Journal article
BigFoot: Bayesian alignment and phylogenetic footprinting with MCMC
- Abstract:
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Background: We have previously combined statistical alignment and phylogenetic footprinting to detect conserved functional elements without assuming a fixed alignment. Considering a probability-weighted distribution of alignments removes sensitivity to alignment errors, properly accommodates regions of alignment uncertainty, and increases the accuracy of functional element prediction. Our method utilized standard dynamic programming hidden markov model algorithms to analyze up to four sequences.
Results: We present a novel approach, implemented in the software package BigFoot, for performing phylogenetic footprinting on greater numbers of sequences. We have developed a Markov chain Monte Carlo (MCMC) approach which samples both sequence alignments and locations of slowly evolving regions. We implement our method as an extension of the existing StatAlign software package and test it on well-annotated regions controlling the expression of the even-skipped gene in Drosophila and the α-globin gene in vertebrates. The results exhibit how adding additional sequences to the analysis has the potential to improve the accuracy of functional predictions, and demonstrate how BigFoot outperforms existing alignment-based phylogenetic footprinting techniques.
Conclusion: BigFoot extends a combined alignment and phylogenetic footprinting approach to analyze larger amounts of sequence data using MCMC. Our approach is robust to alignment error and uncertainty and can be applied to a variety of biological datasets. The source code and documentation are publicly available for download from http://www.stats.ox.ac.uk/~satija/BigFoot/
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 1.6MB, Terms of use)
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- Publisher copy:
- 10.1186/1471-2148-9-217
Authors
- Publisher:
- BioMed Central
- Journal:
- BMC Evolutionary Biology More from this journal
- Volume:
- 9
- Article number:
- 217
- Publication date:
- 2009-08-28
- Acceptance date:
- 2009-08-28
- DOI:
- EISSN:
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1471-2148
- Language:
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English
- Keywords:
- Pubs id:
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102891
- UUID:
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uuid:0b957328-0332-4655-9d03-f1cf456d18f9
- Local pid:
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pubs:102891
- Source identifiers:
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102891
- Deposit date:
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2012-12-19
- ARK identifier:
Terms of use
- Copyright holder:
- Satija et al
- Copyright date:
- 2009
- Notes:
- © 2009 Satija et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Licence:
- CC Attribution (CC BY)
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