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Home
Inference And Visualization Of Complex Genotype-phenotype Maps.

Home
Inference And Visualization Of Complex Genotype-phenotype Maps.

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Inference and Visualization of Complex Genotype-Phenotype Maps.

Carlos Martí-Gómez¹, Juannan Zhou^2,3, Wei-Chia Chen⁴

¹Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.

Molecular Biology and Evolution

|February 3, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Researchers developed gpmap-tools, a Python library to analyze complex genotype-phenotype maps from large biological sequence datasets. This tool aids in understanding genetic interactions and their evolutionary impact.

Keywords:

Gaussian process Shine-Dalgarno sequence epistasis fitness landscape genotype–phenotype map

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Area of Science:

Genomics
Systems Biology
Computational Biology

Background:

Understanding genotype-phenotype maps is crucial in biology but challenging due to high-dimensional sequence space and context-dependent mutation effects.
Multiplex assays of variant effect (MAVEs) and large natural sequence collections offer new avenues for characterizing these maps.
Existing tools lack comprehensive statistical and exploratory analysis capabilities for high-dimensional genotype-phenotype data.

Purpose of the Study:

To develop a computational framework for analyzing complex genotype-phenotype maps.
To provide tools for inference, imputation, and error estimation from MAVE and natural sequence data.
To enable visualization and summarization of epistasis patterns in large genotype-phenotype landscapes.

Main Methods:

Development of the Python library 'gpmap-tools'.
Integration of models for statistical inference, phenotypic imputation, and error estimation.
Application of methods for summarizing epistasis and visualizing genotype-phenotype maps with millions of genotypes.

Main Results:

Successfully inferred genotype-phenotype maps for 262,144 variants of the bacterial Shine-Dalgarno sequence using genomic and MAVE data.
Visualization revealed high-fitness ridges connecting core motifs, suggesting a new thermodynamic model.
The study demonstrated the utility of gpmap-tools for analyzing complex genetic interactions.

Conclusions:

gpmap-tools offers a flexible and interpretable framework for studying complex genotype-phenotype relationships.
The tool provides new insights into the architecture of genetic interactions and their evolutionary consequences.
This work facilitates a deeper understanding of how biological sequences determine observable traits.