Mapping short reads in RY-space: a novel strategy for extending the phylogenetic range of Next Generation Sequence mapping algorithms

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Modern DNA sequencers can generate billions of short DNA fragments that will typically be only 50 to 100 molecules (nucleotides) in size. To make sense of these fragments, or reads, it is usually necessary to compare them with previously sequenced DNA, often an entire genome, which may be several billions of nucleotides in length. This is a difficult computational challenge. Although comparing one piece of DNA with another is a relatively simple process, the sheer number of fragments generated, combined with the often large size of the reference genome, means that without well designed software it is hard, if not impossible, to process all the data produced by a single sequencing run within a practical timeframe using current computers. Fast and efficient software have been duly developed but, in order to achieve a reasonable mapping speed with the latest computers, compromises have to be made. Current software can accommodate only a few differences between short DNA sequences for a successful match to be identified (often only two or three differences within a short stretch is possible). It is this limitation which makes it very difficult to compare DNA between species, where a high level of variation is to be expected, and so it is hard to make sense of short-read data if the organism in question has not been sequenced before. Unfortunately most species of research, economic, and clinical importance have yet to be sequenced. Species that haven't been sequenced before must go through a far more expensive process of long-read genome sequencing, and this means that the genomic analysis of many organisms remain financially unfeasible with current technologies. We propose a new way of mapping DNA that will, at least in part, overcome this difficulty. Our approach is based on the observation that evolutionary patterns existing within DNA sequences are more fully revealed when the information content is reduced and the sequence pattern simplified. A stretch of DNA can be thought of as a complex pattern of four types of nucleotide: Adenine, Cytosine, Guanine and Thymine (A, C, G, and T for short). A and G are purine nucleotides, whilst C and T are pyrimidines. It has long been recognised that, as organisms evolve, the rate at which a purine mutates into another purine, or a pyrimidine to another pyrimidine, will tend to be higher than when purines mutate into pyrimidines and visa versa. This imbalance in mutation rate will create patterns of purines and pyrimidines within DNA sequences that are more stable motifs of shared ancestry between species than is the case with more noisy nucleotide patterns. We will use this more robust pattern to match sequences together from different species: we will develop software which will simplify DNA sequences down to their purine and pyrimidine content alone, compare them to identify similarities using approaches equivalent to that currently used with raw DNA sequences, then convert them back into their original nucleotides for subsequent analysis. Because the conversion from individual nucleotides to their purine/pyrimidine identities alone is simple, the speed with which translated reads can be mapped will be comparable to that achieved with raw DNA. Thus, using our strategy, mapping should almost be as quick as current methods and use similar levels of computer resources. However, the extent to which one species can be compared with another will be far greater meaning that it will be possible to sequence more organisms with low-cost short-read sequencing technologies even when a reference sequence for that particular species is not available. As a result lower cost sequencing will become practical for a much wider range of organisms than is currently possible, ensuring that the new techniques currently being developed, that rely on short read sequencing, can be applied in many more contexts.