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Motivation: Alignment-free (AF) distance/similarity functions are a key toolfor sequence analysis. Experimental studies on real datasets abound and, tosome extent, there are also studies regarding their control of false positiverate (Type I error). However, assessment of their power, i.e., their ability toidentify true similarity, has been limited to some members of the D2 family byexperimental studies on short sequences, not adequate for current applications,where sequence lengths may vary considerably. Such a State of the Art ismethodologically problematic, since information regarding a key feature such aspower is either missing or limited. Results: By concentrating on arepresentative set of word-frequency based AF functions, we perform the firstcoherent and uniform evaluation of the power, involving also Type I error forcompleteness. Two Alternative models of important genomic features (CISRegulatory Modules and Horizontal Gene Transfer), a wide range of sequencelengths from a few thousand to millions, and different values of k have beenused. As a result, we provide a characterization of those AF functions that isnovel and informative. Indeed, we identify weak and strong points of eachfunction considered, which may be used as a guide to choose one for analysistasks. Remarkably, of the fifteen functions that we have considered, only fourstand out, with small differences between small and short sequence lengthscenarios. Finally, in order to encourage the use of our methodology forvalidation of future AF functions, the Big Data platform supporting it ispublic.

The Power of Word-Frequency Based Alignment-Free Functions: a Comprehensive Large-scale Experimental Analysis -- Version 3