TAILIEUCHUNG - Báo cáo sinh học: "Estimating the evidence of selection and the reliability of inference in unigenic evolution"

Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí y học Molecular Biology cung cấp cho các bạn kiến thức về ngành sinh học đề tài: Estimating the evidence of selection and the reliability of inference in unigenic evolution. | Fernandes et al. Algorithms for Molecular Biology 2010 5 35 http content 5 1 35 AMR ALGORITHMS FOR MOLECULAR BIOLOGY RESEARCH Open Access Estimating the evidence of selection and the reliability of inference in unigenic evolution 112 1 Andrew D Fernandes Benjamin P Kleinstiver David R Edgell Lindi M Wahl Gregory B Gloor 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 samplesize. 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 .

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