Although non-coding dna sequences do not encode proteins[1,2],more and more studies show that non-codingdna plays an indispensable role in other aspects[3].In this paper,variant maps[4,5]are applied on both coding an...
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Although non-coding dna sequences do not encode proteins[1,2],more and more studies show that non-codingdna plays an indispensable role in other aspects[3].In this paper,variant maps[4,5]are applied on both coding and non-coding dna sequences to analyze different classes of selected *** variant map technology,it is convenient to convert longer data sequences into specific visual figures to illustrate different probability distributions on *** data sequences are selected from the four sample species.
In the post-genomic era, identification of specific regulatory motifs or transcription factor binding sites (TFBSs) in non-coding dna sequences, which is essential to elucidate transcriptional regulatory networks, h...
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In the post-genomic era, identification of specific regulatory motifs or transcription factor binding sites (TFBSs) in non-coding dna sequences, which is essential to elucidate transcriptional regulatory networks, has emerged as an obstacle that frustrates many researchers. Consequently, numerous motif discovery tools and correlated databases have been applied to solving this problem. However, these existing methods, based on different computational algorithms, show diverse motif prediction efficiency in non-coding dna sequences. Therefore, understanding the similarities and differences of computational algorithms and enriching the motif discovery literatures are important for users to choose the most appropriate one among the online available tools. Moreover, there still lacks credible criterion to assess motif discovery tools and instructions for researchers to choose the best according to their own projects. Thus integration of the related resources might be a good approach to improve accuracy of the application. Recent studies integrate regulatory motif discovery tools with experimental methods to offer a complementary approach for researchers, and also provide a much-needed model for current researches on transcriptional regulatory networks. Here we present a comparative analysis of regulatory motif discovery tools for TFBSs.
Microstructural changes such as insertions and deletions (=indels) are a major driving force in the evolution of non-coding dna sequences. To better understand the mechanisms by which indel mutations arise, as well as...
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Microstructural changes such as insertions and deletions (=indels) are a major driving force in the evolution of non-coding dna sequences. To better understand the mechanisms by which indel mutations arise, as well as the molecular evolution of non-coding regions, the number and pattern of indels and nucleotide substitutions were compared in the whole chloroplast genomes. Comparisons were made for a total of over 38 kb non-coding dna sequences from 126 intergenic regions in two data sets representing species with different divergence times: sugarcane and maize and Oryza sativa var. indica and japonica. The main findings of this study are: (i) Approximately half of all indels are single nucleotide indels. This observation agrees with previous studies in various organisms. (ii) The distribution and number of indels was different between two data sets, and different patterns were observed for tandem repeat and non-repeat indels. (iii) Distribution pattern of tandem repeat indels showed statistically significant bias towards A/T-rich. (iv) The rate of indel mutation was estimated to be approximate to 0.8 +/- 0.04 x 10(-9) per site per year, which was similar to previous estimates in other organisms. (v) The frequencies of nucleotide substitutions and indels were significantly lower in inverted repeat (IR).
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