Optimal scoring matrices for estimating distances between aligned sequences (1999).

Content Provider	CiteSeerX
Author	Gonnet, Gaston Korostensky, Chantal
Abstract	Sequence alignment is typically the first step in many research areas of bioinformatics, where some form of score or distance is derived. Those scores and distances are often used for evolutionary tree construction, multiple sequence alignments, allagainst -all comparisons of whole genomes and many other tasks. Since those scores and distances are the basis for further studies, it is important that they can be estimated as well as possible. In this paper, we prove that the scores obtained from Dayhoff matrices (or from any other matrix) are not consistent for tree construction. Then we show how this can be corrected and how to create an optimal scoring matrix to estimate distances. This scoring matrix is optimal within a large class of estimators. Finally we present a complete example. 1 Introduction 1.1 Model of Evolution The model we consider here is a Markovian model of evolution [1], which assumes that amino acids mutate independently of each other, with probabilities which depen...
File Format	PDF
Publisher Date	1999-01-01
Access Restriction	Open
Subject Keyword	Optimal Scoring Matrix Sequence Alignment Amino Acid Tree Construction Complete Example Markovian Model Evolutionary Tree Construction Multiple Sequence Alignment First Step Dayhoff Matrix Large Class Whole Genome Many Task Many Research Area
Content Type	Text