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

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.

Analysis of in vitro evolution reveals the underlying distribution of catalytic activity among random sequences

“…Sequences were sorted by similarity using custom software running on the Galaxy bioinformatics platform ( 20 , 31 ), with the highest-abundance sequence in each family defined as the center, for each round. In general, families were separated by large Hamming distances from each other due to the long length of the random region, allowing unambiguous assignment of sequences to families.…”

“…We use the term ‘cluster’ to refer to a group of related sequences for which such splits were performed when appropriate (and ‘cluster’ is equivalent to ‘family’ when no split was appropriate). Further details on sequence-grouping procedures are described in previous work ( 20 , 31 ) and in Text S1.…”

“…Any data and code not previously described ( 20 , 31 ) will be made available by the authors upon request.…”

Mapping a Systematic Ribozyme Fitness Landscape Reveals a Frustrated Evolutionary Network for Self-Aminoacylating RNA

“…Clustering was performed on the Galaxy platform 94 for sequences in Rounds 4–6. Multiple families containing the same motif were designated as 1A.1, 1A.2, etc., or 2.1, 2.2, etc.…”

High throughput sequencing of in vitro selections of mRNA-displayed peptides: data analysis and applications

“…Pre-processing of sequencing data is imperative for successful downstream data transformations and analyses (see ref. [71][72][73] for some examples and discussion). Pre-processing steps typically entail an initial quality assessment, removal of low-quality reads, quality trimming, adapter trimming, read joining/merging for paired-end reads, and primer trimming/sequence extraction (Fig.…”