Scoredist: A simple and robust protein sequence distance estimator

Background: Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on ...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Erik L. L. Sonnhammer, Volker Hollich
• Keywords: BMCBI 2005, Evolutionary, Evolutionary Models, Optimal Matrix