Parametric and non-parametric masking of randomness in sequence alignments can be improved and leads to better resolved trees.

ABSTRACT:

Background

Methods of alignment masking, which refers to the technique of excluding alignment blocks prior to tree reconstructions, have been successful in improving the signal-to-noise ratio in sequence alignments. However, the lack of formally well defined methods to identify randomness in sequence alignments has prevented a routine application of alignment masking. In this study, we compared the effects on tree reconstructions of the most commonly used profiling method (GBLOCKS) which uses a predefined set of rules in combination with alignment masking, with a new profiling approach (ALISCORE) based on Monte Carlo resampling within a sliding window, using different data sets and alignment methods. While the GBLOCKS approach excludes variable sections above a certain threshold wh

SUBMITTER: Kuck P 

PROVIDER: S-EPMC2867768 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature