HypercubeME

HypercubeME identifies combinatorially complete genotype hypercubes in high-throughput mutagenesis datasets to enable detection and analysis of higher-order epistasis.

Key Features:

• Recursive Algorithm: Employs a recursive algorithm to enumerate all hypercube structures within large genotype lists, enabling detection of higher-order epistatic interactions.
• Combinatorially Complete Datasets: Generates combinatorially complete datasets representing n-dimensional hypercubes, requiring fitness measurements for all 2^n genotypes to assess n-th order epistasis.
• High-Throughput Capability: Scales to large datasets and, in an application to an HIS3 protein dataset, identified 199,847,053 unique combinatorially complete genotype combinations across dimensions two through twelve.
• Input Data Format: Parses genotype list files with tab-separated columns where mutation lists are in the first column and wild-type variants are denoted by '0Z' or an empty string (e.g., test_complete_03.txt).

Scientific Applications:

• Epistasis Detection: Facilitates identification and analysis of higher-order epistatic interactions from combinatorially complete genotype sets.
• Evolutionary Biology Research: Supports studies of molecular evolution by revealing how combinations of amino acid substitutions affect evolutionary fitness.
• Genetic Engineering and Synthetic Biology: Aids prediction and optimization of effects of multiple simultaneous mutations for genetic modification efforts.

Methodology:

Parses input genotype lists and leverages a recursive algorithm to systematically enumerate hypercube structures and generate combinatorially complete datasets.