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We propose a novel combination of methods that (i) portrays quantitativecharacteristics of a DNA sequence as an image, (ii) computes distances betweenthese images, and (iii) uses these distances to output a map wherein eachsequence is a point in a common Euclidean space. In the resulting "MolecularDistance Map" each point signifies a DNA sequence, and the geometric distancebetween any two points reflects the degree of relatedness between thecorresponding sequences and species.  Molecular Distance Maps present compelling visual representations ofrelationships between species and could be used for taxonomic clarifications,for species identification, and for studies of evolutionary history. One of theadvantages of this method is its general applicability since, as sequencealignment is not required, the DNA sequences chosen for comparison can becompletely different regions in different genomes. In fact, this method can beused to compare any two DNA sequences. For example, in our dataset of 3,176mitochondrial DNA sequences, it correctly finds the mtDNA sequences mostclosely related to that of the anatomically modern human (the Neanderthal, theDenisovan, and the chimp), and it finds that the sequence most different fromit belongs to a cucumber. Furthermore, our method can be used to compare realsequences to artificial, computer-generated, DNA sequences. For example, it isused to determine that the distances between a Homo sapiens sapiens mtDNA andartificial sequences of the same length and same trinucleotide frequencies canbe larger than the distance between the same human mtDNA and the mtDNA of afruit-fly.  We demonstrate this method's promising potential for taxonomicalclarifications by applying it to a diverse variety of cases that have beenhistorically controversial, such as the genus Polypterus, the family Tarsiidae,and the vast (super)kingdom Protista.

Map of Life: Measuring and Visualizing Species' Relatedness with "Molecular Distance Maps"