The algorithms that are used for domain identification are built around a set of simple assumptions that describe the process of evolution. In general, evolution is believed to form and mold genomes largely via three mechanisms, namely i) chemical changes through the incorporation of base analogs, the effects of radiation or random enzymatic errors by polymerases, ii) cellular repair processes that counter mutations, and iii) selection pressures that manifest themselves as the positive or negative influence that determines whether the mutation will be present in subsequent generations [20, 21] . By definition, each of these phenomena styles, reproductive strategies, or the lack of apparent polymerase-dependent proofreading such as in positivestranded RNA viruses [22] [23] [24] [25] . Consequently, substitution rates need therefore be calculated to correctly compare two or more sequences and hunt uncharted genomes for comparable domains. Particularly this last strategy, using general rate matrices like BLOSOM and PAM, is an elegant example of how new protein functions can be discovered [26] [27] [28] [29] [30] . Fast algorithms for pair-wise alignments can be found in the Basic Local Alignment Search Tool (BLAST), whereas multiple sequence alignments (MSAs, Fig. 1A) in which multiple sequences are compared simultaneously are commonly created with for example ClustalX and MUSCLE (see Table  1 ) [31] [32] [33] [34] .