Schema Version: 1.3



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


Column Type Size Foreign Key Nullable Default Comments
analysisfeature_id bigserial 19
feature_id int8 19
feature.feature_id analysisfeature_feature_id_fkey C
analysis_id int8 19
analysis.analysis_id analysisfeature_analysis_id_fkey C
rawscore float8 17,17

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 float8 17,17

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 float8 17,17

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 float8 17,17

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.

Table contained -1 rows


Constraint Name Type Sort Column(s)
analysisfeature_pkey Primary key Asc analysisfeature_id
analysisfeature_c1 Must be unique Asc/Asc feature_id + analysis_id
analysisfeature_idx1 Performance Asc feature_id
analysisfeature_idx2 Performance Asc analysis_id