ArticleMethods (San Diego, Calif.)2016
Computational analysis of fitness landscapes and evolutionary networks from in vitro evolution experiments.
Article in Methods (San Diego, Calif.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Protocell Effects on RNA Folding, Function, and Evolution.Accounts of chemical research · 2024Article
- Kinetic sequencing (k-Seq) as a massively parallel assay for ribozyme kinetics: utility and critical parameters.Nucleic acids research · 2021Article
- High throughput sequencing of in vitro selections of mRNA-displayed peptides: data analysis and applications.Physical chemistry chemical physics : PCCP · 2020Review
- Mapping a Systematic Ribozyme Fitness Landscape Reveals a Frustrated Evolutionary Network for Self-Aminoacylating RNA.Journal of the American Chemical Society · 2019Article
- Analysis of in vitro evolution reveals the underlying distribution of catalytic activity among random sequences.Nucleic acids research · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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.
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