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      Computing DNA duplex instability profiles efficiently with a two-state model: trends of promoters and binding sites

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      1 , 2 , , 1 , , 3 , 3
      BMC Bioinformatics
      BioMed Central

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          Abstract

          Background

          DNA instability profiles have been used recently for predicting the transcriptional start site and the location of core promoters, and to gain insight into promoter action. It was also shown that the use of these profiles can significantly improve the performance of motif finding programs.

          Results

          In this work we introduce a new method for computing DNA instability profiles. The model that we use is a modified Ising-type model and it is implemented via statistical mechanics. Our linear time algorithm computes the profile of a 10,000 base-pair long sequence in less than one second. The method we use also allows the computation of the probability that several consecutive bases are unpaired simultaneously. This is a feature that is not available in other linear-time algorithms. We use the model to compare the thermodynamic trends of promoter sequences of several genomes. In addition, we report results that associate the location of local extrema in the instability profiles with the presence of core promoter elements at these locations and with the location of the transcription start sites (TSS). We also analyzed the instability scores of binding sites of several human core promoter elements. We show that the instability scores of functional binding sites of a given core promoter element are significantly different than the scores of sites with the same motif occurring outside the functional range (relative to the TSS).

          Conclusions

          The time efficiency of the algorithm and its genome-wide applications makes this work of broad interest to scientists interested in transcriptional regulation, motif discovery, and comparative genomics.

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          Most cited references27

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          Origins of specificity in protein-DNA recognition.

          Specific interactions between proteins and DNA are fundamental to many biological processes. In this review, we provide a revised view of protein-DNA interactions that emphasizes the importance of the three-dimensional structures of both macromolecules. We divide protein-DNA interactions into two categories: those when the protein recognizes the unique chemical signatures of the DNA bases (base readout) and those when the protein recognizes a sequence-dependent DNA shape (shape readout). We further divide base readout into those interactions that occur in the major groove from those that occur in the minor groove. Analogously, the readout of the DNA shape is subdivided into global shape recognition (for example, when the DNA helix exhibits an overall bend) and local shape recognition (for example, when a base pair step is kinked or a region of the minor groove is narrow). Based on the >1500 structures of protein-DNA complexes now available in the Protein Data Bank, we argue that individual DNA-binding proteins combine multiple readout mechanisms to achieve DNA-binding specificity. Specificity that distinguishes between families frequently involves base readout in the major groove, whereas shape readout is often exploited for higher resolution specificity, to distinguish between members within the same DNA-binding protein family.
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            Base-stacking and base-pairing contributions into thermal stability of the DNA double helix

            Two factors are mainly responsible for the stability of the DNA double helix: base pairing between complementary strands and stacking between adjacent bases. By studying DNA molecules with solitary nicks and gaps we measure temperature and salt dependence of the stacking free energy of the DNA double helix. For the first time, DNA stacking parameters are obtained directly (without extrapolation) for temperatures from below room temperature to close to melting temperature. We also obtain DNA stacking parameters for different salt concentrations ranging from 15 to 100 mM Na+. From stacking parameters of individual contacts, we calculate base-stacking contribution to the stability of A•T- and G•C-containing DNA polymers. We find that temperature and salt dependences of the stacking term fully determine the temperature and the salt dependence of DNA stability parameters. For all temperatures and salt concentrations employed in present study, base-stacking is the main stabilizing factor in the DNA double helix. A•T pairing is always destabilizing and G•C pairing contributes almost no stabilization. Base-stacking interaction dominates not only in the duplex overall stability but also significantly contributes into the dependence of the duplex stability on its sequence.
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              Improved nearest-neighbor parameters for predicting DNA duplex stability.

              Thermodynamic data were determined from UV absorbance vs temperature profiles of 23 oligonucleotides. These data were combined with data from the literature for 21 sequences to derive improved parameters for the 10 Watson-Crick nearest neighbors. The observed trend in nearest-neighbor stabilities at 37 degrees C is GC > CG > GG > GA approximately GT approximately CA > CT > AA > AT > TA (where only the top strand is shown for each nearest neighbor). This trend suggests that both sequence and base composition are important determinants of DNA duplex stability. On average, the improved parameters predict deltaG degrees(37), deltaH degrees, deltaS degrees, and T(m) within 4%, 7%, 8%, and 2 degrees C, respectively. The parameters are optimized for the prediction of oligonucleotides dissolved in 1 M NaC1.
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                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2010
                21 December 2010
                : 11
                : 604
                Affiliations
                [1 ]Department of Mathematics, University of Illinois at Urbana-Champaign, Urbana, IL, USA
                [2 ]National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, USA
                [3 ]Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
                Article
                1471-2105-11-604
                10.1186/1471-2105-11-604
                3018474
                21172036
                916fe2f4-1f98-4170-9ece-07c1d6796c8d
                Copyright ©2010 Kantorovitz et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 11 May 2010
                : 21 December 2010
                Categories
                Research Article

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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