tailieunhanh - Báo cáo hóa học: " Segmentation of DNA into Coding and Noncoding Regions Based on Recursive Entropic Segmentation and Stop-Codon Statistics"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Segmentation of DNA into Coding and Noncoding Regions Based on Recursive Entropic Segmentation and Stop-Codon Statistics | EURASIP Journal on Applied Signal Processing 2004 1 81-91 2004 Hindawi Publishing Corporation Segmentation of DNA into Coding and Noncoding Regions Based on Recursive Entropic Segmentation and Stop-Codon Statistics Daniel Nicorici Tampere International Center for Signal Processing Tampere University of Technology . Box 553 Tampere FIN-33101 Finland Email Jaakko Astola Tampere International Center for Signal Processing Tampere University of Technology . Box 553 Tampere FIN-33101 Finland Email Received 28 February 2003 Revised 15 September 2003 Heterogeneous DNA sequences can be partitioned into homogeneous domains that are comprised of the four nucleotides A C G and T and the stop codons. Recursively we apply a new entropic segmentation method on DNA sequences using Jensen-Shannon and Jensen-Renyi divergences in order to find the borders between coding and noncoding DNA regions. We have chosen 12-and 18-symbol alphabets that capture i the differential nucleotide composition in codons and ii the differential stop-codon composition along all the three phases in both strands of the DNA. The new segmentation method is based on the Jensen-Renyi divergence measure nucleotide statistics and stop-codon statistics in both DNA strands. The recursive segmentation process requires no prior training on known datasets. Consequently for three entire genomes of bacteria we find that the use of nucleotide composition stop-codon composition and Jensen-Renyi divergence improve the accuracy of finding the borders between coding and noncoding regions in DNA sequences. Keywords and phrases recursive segmentation DNA sequence information divergence measures statistics of stop codons Bayesian information criterion. 1. INTRODUCTION The computational identification of genes and coding re- gions in DNA sequences is a major goal and a long-lasting topic for molecular biology especially for the human genome project 1 2 . One of the main goals of

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