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Motivation: Alignment-free distance and similarity functions (AF functions,for short) are a well established alternative to two and multiple sequencealignments for many genomic, metagenomic and epigenomic tasks. Due todata-intensive applications, the computation of AF functions is a Big Dataproblem, with the recent Literature indicating that the development of fast andscalable algorithms computing AF functions is a high-priority task. Somewhatsurprisingly, despite the increasing popularity of Big Data technologies inComputational Biology, the development of a Big Data platform for those taskshas not been pursued, possibly due to its complexity. Results: We fill thisimportant gap by introducing FADE, the first extensible, efficient and scalableSpark platform for Alignment-free genomic analysis. It supports nativelyeighteen of the best performing AF functions coming out of a recent hallmarkbenchmarking study. FADE development and potential impact comprises novelaspects of interest. Namely, (a) a considerable effort of distributedalgorithms, the most tangible result being a much faster execution time ofreference methods like MASH and FSWM; (b) a software design that makes FADEuser-friendly and easily extendable by Spark non-specialists; (c) its abilityto support data- and compute-intensive tasks. About this, we provide a noveland much needed analysis of how informative and robust AF functions are, interms of the statistical significance of their output. Our findings naturallyextend the ones of the highly regarded benchmarking study, since the functionsthat can really be used are reduced to a handful of the eighteen included inFADE.

Alignment-free Genomic Analysis via a Big Data Spark Platform