IMR Press / FBL / Volume 13 / Issue 16 / DOI: 10.2741/3137

Frontiers in Bioscience-Landmark (FBL) is published by IMR Press from Volume 26 Issue 5 (2021). Previous articles were published by another publisher on a subscription basis, and they are hosted by IMR Press on imrpress.com as a courtesy and upon agreement with Frontiers in Bioscience.

Open Access Article
Information, probability, and the abundance of the simplest RNA active sites
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1 Department of Computer Science, University of Colorado at Boulder, 430 UCB, Boulder, CO 80309-0430
2 Department of Applied Mathematics, University of Colorado at Boulder, 526 UCB, Boulder, CO 80309-0526
3 Department of Molecular, Cellular and Developmental Biology, University of Colorado at Boulder, 347 UCB, Boulder, CO 80309-0430
4 Department of Chemistry and Biochemistry, University of Colorado at Boulder, 215 UCB, Boulder, CO 80309

*Author to whom correspondence should be addressed.

Academic Editor: Massimo Giulio

Front. Biosci. (Landmark Ed) 2008, 13(16), 6060–6071; https://doi.org/10.2741/3137
Published: 1 May 2008
(This article belongs to the Special Issue Early evolution of life)
Abstract

The abundance of simple but functional RNA sites in random-sequence pools is critical for understanding emergence of RNA functions in nature and in the laboratory today. The complexity of a site is typically measured in terms of information, i.e. the Shannon entropy of the positions in a multiple sequence alignment. However, this calculation can be incorrect by many orders of magnitude. Here we compare several methods for estimating the abundance of RNA active-site patterns in the context of in vitro selection (SELEX), highlighting the strengths and weaknesses of each. We include in these methods a new approach that yields confidence bounds for the exact probability of finding specific kinds of RNA active sites. We show that all of the methods that take modularity into account provide far more accurate estimates of this probability than the informational methods, and that fast approximate methods are suitable for a wide range of RNA motifs.

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