The algorithm proceeds in two phases.
A Covariate represents a predictor, also known as a "feature" or "independent variable".
A Covariate represents a predictor, also known as a "feature" or "independent variable".
Concrete implementations of Covariate should inherit from AbstractCovariate, not Covariate.
Represents a tuple containing a value for each covariate.
Represents a tuple containing a value for each covariate.
The values for mandatory covariates are stored in member fields and optional
covariate values are in extras
.
Represents the abstract space of all possible CovariateKeys for the given set of Covariates.
An empirical frequency count of mismatches from the reference.
An empirical frequency count of mismatches from the reference.
This is used in ObservationTable, which maps from CovariateKey to Observation.
Table containing the empirical frequency of mismatches for each set of covariate values.
The algorithm proceeds in two phases. First, we make a pass over the reads to collect statistics and build the recalibration tables. Then, we perform a second pass over the reads to apply the recalibration and assign adjusted quality scores.