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<h1>Schiessel Lab GitHub Page</h1>
<p>Welcome to the GitHub page of the Theoretical Biophysics group of Prof. <NAME> at Leiden University. We will be updating this shortly.</p>
<p>Projects:</p>
<p><a href="https://github.com/SchiesselLab/MarkovModel" target="_blank">Markov chain model</a></p>
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                <h1>The Schiessel Lab code repository</h1>
                <p class="lead">You're on the GitHub page for the Theoretical Physics of Life Processes group at Leiden University, led by <NAME>. Below you'll find information about the projects in our <a href="https://github.com/SchiesselLab" target="_blank">GitHub repositories</a>. More information about the group itself and its research can be found on the <a href="http://www.lorentz.leidenuniv.nl/~schiessel/" target="_blank">group website</a>.</p>
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                <h1>Our repositories</h1>
                <p class="lead">The Schiessel Lab's GitHub repositories can be found at <a href="https://github.com/SchiesselLab/" target="_blank">github.com/SchiesselLab</a>. Here follow descriptions of our various computational projects.</p>

                <div class="project" id="proj-markov">
                    <span class="projectimage"> <img src="Selection.png" style="width: 90%" /> </span>
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                        <h3> <a class="projecttitle" href="https://github.com/SchiesselLab/MarkovModel">Nucleosome Positioning with Markov Chains</a> </h3>
                        <p style="text-align: justify">
                            Combining the basic idea for a Markov chain model for sequence-dependent nucleosome positioning presented by Segal et al. (2006) [<a href="https://www.doi.org/10.1038/nature04979" target="_blank">doi</a>] with nucleosomal sequence preference statistics derived from the Mutation Monte Carlo method of Eslami-Mossallam et al. (2016) [<a href="https://doi.org/10.1371/journal.pone.0156905" target="_blank">open access</a>] leads to a computationally efficient method for predicting nucleosome affinity from based on a theoretical model of the nucleosome. 
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                            This methodology is explained and applied in the following publications: 
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                                <li>Tompitak et al., BMC Bioinformatics 18 (2017), 157 [<a href="https://www.doi.org/10.1186/s12859-017-1569-0" target="_blank">open access</a>]</li>
                                <li> Tompitak et al., Biophysical Journal 112 (2017), 505-511 [<a href="http://dx.doi.org/10.1016/j.bpj.2016.12.041" target="_blank">doi</a>]</li>
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                <div class="project" id="proj-rbp3d">
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                        <h3> 3D Visualization of the RBP Model </h3>
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                            Built on <a href="https://threejs.org" target="_blank">three.js</a>, this project provides in-browser 3D visualization of the Rigid Base Pair model. Live version <a href="RBP3D/index.html">here</a>. It has been used to provide visuals for various publications:
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                                <li>Tompitak and Schiessel, Nederlands Tijdschrift voor Natuurkunde 83 (2017) 164-167</li>
                                <li>Tompitak et al., Phys. Rev. E 95 (2017) [<a href="https://doi.org/10.1103/PhysRevE.95.052402" target="_blank">doi</a>]</li>
                                <li>Tompitak et al., EPL 116 (2016) 68005 [<a href="https://doi.org/10.1209/0295-5075/116/68005" target="_blank">doi</a>]</li>
                                <li>De Bruin et al., J. Phys. Chem. B 120 (2016), 5855-5863 [<a href="https://www.doi.org/10.1021/acs.jpcb.6b00391" target="_blank">doi</a>]</li>
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