,
cascade_delete => 0,
is_deferrable => 1,
on_delete => "CASCADE",
on_update => "CASCADE",
},
);
=head2 analysisfeatureprops
Type: has_many
Related object: L<Bio::Chado::Schema::Result::Companalysis::Analysisfeatureprop>
=cut
__PACKAGE__->has_many(
"analysisfeatureprops",
"Bio::Chado::Schema::Result::Companalysis::Analysisfeatureprop",
{ "foreign.analysisfeature_id" => "self.analysisfeature_id" },
{ cascade_copy => 0, cascade_delete => 0 },
);
# Created by DBIx::Class::Schema::Loader v0.07010 @ 2011-03-16 23:09:58
# DO NOT MODIFY THIS OR ANYTHING ABOVE! md5sum:7SsoB6zDC9mxvavrk0DtTA
# You can replace this text with custom content, and it will be preserved on regeneration
1;
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