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Fernandes AD, Kleinstiver BP, Edgell DR, et al. Estimating the evidence of selection and the reliability of inference in unigenic evolution. Algorithms Mol Biol. 2010;5:35doi: 10.1186/1748-7188-5-35.
Fernandes, A. D., Kleinstiver, B. P., Edgell, D. R., Wahl, L. M., & Gloor, G. B. (2010). Estimating the evidence of selection and the reliability of inference in unigenic evolution. Algorithms for molecular biology : AMB, 535. https://doi.org/10.1186/1748-7188-5-35
Fernandes, Andrew D, et al. "Estimating the evidence of selection and the reliability of inference in unigenic evolution." Algorithms for molecular biology : AMB vol. 5 (2010): 35. doi: https://doi.org/10.1186/1748-7188-5-35
Fernandes AD, Kleinstiver BP, Edgell DR, Wahl LM, Gloor GB. Estimating the evidence of selection and the reliability of inference in unigenic evolution. Algorithms Mol Biol. 2010 Nov 08;5:35. doi: 10.1186/1748-7188-5-35. PMID: 21059250; PMCID: PMC2994857.
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Algorithms Mol Biol. 2010 Nov 08;5:35. doi: 10.1186/1748-7188-5-35.

Estimating the evidence of selection and the reliability of inference in unigenic evolution.

Algorithms for molecular biology : AMB

Andrew D Fernandes, Benjamin P Kleinstiver, David R Edgell, Lindi M Wahl, Gregory B Gloor

Affiliations

  1. Department of Biochemistry, The University of Western Ontario, N6A 5C1 Canada. [email protected].

PMID: 21059250 PMCID: PMC2994857 DOI: 10.1186/1748-7188-5-35

Abstract

BACKGROUND: Unigenic evolution is a large-scale mutagenesis experiment used to identify residues that are potentially important for protein function. Both currently-used methods for the analysis of unigenic evolution data analyze 'windows' of contiguous sites, a strategy that increases statistical power but incorrectly assumes that functionally-critical sites are contiguous. In addition, both methods require the questionable assumption of asymptotically-large sample size due to the presumption of approximate normality.

RESULTS: We develop a novel approach, termed the Evidence of Selection (EoS), removing the assumption that functionally important sites are adjacent in sequence and and explicitly modelling the effects of limited sample-size. Precise statistical derivations show that the EoS score can be easily interpreted as an expected log-odds-ratio between two competing hypotheses, namely, the hypothetical presence or absence of functional selection for a given site. Using the EoS score, we then develop selection criteria by which functionally-important yet non-adjacent sites can be identified. An approximate power analysis is also developed to estimate the reliability of inference given the data. We validate and demonstrate the the practical utility of our method by analysis of the homing endonuclease I-Bmol, comparing our predictions with the results of existing methods.

CONCLUSIONS: Our method is able to assess both the evidence of selection at individual amino acid sites and estimate the reliability of those inferences. Experimental validation with I-Bmol proves its utility to identify functionally-important residues of poorly characterized proteins, demonstrating increased sensitivity over previous methods without loss of specificity. With the ability to guide the selection of precise experimental mutagenesis conditions, our method helps make unigenic analysis a more broadly applicable technique with which to probe protein function.

AVAILABILITY: Software to compute, plot, and summarize EoS data is available as an open-source package called 'unigenic' for the 'R' programming language at http://www.fernandes.org/txp/article/13/an-analytical-framework-for-unigenic-evolution.

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