For comparison of whole-genome (genic ؉ nongenic) sequences, multiple sequence alignment of a few selected genes is not appropriate. One approach is to use an alignment-free method in which feature (or l-mer) frequency profiles (FFP) of whole genomes are used for comparison-a variation of a text or book comparison method, using word frequency profiles. In this approach it is critical to identify the optimal resolution range of l-mers for the given set of genomes compared. The optimum FFP method is applicable for comparing whole genomes or large genomic regions even when there are no common genes with high homology. We outline the method in 3 stages: (i) We first show how the optimal resolution range can be determined with English books which have been transformed into long character strings by removing all punctuation and spaces. (ii) Next, we test the robustness of the optimized FFP method at the nucleotide level, using a mutation model with a wide range of base substitutions and rearrangements. (iii) Finally, to illustrate the utility of the method, phylogenies are reconstructed from concatenated mammalian intronic genomes; the FFP derived intronic genome topologies for each l within the optimal range are all very similar. The topology agrees with the established mammalian phylogeny revealing that intron regions contain a similar level of phylogenic signal as do coding regions.mammalian genome phylogeny ͉ whole-genome comparison ͉ whole-genome phylogeny ͉ whole-intron phylogeny T he comparison of 2 closely related genomes at the base-by-base nucleotide sequence level is accomplished by sequence alignment. However, because species diverge extensively over time, insertions/deletions and genomic rearrangements make straightforward sequence alignment unreliable or impossible. This difficulty is typically overcome by 1 of 2 methods. The first involves extracting a common subset of genes (coding sequences) shared by all of the species compared, then building a multiple sequence alignment (MSA) for each gene, and finally concatenating each alignment into a super MSA (1). The MSA and an appropriate base-substitution model are used to calculate similarity scores. The second method is best described as gene profiling, where the occurrence of each gene in a dictionary of genes is counted, forming a gene presence/ absence profile. The relative frequency difference between genomes from their gene profiles is used to derive a similarity score (2). Both methods rely on the correct definition and selection of common genes to be compared, and significant homology among aligned gene sequences.If, however, the genomes do not share an alignable set of common genes, the alignment-free method is the only option of choice at present. Also, these methods of comparison strictly focus on comparing the coding (coding for protein, and functional RNA) portions of genomes, which can amount to as little as 1% of the genomic sequence in humans (3). As for the noncoding sequence of the genome (the other 99%), much of its function is unknown, ...