biostudies-literatureUnknown1(1)Zhang YFunctional beta-diversity analysis on numerous microbiomes interprets the linkages between metabolic functions and their meta-data. To evaluate the microbiome beta-diversity, widely used distance metrices only count overlapped gene families but omit their inherent relationships, resulting in erroneous distances due to the sparsity of high-dimensional function profiles. Here we propose <i>Hierarchical Meta-Storms</i> (HMS) to tackle such problem. HMS contains two core components: (i) a dissimilarity algorithm that comprehensively measures functional distances among microbiomes using multi-level metabolic hierarchy and (ii) a fast Principal Co-ordinates Analysis (PCoA) implementation that deduces the beta-diversity pattern optimized by parallel computing. Results showed HMS can detect the variations of microbial functions in upper-level metabolic pathways, however, always missed by other methods. In addition, HMS accomplished the pairwise distance matrix and PCoA for 20 000 microbiomes in 3.9 h on a single computing node, which was 23 times faster and 80% less RAM consumption compared to existing methods, enabling the in-depth data mining among microbiomes on a high resolution. HMS takes microbiome functional profiles as input, produces their pairwise distance matrix and PCoA coordinates.<h4>Availability and implementation</h4>It is coded in C/C++ with parallel computing and released in two alternative forms: a standalone software (https://github.com/qdu-bioinfo/hierarchical-meta-storms) and an equivalent R package (https://github.com/qdu-bioinfo/hrms).<h4>Supplementary information</h4>Supplementary data are available at <i>Bioinformatics Advances</i> online.Bioinformatics advancesvbab003https://www.ebi.ac.uk/biostudies/studies/S-EPMC9710644biostudies-literature<i>Hierarchical Meta-Storms</i> enables comprehensive and rapid comparison of microbiome functional profiles on a large scale using hierarchical dissimilarity metrics and parallel computing.PMC9710644Li JZhang YChen YSu XJing Gfalse<i>Hierarchical Meta-Storms</i> enables comprehensive and rapid comparison of microbiome functional profiles on a large scale using hierarchical dissimilarity metrics and parallel computing.Functional beta-diversity analysis on numerous microbiomes interprets the linkages between metabolic functions and their meta-data. To evaluate the microbiome beta-diversity, widely used distance metrices only count overlapped gene families but omit their inherent relationships, resulting in erroneous distances due to the sparsity of high-dimensional function profiles. Here we propose <i>Hierarchical Meta-Storms</i> (HMS) to tackle such problem. HMS contains two core components: (i) a dissimilarity algorithm that comprehensively measures functional distances among microbiomes using multi-level metabolic hierarchy and (ii) a fast Principal Co-ordinates Analysis (PCoA) implementation that deduces the beta-diversity pattern optimized by parallel computing. Results showed HMS can detect the variations of microbial functions in upper-level metabolic pathways, however, always missed by other methods. In addition, HMS accomplished the pairwise distance matrix and PCoA for 20 000 microbiomes in 3.9 h on a single computing node, which was 23 times faster and 80% less RAM consumption compared to existing methods, enabling the in-depth data mining among microbiomes on a high resolution. HMS takes microbiome functional profiles as input, produces their pairwise distance matrix and PCoA coordinates.<h4>Availability and implementation</h4>It is coded in C/C++ with parallel computing and released in two alternative forms: a standalone software (https://github.com/qdu-bioinfo/hierarchical-meta-storms) and an equivalent R package (https://github.com/qdu-bioinfo/hrms).<h4>Supplementary information</h4>Supplementary data are available at <i>Bioinformatics Advances</i> online.2021-01-01T00:00:00Z20212025-04-21T23:16:21.233Z2025-04-05T19:05:49.489ZS-EPMC97106443670010110.1093/bioadv/vbab003