Computational analysis of fitness landscapes and evolutionary networks from in vitro evolution experiments

• Published in: Methods (San Diego, Calif.) Vol. 106; pp. 86 - 96
• Main Authors: Xulvi-Brunet, Ramon, Campbell, Gregory W., Rajamani, Sudha, Jiménez, José I., Chen, Irene A.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.08.2016
• Subjects: Directed Molecular Evolution; Fitness landscape; Genetic Fitness; Genotype; In vitro evolution; Phenotype; RNA - genetics; RNA selection; RNA selection; Fitness landscape; In vitro evolution
• ISSN: 1046-2023; 1095-9130
• DOI: 10.1016/j.ymeth.2016.05.012

•We describe methods to extract quantitative results from in vitro selections.•Systematic biases in the selection can be estimated and corrected.•Tools to map an RNA fitness landscape are available in the Galaxy platform. In vitro selection experiments in biochemistry allow for the discovery of novel molecules capable of specific desired biochemical functions. However, this is not the only benefit we can obtain from such selection experiments. Since selection from a random library yields an unprecedented, and sometimes comprehensive, view of how a particular biochemical function is distributed across sequence space, selection experiments also provide data for creating and analyzing molecular fitness landscapes, which directly map function (phenotypes) to sequence information (genotypes). Given the importance of understanding the relationship between sequence and functional activity, reliable methods to build and analyze fitness landscapes are needed. Here, we present some statistical methods to extract this information from pools of RNA molecules. We also provide new computational tools to construct and study molecular fitness landscapes.