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Bio-Chado-Schema-0.05001
River stage one • 3 direct dependents • 3 total dependents
4 ++
/ Bio::Chado::Schema::Companalysis::Analysisfeature

NAME

Bio::Chado::Schema::Companalysis::Analysisfeature - 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

ACCESSORS

analysisfeature_id

  data_type: integer
  default_value: nextval('analysisfeature_analysisfeature_id_seq'::regclass)
  is_auto_increment: 1
  is_nullable: 0
  size: 4

feature_id

  data_type: integer
  default_value: undef
  is_foreign_key: 1
  is_nullable: 0
  size: 4

analysis_id

  data_type: integer
  default_value: undef
  is_foreign_key: 1
  is_nullable: 0
  size: 4

rawscore

  data_type: double precision
  default_value: undef
  is_nullable: 1
  size: 8

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.

normscore

  data_type: double precision
  default_value: undef
  is_nullable: 1
  size: 8

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.

significance

  data_type: double precision
  default_value: undef
  is_nullable: 1
  size: 8

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.

identity

  data_type: double precision
  default_value: undef
  is_nullable: 1
  size: 8

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.

RELATIONS

feature

Type: belongs_to

Related object: Bio::Chado::Schema::Sequence::Feature

analysis

Type: belongs_to

Related object: Bio::Chado::Schema::Companalysis::Analysis

analysisfeatureprops

Type: has_many

Related object: Bio::Chado::Schema::Companalysis::Analysisfeatureprop