Bioinformatics Advance Access originally published online on April 29, 2008
Bioinformatics 2008 24(12):1456-1458; doi:10.1093/bioinformatics/btn212
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nuScore: a web-interface for nucleosome positioning predictions
1Harvard-Partners Center for Genetics and Genomics, Brigham and Women's Hospital, Boston, MA 02115, 2Children Hospital Informatics Program, Boston, MA 02115, 3Wright-Rieman Laboratories, Rutgers, The State University of New Jersey, Piscataway, NJ 08854 and 4Laboratory of Cell Biology, National Cancer Institute, Bethesda, MD 20892, USA
*To whom correspondence should be addressed.
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ABSTRACT |
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 TOP ABSTRACT 1 INTRODUCTION2 METHODOLOGY3 OPTIONS AND CONTROLS4 FUTURE DEVELOPMENTSACKNOWLEDGEMENTSREFERENCES |
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Summary: Sequence-directed mapping of nucleosome positions is of major biological interest. Here, we present a web-interface for estimation of the affinity of the histone core to DNA and prediction of nucleosome arrangement on a given sequence. Our approach is based on assessment of the energy cost of imposing the deformations required to wrap DNA around the histone surface. The interface allows the user to specify a number of options such as selecting from several structural templates for threading calculations and adding random sequences to the analysis.
Availability: The nuScore interface is freely available for use at http://compbio.med.harvard.edu/nuScore.
Contact: peter_park{at}harvard.edu; tolstorukov{at}gmail.com
Supplementary information: The site contains user manual, description of the methodology and examples.
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1 INTRODUCTION |
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TOPABSTRACT1 INTRODUCTION2 METHODOLOGY3 OPTIONS AND CONTROLS4 FUTURE DEVELOPMENTSACKNOWLEDGEMENTSREFERENCES |
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Nucleosomes, the basic repeating units of chromatin, comprise 147 bp of DNA wrapped around the histone protein core (Richmond and Davey, 2003). Placement of nucleosomes at transcription start sites may significantly affect gene expression (Jenuwein and Allis, 2001; Kornberg and Lorch, 1999) by occluding access to DNA for regulatory proteins or recruiting them through the interactions with the histones. Therefore, prediction of nucleosome positioning on genomic sequences is of great biological interest.
The ability of DNA to form nucleosomes depends at least partially on the underlying sequence (Trifonov, 1980; Widom, 2001). Thus, the nucleosome arrangement for a DNA fragment can be predicted based on the sequence alone in the absence of other factors, e.g. chromatin-remodeling proteins. Most of the computational algorithms currently available for such predictions use scoring functions based on the distributions of dinucleotides in the sequences and are trained on the sets of sequences known to position nucleosomes (Ioshikhes et al., 1999; Segal et al., 2006; Yuan and Liu, 2007). Another approach is to use the sequence-dependent structural properties of DNA to identify sequences that wrap more (or less) readily around the histone core (Anselmi et al., 2000; Mengeritsky and Trifonov, 1983; Sivolob and Khrapunov, 1995; Zhurkin, 1983).
In line with the ‘structural’ approach, we estimate the energy cost of the deformations imposed by histones on DNA (Tolstorukov et al., 2007). Such a cost is sequence-dependent (Olson et al., 1998) and, since the sequence-specific interactions between the histones and DNA bases are essentially absent, is one of the main factors determining affinity of the histones to DNA. Here, we present a web-based application that implements this methodology for prediction of nucleosome positioning patterns on DNA sequences.
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2 METHODOLOGY |
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TOPABSTRACT1 INTRODUCTION2 METHODOLOGY3 OPTIONS AND CONTROLS4 FUTURE DEVELOPMENTSACKNOWLEDGEMENTSREFERENCES |
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The cost of DNA deformation is estimated by ‘threading’ the DNA fragment of a given sequence on the template comprising the trajectory of DNA observed in the structure of a nucleosome core particle determined experimentally. We use the representation of DNA structure at the dinucleotide level, i.e. with a set of collective variables specifying the relative positioning of neighboring base pairs (Twist, Tilt, Roll, Shift, Slide and Rise) (Dickerson et al., 1989). The deformation energy score E of the threaded sequence can be expressed as (Olson et al., 1998):
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| (1) |
is the imposed deviation of the i-th dinucleotide step parameter 
at the n-th step of the template from the rest-state value 
of the step MN. The fij(MN) are DNA stiffness constants that depend on the sequence, and L is the number of base pair steps in the nucleosome template. The rest-state values and stiffness constants of the 16 dinucleotide steps were estimated from the analysis of a set of protein–DNA complexes (Olson et al., 1998).
