Quality estimation of multiple sequence alignments by Bayesian hypothesis testing.
In this work we present a web-based tool for estimating multiple alignment quality using Bayesian hypothesis testing. The proposed method is very simple, easily implemented and not time consuming with a linear complexity. We evaluated method against a series of different alignments (a set of random and biologically derived alignments) and compared the results with tools based on classical statistical methods (such as sFFT and csFFT). Taking correlation coefficient as an objective criterion of the true quality, we found that Bayesian hypothesis testing performed better on average than the classical methods we tested. This approach may be used independently or as a component of any tool in computational biology which is based on the statistical estimation of alignment quality.


BACKGROUND
http://www.fmi.ch/groups/functional.genomics/tool.htm.


BACKGROUND
Supplementary data are available from http://www.fmi.ch/groups/functional.genomics/tool-Supp.htm.
Friedrich Miescher Institute for Biomedical Research, Novartis Research Foundation, Maulbeerestrasse 66, CH-4056 Basel.
Andrija
A
Tomovic
Edward J
EJ
Oakeley
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D010363:Pattern Recognition, Automated; D016415:Sequence Alignment / Q000379:methods*; D017421:Sequence Analysis / Q000379:methods*; D012984:Software