Parameter Advising for Multiple Sequence Alignment

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<p>This book develops a new approach called<i>parameter advising</i> 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<i> advisor</i> is a procedure that automatically chooses a parameter setting for the input, and has two main ingredients:</p><p>(a)         the<i>set</i>of parameter choices considered by the advisor, and</p><p>(b)         an<i>estimator</i> of alignment accuracy used to rank alignments produced by the aligner.</p><p>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.</p><p>The chapters first lay out the foundations of parameter advising, and then cover applications and extensions of advising. The content</p><p>   examines formulations of parameter advising and their<i>computational complexity</i>,</p><p>   develops methods for learning good<i>accuracy estimators</i>,</p><p>   presents approximation algorithms for finding good sets of<i>parameter choices</i>, and</p><p>   assesses<i>software implementations</i> of advising that perform well on real biological data.</p><p>Also explored are applications of parameter advising to</p><p><i>adaptive local realignment</i>, where advising is performed on local regions of the sequences to automatically adapt to varying mutation rates, and</p><p><i>ensemble alignment</i>, 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.</p><p>The book concludes by offering future directions in advising research.</p>

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