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Reference [33] has proposed a permutation alignment approach based on the power ratio measure.
Concretely, we present a subinteger range-bin alignment approach based on the traditional envelope correlation method and the use of reference profiles.
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Liao and Jain [18] proposed an alignment-free approach based on a large scale dictionary of SIFT descriptors.
The method is an alignment-free approach based on an efficient graph kernel.
Here, we characterize protein sequences using an alignment-free approach based on techniques of time series analysis commonly-used in statistical physics and elsewhere (Bray, 2013; Redner, 2007).
We consider an alignment-free approach based on a graph representation of the peptide sequence where different abstraction levels are available in a unified way.
This subinteger alignment approach is based on an optimization stage which has carefully been designed in order to avoid possible convergence to local maxima.
As with the previous dataset, the classical approach based on multiple sequence alignment performed best; this time the difference between alignment-based and alignment-free methods was larger.
Initially, we attempted an SNP discovery approach based on alignments of ESTs.
On our simulated protein families, this approach led to even better results than the classical approach based on multiple alignment and likelihood.
Using a reproducible approach based on automated alignments without a priori filtering of nucleotides, we were able to infer several phylogenetic relationships, which obtain significant support from bootstrap analyses of all underlying data matrices (Figs. 3– 5, Tables 3, 4; Additional files 2, 3).
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