BACKGROUND: Although some aspects of limb development can be treated as a 2D problem, a true understanding of the morphogenesis and patterning requires 3D analysis. Since the data on gene expression patterns are largely static 3D image stacks, a major challenge is an efficient pipeline for staging each data-set, and then aligning and warping the data into a standard atlas for convenient visualisation. RESULTS: We present a novel bioinformatic pipeline tailored for 3D visualization and analysis of developing limb buds. The pipeline integrates key steps such as data acquisition, volume cleaning, surface extraction, staging, alignment, and advanced visualization techniques. Its modular design allows researchers to customize workflows while maintaining compatibility with tools such as Fiji and Vedo. The pipeline can be accessed at https://github.com/LauAvinyo/limblab . CONCLUSIONS: The pipeline advances 3D gene expression analysis in limb development by integrating flexible tools for staging, alignment, and visualization. It is user-friendly, scalable to other samples, and optimized for research needs. Future updates will enhance customization and expand applicability to other species and developmental biology fields.

MOTIVATION: Epistasis, the context-dependence of the contribution of an amino acid substitution to fitness, is common in evolution. To detect epistasis, fitness must be measured for at least four genotypes: the reference genotype, two different single mutants and a double mutant with both of the single mutations. For higher-order epistasis of the order n, fitness has to be measured for all 2n genotypes of an n-dimensional hypercube in genotype space forming a "combinatorially complete dataset". So far, only a handful of such datasets have been produced by manual curation. Concurrently, random mutagenesis experiments have produced measurements of fitness and other phenotypes in a high-throughput manner, potentially containing a number of combinatorially complete datasets. RESULTS: We present an effective recursive algorithm for finding all hypercube structures in random mutagenesis experimental data. To test the algorithm, we applied it to the data from a recent HIS3 protein dataset and found all 199,847,053 unique combinatorially complete genotype combinations of dimensionality ranging from two to twelve. The algorithm may be useful for researchers looking for higher-order epistasis in their high-throughput experimental data. AVAILABILITY: https://github.com/ivankovlab/HypercubeME.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.