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Estimating sufficient statistics in co-evolutionary analysis by mutual information

Paper ID Volume ID Publish Year Pages File Format Full-Text
15272 1399 2009 5 PDF Available
Title
Estimating sufficient statistics in co-evolutionary analysis by mutual information
Abstract

Mutual information (MI) is a standard measure in information theory to observe and quantify correlated signals and events in both, empirical data sets and theoretical models. In the field of computational biology the MI turned out to be particularly useful in studies on co-evolutionary signals of sites within biomolecules. A key issue in the applicability of the MI is, however, a correct reference system or null model to understand finite-size effects in the underlying, finite data set. Although some bioinformatics studies exist with rigorous results for theoretical, well-designed random distributions, data from real-world proteins was never used to quantify the effect of finite-size samples. The impact of real-world statistics is, however, most relevant for researchers in all fields concerned with detecting evolutionary signals within biological sequences. We present results on such effects in finite-sized biological data sets and point to future research directions. We are most of all concerned with bacterial, ribosomal proteins as a prototypical example in molecular evolution. We compare to previous published suggestions, give an empirical formula, and propose a protocol to guide future research projects.

Keywords
Mutual information; Sequence alignment; Correlation; Finite size; Bacterial ribosome
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 33, Issue 6, December 2009, Pages 440–444
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
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