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71.
Bayesian adaptive sequence alignment algorithms 总被引:3,自引:1,他引:2
The selection of a scoring matrix and gap penalty parameters continues to
be an important problem in sequence alignment. We describe here an
algorithm, the 'Bayes block aligner, which bypasses this requirement.
Instead of requiring a fixed set of parameter settings, this algorithm
returns the Bayesian posterior probability for the number of gaps and for
the scoring matrices in any series of interest. Furthermore, instead of
returning the single best alignment for the chosen parameter settings, this
algorithm returns the posterior distribution of all alignments considering
the full range of gapping and scoring matrices selected, weighing each in
proportion to its probability based on the data. We compared the Bayes
aligner with the popular Smith-Waterman algorithm with parameter settings
from the literature which had been optimized for the identification of
structural neighbors, and found that the Bayes aligner correctly identified
more structural neighbors. In a detailed examination of the alignment of a
pair of kinase and a pair of GTPase sequences, we illustrate the
algorithm's potential to identify subsequences that are conserved to
different degrees. In addition, this example shows that the Bayes aligner
returns an alignment-free assessment of the distance between a pair of
sequences.
相似文献
72.
73.
Ingolf Sommer Stefano Toppo Oliver Sander Thomas Lengauer Silvio CE Tosatto 《BMC bioinformatics》2006,7(1):364
Background
In the area of protein structure prediction, recently a lot of effort has gone into the development of Model Quality Assessment Programs (MQAPs). MQAPs distinguish high quality protein structure models from inferior models. Here, we propose a new method to use an MQAP to improve the quality of models. With a given target sequence and template structure, we construct a number of different alignments and corresponding models for the sequence. The quality of these models is scored with an MQAP and used to choose the most promising model. An SVM-based selection scheme is suggested for combining MQAP partial potentials, in order to optimize for improved model selection. 相似文献74.
Background
The impressive increase of novel RNA structures, during the past few years, demands automated methods for structure comparison. While many algorithms handle only small motifs, few techniques, developed in recent years, (ARTS, DIAL, SARA, SARSA, and LaJolla) are available for the structural comparison of large and intact RNA molecules. 相似文献75.