Inference and Visualization of Complex Genotype-Phenotype Maps

Understanding how biological sequences give rise to observable traits, that is, how genotype maps to phenotype, is a central goal in biology. Yet, our knowledge of genotype-phenotype maps in natural systems remains limited by the high dimensionality of sequence space and the context-dependent effects of mutations. The emergence of Multiplex assays of variant effect (MAVEs) and the availability of ever growing collections of natural sequences offer new opportunities to characterize these maps at an unprecedented scale. However, tools for statistical and exploratory analyses of such high-dimensional data are still needed. To address this gap, we developed gpmap-tools (https://github.com/cmarti/gpmap-tools), a python library integrating models for inference, phenotypic imputation, and error estimation from MAVE data or natural sequences in the presence of genetic interactions of any order. gpmap-tools also provides methods for summarizing patterns of epistasis across sites and visualization of genotype-phenotype maps with millions of genotypes. We demonstrate its utility by inferring genotype-phenotype maps containing 262,144 variants of the Shine-Dalgarno sequence, a key motif for mRNA translation in bacteria, from both genomic 5 'UTR sequences and MAVE data. Visualization of the inferred landscapes consistently revealed high-fitness ridges that link core motifs at different distances from the start codon, motivating a new, highly interpretable thermodynamic model for this system. In summary, gpmap-tools provides a flexible, interpretable framework for studying complex genotype-phenotype maps, offering new insights into the architecture of genetic interactions and their evolutionary consequences.