Nucleic Acids Research Advance Access originally published online on February 19, 2008
Nucleic Acids Research 2008 36(7):2240-2248; doi:10.1093/nar/gkn065
Nucleic Acids Research, 2008, Vol. 36, No. 7 2240-2248
© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computational Biology |
Accurate statistical model of comparison between multiple sequence alignments
1Howard Hughes Medical Institute and 2Department of Biochemistry, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390-9050, USA
*To whom correspondence should be addressed. Tel: +1 214 645 5951; Fax: +1 214 645 5948; Email: sadreyev{at}chop.swmed.edu
Received January 2, 2008. Revised January 31, 2008. Accepted February 1, 2008.
Comparison of multiple protein sequence alignments (MSA) reveals unexpected evolutionary relations between protein families and leads to exciting predictions of spatial structure and function. The power of MSA comparison critically depends on the quality of statistical model used to rank the similarities found in a database search, so that biologically relevant relationships are discriminated from spurious connections. Here, we develop an accurate statistical description of MSA comparison that does not originate from conventional models of single sequence comparison and captures essential features of protein families. As a final result, we compute E-values for the similarity between any two MSA using a mathematical function that depends on MSA lengths and sequence diversity. To develop these estimates of statistical significance, we first establish a procedure for generating realistic alignment decoys that reproduce natural patterns of sequence conservation dictated by protein secondary structure. Second, since similarity scores between these alignments do not follow the classic Gumbel extreme value distribution, we propose a novel distribution that yields statistically perfect agreement with the data. Third, we apply this random model to database searches and show that it surpasses conventional models in the accuracy of detecting remote protein similarities.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  Y. Wang, R. I. Sadreyev, and N. V. Grishin PROCAIN server for remote protein sequence similarity search Bioinformatics, August 15, 2009; 25(16): 2076 - 2077. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 B. W. Brandt and J. Heringa webPRC: the Profile Comparer for alignment-based searching of public domain databases Nucleic Acids Res., July 1, 2009; 37(suppl_2): W48 - W52. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. I. Sadreyev, M. Tang, B.-H. Kim, and N. V. Grishin COMPASS server for homology detection: improved statistical accuracy, speed and functionality Nucleic Acids Res., July 1, 2009; 37(suppl_2): W90 - W94. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
I. Jung and D. Kim SIMPRO: simple protein homology detection method by using indirect signals Bioinformatics, March 15, 2009; 25(6): 729 - 735. [Abstract] [Full Text] [PDF]
|
|