This book develops a new approach called
parameter advising
for finding a parameter setting for a sequence aligner that yields a quality alignment of a given set of input sequences. In this framework, a parameter
advisor
is a procedure that automatically chooses a parameter setting for the input, and has two main ingredients:
(a) the
set
of parameter choices considered by the advisor, and
(b) an
estimator
of alignment accuracy used to rank alignments produced by the aligner.
On coupling a parameter advisor with an aligner, once the advisor is trained in a learning phase, the user simply inputs sequences to align, and receives an output alignment from the aligner, where the advisor has automatically selected the parameter setting.
The chapters first lay out the foundations of parameter advising, and then cover applications and extensions of advising. The content
examines formulations of parameter advising and their
computational complexity
,
develops methods for learning good
accuracy estimators
,
presents approximation algorithms for finding good sets of
parameter choices
, and
assesses
software implementations
of advising that perform well on real biological data.
Also explored are applications of parameter advising to
adaptive local realignment
, where advising is performed on local regions of the sequences to automatically adapt to varying mutation rates, and
ensemble alignment
, where advising is applied to an ensemble of aligners to effectively yield a new aligner of higher quality than the individual aligners in the ensemble.
The book concludes by offering future directions in advising research.