A strong positioning DNA sequence is expected to show a sharp dip in the energy score for the preferred template setting. The positioning score P can be defined by the relative deviation of the energy score calculated for a given position x of the template on the sequence with respect to the scores calculated for n neighboring positions of the template (Fig. 1):
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| (2) |
±n/2, the condition |E(x) – 
E
±n/2)|/E±n/2 E > 0.1 is used in addition to Equation 2.
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All numerical calculations are performed with FORTRAN code (available upon request). The interface was implemented using HTML and java script at the front end and CGI/Perl at the server end. A module written in R (http://www.R-project.org) is used to produce the output plots. Computational time depends linearly on the total number of the bases in the input sequences and the server can handle a large set of input sequences easily. The algorithm was tested on the sequences for which nucleosome positions were experimentally mapped to base pair resolution (see Supplementary material on the nuScore site).
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3 OPTIONS AND CONTROLS |
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TOPABSTRACT1 INTRODUCTION2 METHODOLOGY3 OPTIONS AND CONTROLS4 FUTURE DEVELOPMENTSACKNOWLEDGEMENTSREFERENCES |
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A general view of the nuScore interface is shown in Figure 2. The user can select from eight nucleosomal templates based on crystal structures solved to a resolution of 3Å or better. These structures contain native histones from different organisms and no additional ligands (see Methods section on the nuScore site for details). Since the threading templates are not symmetrical, two orientations of each template on the sequence are possible and the user has the option to select one. Also, the average or the lowest-energy score for the two template orientations may be reported in the output files. Alternatively, the user may use templates with the parameters symmetrized relative to the structural dyad.
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A set of random sequences of the same dinucleotide composition can be generated for each input sequence. The number of random sequences in the set is specified by the user. The mean and standard deviation of the deformation and positioning scores for each random set are reported in the output files.
The user can select the size of the window used to calculate the nucleosome-positioning score, which specifies how many neighboring positions are used to compute the score at a given position (Equation 2). Considering 18 neighboring positions seems to be reasonable; using fewer neighbors may not be enough to produce statistically sound results and using more positions may interfere with known nucleosome-positioning signals, which often show 10-bp periodicities [e.g. AA:TT signal (Trifonov, 1980)].
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4 FUTURE DEVELOPMENTS |
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TOPABSTRACT1 INTRODUCTION2 METHODOLOGY3 OPTIONS AND CONTROLS4 FUTURE DEVELOPMENTSACKNOWLEDGEMENTSREFERENCES |
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Other models of DNA flexibility, based on the analysis of other sets of DNA structures (Morozov et al., 2005) or the molecular dynamics trajectories of DNA (Lankas et al., 2003), can easily be implemented into the program. Incorporation of trimeric models is anticipated to increase the predictive power of our approach since such models combine descriptions of DNA deformability at the levels of dinucleotides and individual base pairs.
Finally, using threading to assess DNA deformation scores assumes that all sequences adopt exactly the same fold on the nucleosome. The structure of the nucleosomal DNA, however, may depend on the sequence. Optimization of the DNA trajectory for each given sequence will further advance our structural approach.
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ACKNOWLEDGEMENTS |
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TOPABSTRACT1 INTRODUCTION2 METHODOLOGY3 OPTIONS AND CONTROLS4 FUTURE DEVELOPMENTSACKNOWLEDGEMENTSREFERENCES |
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Funding: This work was supported in part by the Intramural Research Program of the NIH National Cancer Institute, Center for Cancer Research and USPHS grant GM20861.
Conflict of Interest: none declared.
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FOOTNOTES |
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Associate Editor: Martin Bishop
Received on January 2, 2008; revised on April 25, 2008; accepted on April 26, 2008
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REFERENCES |
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TOPABSTRACT1 INTRODUCTION2 METHODOLOGY3 OPTIONS AND CONTROLS4 FUTURE DEVELOPMENTSACKNOWLEDGEMENTSREFERENCES |
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