Identification of CpG islands using a bank of IIR lowpass filters

It has been known that biological sequences such as the DNA sequence display different kinds of patterns depending on their biological functions. This statistical difference can be exploited for identifying the region of interest, such as the protein coding regions or CpG islands, in a new biological sequence that has not been annotated yet. A region of particular interest is the CpG island, which is a region in a DNA sequence that is rich in the dinucleotide CpG, since it is known that they can be used as gene markers. There have been several computational methods for identifying CpG islands, each with its own strength and weakness. In this paper, we propose a novel scheme for detecting CpG islands in a genomic sequence, which is based on a bank of IIR lowpass filters. The proposed method is capable of identifying CpG islands efficiently at a low computational expense. Simulation results are included where appropriate to demonstrate the idea

Statistical-mechanical lattice models for protein-DNA binding in chromatin

Statistical-mechanical lattice models for protein-DNA binding are well established as a method to describe complex ligand binding equilibriums measured in vitro with purified DNA and protein components. Recently, a new field of applications has opened up for this approach since it has become possible to experimentally quantify genome-wide protein occupancies in relation to the DNA sequence. In particular, the organization of the eukaryotic genome by histone proteins into a nucleoprotein complex termed chromatin has been recognized as a key parameter that controls the access of transcription factors to the DNA sequence. New approaches have to be developed to derive statistical mechanical lattice descriptions of chromatin-associated protein-DNA interactions. Here, we present the theoretical framework for lattice models of histone-DNA interactions in chromatin and investigate the (competitive) DNA binding of other chromosomal proteins and transcription factors. The results have a number of applications for quantitative models for the regulation of gene expression.Comment: 19 pages, 7 figures, accepted author manuscript, to appear in J. Phys.: Cond. Mat

Predicting nucleosome positioning using a duration Hidden Markov Model

<p>Abstract</p> <p>Background</p> <p>The nucleosome is the fundamental packing unit of DNAs in eukaryotic cells. Its detailed positioning on the genome is closely related to chromosome functions. Increasing evidence has shown that genomic DNA sequence itself is highly predictive of nucleosome positioning genome-wide. Therefore a fast software tool for predicting nucleosome positioning can help understanding how a genome's nucleosome organization may facilitate genome function.</p> <p>Results</p> <p>We present a duration Hidden Markov model for nucleosome positioning prediction by explicitly modeling the linker DNA length. The nucleosome and linker models trained from yeast data are re-scaled when making predictions for other species to adjust for differences in base composition. A software tool named NuPoP is developed in three formats for free download.</p> <p>Conclusions</p> <p>Simulation studies show that modeling the linker length distribution and utilizing a base composition re-scaling method both improve the prediction of nucleosome positioning regarding sensitivity and false discovery rate. NuPoP provides a user-friendly software tool for predicting the nucleosome occupancy and the most probable nucleosome positioning map for genomic sequences of any size. When compared with two existing methods, NuPoP shows improved performance in sensitivity.</p