Bayesian alignment of proteins via Delaunay tetrahedralization
Najibi, S. M. LU
; Faghihi, M. R.
; Golalizadeh, M.
and Arab, S. S.
(2015)
In Journal of Applied Statistics
42(5).
p.1064-1079
- Abstract
An active area of research in bioinformatics is finding structural similarity of proteins by alignment. Among many methods, the popular one is to find the similarity based on statistical features. This method involves gathering information from the complex biomolecule structure and obtaining the best alignment by maximizing the number of matched features. In this paper, after reviewing statistical models for matching the structural biomolecule, it is shown that local alignment based on the Delaunay tetrahedralization (DT) can be used for Bayesian alignment of proteins. In this method, we use DT to add a priori structural information of protein in the Bayesian methodology. We demonstrate that this method shows advantages over competing... (More)
An active area of research in bioinformatics is finding structural similarity of proteins by alignment. Among many methods, the popular one is to find the similarity based on statistical features. This method involves gathering information from the complex biomolecule structure and obtaining the best alignment by maximizing the number of matched features. In this paper, after reviewing statistical models for matching the structural biomolecule, it is shown that local alignment based on the Delaunay tetrahedralization (DT) can be used for Bayesian alignment of proteins. In this method, we use DT to add a priori structural information of protein in the Bayesian methodology. We demonstrate that this method shows advantages over competing methods in achieving a global alignment of proteins, accelerating the convergence rate and improving the parameter estimates.
(Less)
- author
- Najibi, S. M.
LU
; Faghihi, M. R.
; Golalizadeh, M.
and Arab, S. S.
- publishing date
- 2015-05-04
- type
- Contribution to journal
- publication status
- published
- keywords
- MCMC, primary structure, protein alignment, shape analysis, size-and-shape distance, structural alignment
- in
- Journal of Applied Statistics
- volume
- 42
- issue
- 5
- pages
- 16 pages
- publisher
- Carfax Publishing
- external identifiers
-
- scopus:84924264845
- ISSN
- 0266-4763
- DOI
- 10.1080/02664763.2014.995605
- language
- English
- LU publication?
- no
- id
- 29f69bad-a361-4402-b67a-6d9e4e120b7a
- date added to LUP
- 2020-02-14 01:03:36
- date last changed
- 2022-02-01 03:35:31
@article{29f69bad-a361-4402-b67a-6d9e4e120b7a,
abstract = {{<p>An active area of research in bioinformatics is finding structural similarity of proteins by alignment. Among many methods, the popular one is to find the similarity based on statistical features. This method involves gathering information from the complex biomolecule structure and obtaining the best alignment by maximizing the number of matched features. In this paper, after reviewing statistical models for matching the structural biomolecule, it is shown that local alignment based on the Delaunay tetrahedralization (DT) can be used for Bayesian alignment of proteins. In this method, we use DT to add a priori structural information of protein in the Bayesian methodology. We demonstrate that this method shows advantages over competing methods in achieving a global alignment of proteins, accelerating the convergence rate and improving the parameter estimates.</p>}},
author = {{Najibi, S. M. and Faghihi, M. R. and Golalizadeh, M. and Arab, S. S.}},
issn = {{0266-4763}},
keywords = {{MCMC; primary structure; protein alignment; shape analysis; size-and-shape distance; structural alignment}},
language = {{eng}},
month = {{05}},
number = {{5}},
pages = {{1064--1079}},
publisher = {{Carfax Publishing}},
series = {{Journal of Applied Statistics}},
title = {{Bayesian alignment of proteins via Delaunay tetrahedralization}},
url = {{http://dx.doi.org/10.1080/02664763.2014.995605}},
doi = {{10.1080/02664763.2014.995605}},
volume = {{42}},
year = {{2015}},
}