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Related Experiment Video

Updated: Feb 25, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

phydms: software for phylogenetic analyses informed by deep mutational scanning.

Sarah K Hilton^1,2, Michael B Doud^1,2,3, Jesse D Bloom^1,2

¹Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.

|August 9, 2017

Summary

This summary is machine-generated.

Deep mutational scanning experiments can now inform gene evolution models. New software, phydms, compares these experimental results to natural selection, aiding evolutionary studies.

Keywords:

Amino acid preferences Beta lactamase Codon substitution model Deep mutational scanning Diversifying selection ExpCM Hemagglutinin Phylogenetics Positive selection dN/dS

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Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Area of Science:

Evolutionary biology
Molecular biology
Bioinformatics

Background:

Deep mutational scanning (DMS) enables comprehensive experimental measurement of all amino-acid point mutations in proteins.
These experimental datasets offer valuable insights into protein evolution and function.
Phylogenetic substitution models are crucial for understanding gene evolution in natural populations.

Purpose of the Study:

To introduce phydms, a software tool for analyzing deep mutational scanning data with site-specific phylogenetic substitution models.
To enable quantitative comparisons between DMS experimental results and natural selection pressures.
To facilitate the re-scaling of DMS data and identify discrepancies between lab experiments and natural evolution.

Main Methods:

Utilizing phylogenetic tree topologies inferred by external programs.
Implementing site-specific phylogenetic substitution models for comparative analysis.
Developing algorithms for re-scaling DMS data and detecting evolutionary anomalies.

Main Results:

phydms allows for rigorous comparison of DMS experiments against natural selection.
The software can adjust DMS data to match the stringency of natural selection.
phydms identifies specific sites with divergent evolutionary patterns between lab and nature.

Conclusions:

phydms provides a robust framework for integrating experimental protein evolution data with evolutionary modeling.
The software will be instrumental as DMS datasets become more prevalent.
phydms enhances our ability to understand the evolutionary forces shaping protein sequences in nature.