We present a new computational approach to finding borders between coding and noncoding DNA. This approach has two features: (i) DNA sequences are described by a 12-letter alphabet that captures the differential base composition at each codon position, and (ii) the search for the borders is carried out by means of an entropic segmentation method which uses only the general statistical properties of coding DNA. We find that this method is highly accurate in finding borders between coding and noncoding regions and requires no "prior training" on known data sets. Our results appear to be more accurate than those obtained with moving windows in the discrimination of coding from noncoding DNA. PACS numbers: 87.15.Cc, 87.10. + e, 87.14.Gg The entropic segmentation process partitions a heterogeneous DNA sequence into homogeneous subsequences, which we term compositional domains [1] [2] [3] . Although all the alphabets or mapping rules conventionally used in describing DNA sequences had been tried, we had not been able so far to assign any biological function to the obtained domains. Here we introduce a new alphabet that takes into account the differential base composition at each codon position, and we find that the compositional domains correlate to either coding or noncoding DNA regions. This finding suggests the possibility of using the entropic segmentation process for computational gene finding. The computational recognition of genes is one of the challenges in the analysis of newly sequenced genomes, which is fundamental for modern functional genomics (the goal of which is the search for the different functional elements which make up the DNA sequences [4] ). The predictive power of current computational methods, although adequate in genomes for which a significant fraction of genes are previously known [5] , remains rather low when faced with the large anonymous sequences now being generated by genome projects. An example is provided by the DNA sequence of human chromosome 22, where only 20 per cent of annotated genes have all exons predicted exactly [6] . A critical limitation of most current methods is the need of "prior training," given the lack of experimentally annotated DNA sequences in most of the recently sequenced genomes. Hence, new strategies for computational gene finding that do not require prior training on organism-specific data sets are needed, and the entropic segmentation process may serve as a first step in this direction. Current methods of gene finding use a variety of biological information as potential sequence signals involved in gene specification or sequence similarity database searches. These signals are mainly used to obtain probable borders of coding regions, and they should be obtained from elaborated biological information, which is highly dependent on the particular genome considered. Here we offer an entropic segmentation method to detect borders between coding and noncoding DNA. Although previous works based on the Shannon's entropy deal with the problem of finding patterns in DNA or protein sequences [7] , their approaches are of local character, instead of the global segmentation we address here. Our method uses only the known statistical general properties of coding DNA. In this way, the prior training on known data sets is avoided; furthermore, the search for additional biological information (such as splice sites or termination signals) can also be avoided; however, such additional information could be easily incorporated and exploited in a concrete implementation of the algorithm. One of the most relevant and well-known statistical features of coding regions is nonuniform codon usage [8] . This means that, inside coding regions, not all triplets of nucleotides (called codons) occur with the same probability. In particular, the probability of appearance of a nucleotide is different in each of the three positions of the triplets [9] [10] [11] . This may be due to the restrictions imposed by the genetic code and also probably to some kind of preferences in the synonymous codon usage; but no matter what its origin is, this feature is not present in noncoding DNA, so this property can be used to distinguish between coding and noncoding DNA. In fact, based on these differences, the first generation of gene prediction programs, designed to identify approximate locations of coding regions in genomic DNA, were developed [10] . To take into account this statistical property of coding DNA, we develop a segmentation algorithm based on a 12-symbol alphabet. We define the phase of position, 0031-9007͞00͞85(6)͞1342(4)$15.00
Bernaola et al. REF proposed using entropic segmentation for finding borders between coding and non-coding DNA regions.
Finding Borders between Coding and Noncoding DNA Regions by an Entropic Segmentation Method
Physical review letters
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