Correlation approach to identify coding regions in DNA sequences

• Published in: Biophysical journal Vol. 67; no. 1; pp. 64 - 70
• Main Authors: Ossadnik, S.M., Buldyrev, S.V., Goldberger, A.L., Havlin, S., Mantegna, R.N., Peng, C.K., Simons, M., Stanley, H.E.
• Format: Journal Article
• Language: English
• Published: Legacy CDMS Elsevier Inc 01.07.1994
• Subjects: Algorithms; Base Sequence; Chloroplasts - genetics; Chromosomes, Fungal; coding sequence finder algorithms; correlation analysis; DNA; DNA - chemistry; DNA - genetics; DNA, Fungal - genetics; False Positive Reactions; Genetic Code; Life Sciences (General); nucleotide sequences; open reading frames; Plants - genetics; prediction; Saccharomyces cerevisiae; Space life sciences; structural genes; Non-Nasa Center; Nasa Discipline Cardiopulmonary; NASA Discipline Cardiopulmonary; Non-NASA Center
• ISSN: 0006-3495; 1542-0086
• DOI: 10.1016/S0006-3495(94)80455-2

Recently, it was observed that noncoding regions of DNA sequences possess long-range power-law correlations, whereas coding regions typically display only short-range correlations. We develop an algorithm based on this finding that enables investigators to perform a statistical analysis on long DNA sequences to locate possible coding regions. The algorithm is particularly successful in predicting the location of lengthy coding regions. For example, for the complete genome of yeast chromosome III (315,344 nucleotides), at least 82% of the predictions correspond to putative coding regions; the algorithm correctly identified all coding regions larger than 3000 nucleotides, 92% of coding regions between 2000 and 3000 nucleotides long, and 79% of coding regions between 1000 and 2000 nucleotides. The predictive ability of this new algorithm supports the claim that there is a fundamental difference in the correlation property between coding and noncoding sequences. This algorithm, which is not species-dependent, can be implemented with other techniques for rapidly and accurately locating relatively long coding regions in genomic sequences.