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

Structural features based genome-wide characterization and prediction of nucleosome organization

<p>Abstract</p> <p>Background</p> <p>Nucleosome distribution along chromatin dictates genomic DNA accessibility and thus profoundly influences gene expression. However, the underlying mechanism of nucleosome formation remains elusive. Here, taking a structural perspective, we systematically explored nucleosome formation potential of genomic sequences and the effect on chromatin organization and gene expression in <it>S. cerevisiae</it>.</p> <p>Results</p> <p>We analyzed twelve structural features related to flexibility, curvature and energy of DNA sequences. The results showed that some structural features such as DNA denaturation, DNA-bending stiffness, Stacking energy, Z-DNA, Propeller twist and free energy, were highly correlated with in vitro and in vivo nucleosome occupancy. Specifically, they can be classified into two classes, one positively and the other negatively correlated with nucleosome occupancy. These two kinds of structural features facilitated nucleosome binding in centromere regions and repressed nucleosome formation in the promoter regions of protein-coding genes to mediate transcriptional regulation. Based on these analyses, we integrated all twelve structural features in a model to predict more accurately nucleosome occupancy in vivo than the existing methods that mainly depend on sequence compositional features. Furthermore, we developed a novel approach, named DLaNe, that located nucleosomes by detecting peaks of structural profiles, and built a meta predictor to integrate information from different structural features. As a comparison, we also constructed a hidden Markov model (HMM) to locate nucleosomes based on the profiles of these structural features. The result showed that the meta DLaNe and HMM-based method performed better than the existing methods, demonstrating the power of these structural features in predicting nucleosome positions.</p> <p>Conclusions</p> <p>Our analysis revealed that DNA structures significantly contribute to nucleosome organization and influence chromatin structure and gene expression regulation. The results indicated that our proposed methods are effective in predicting nucleosome occupancy and positions and that these structural features are highly predictive of nucleosome organization.</p> <p>The implementation of our DLaNe method based on structural features is available online.</p

Nucleosomes affect local transformation efficiency

Genetic transformation is a natural process during which foreign DNA enters a cell and integrates into the genome. Apart from its relevance for horizontal gene transfer in nature, transformation is also the cornerstone of today's recombinant gene technology. Despite its importance, relatively little is known about the factors that determine transformation efficiency. We hypothesize that differences in DNA accessibility associated with nucleosome positioning may affect local transformation efficiency. We investigated the landscape of transformation efficiency at various positions in the Saccharomyces cerevisiae genome and correlated these measurements with nucleosome positioning. We find that transformation efficiency shows a highly significant inverse correlation with relative nucleosome density. This correlation was lost when the nucleosome pattern, but not the underlying sequence was changed. Together, our results demonstrate a novel role for nucleosomes and also allow researchers to predict transformation efficiency of a target region and select spots in the genome that are likely to yield higher transformation efficiency

Nucleosome positioning: resources and tools online

Nucleosome positioning is an important process required for proper genome packing and its accessibility to execute the genetic program in a cell-specific, timely manner. In the recent years hundreds of papers have been devoted to the bioinformatics, physics and biology of nucleosome positioning. The purpose of this review is to cover a practical aspect of this field, namely, to provide a guide to the multitude of nucleosome positioning resources available online. These include almost 300 experimental datasets of genome-wide nucleosome occupancy profiles determined in different cell types and more than 40 computational tools for the analysis of experimental nucleosome positioning data and prediction of intrinsic nucleosome formation probabilities from the DNA sequence. A manually curated, up to date list of these resources will be maintained at http://generegulation.info

Short Synthetic Terminators for Assembly of Transcription Units in vitro and Stable Chromosomal Integration in Yeast S. cerevisiae

The authors thank the assistance of the Iain Fraser Cytometry Centre at the University of Aberdeen. We also thank Claire MacGregor, Diane Massie and Yvonne Turnbull for technical assistance, Alexander Lorenz and Ryohei Sekido for critical reading of the manuscript and Richard Newton for preliminary results. This work was supported by Scottish Universities Life Sciences Alliance (SULSA).Peer reviewedPostprin

Computational prediction of Ds transposon insertion sites in plants using DNA structural features

Transposons, with the ability to integrate into new positions in the genome, can disrupt a gene\u27s function and thereby have been utilized as tools for genome mutagenesis. Critical to improving efficiency of such applications is to elucidate the patterns and preferences of insertion sites selection. We here focus on understanding target site selection of transposon Ac/Ds, one of the best-characterized transposon systems in plants, by exploring various DNA features and predicting insertion sites. A package named DnaFVP (DNA Feature Calculation, Visualization and Vector Preparation) was first developed for calculation, visualization and analysis of various DNA features, including nucleotide sequence features and a broad list of structural/physical properties. In addition, this package allows data preparation prior to calculating features and/or preparation of feature vectors for machine learning. It is developed for building a semi-automatic pipeline to explore various DNA features of any collection of genomic DNA sequences of interest and to prepare feature vectors for further machine learning. By use of combined nucleotide and structural features with application of the DnaFVP package, we prepared various feature vectors and predicted Ds insertion sites for machine learning. Training datasets included well-evidenced Ds insertion events (1605 events in maize and 2078 events in Arabidopsis) as positive datasets and 2000 random sampled genomic coordinates in genic regions from maize and Arabidopsis as negative datasets. An ROC (Receiver Operating Characteristic) of 0.77 in maize, 0.85 in Arabidopsis, and 0.82 in a combined dataset of maize and Arabidopsis have been achieved. One initially tested dataset in maize shows interesting results. Our prediction may provide further insight to the Ac/Ds transposition mechanism, and facilitate the ease of targeted mutagenesis and gene delivery mediated by transposons

Nucleosome Positioning and Its Role in Gene Regulation in Yeast

Nucleosome, composed of a 147-bp segment of DNA helix wrapped around a histone protein octamer, serves as the basic unit of chromatin. Nucleosome positioning refers to the relative position of DNA double helix with respect to the histone octamer. The positioning has an important role in transcription, DNA replication and other DNA transactions since packing DNA into nucleosomes occludes the binding site of proteins. Moreover, the nucleosomes bear histone modifications thus having a profound effect in regulation. Nucleosome positioning and its roles are extensively studied in model organism yeast. In this chapter, nucleosome organization and its roles in gene regulation are reviewed. Typically, nucleosomes are depleted around transcription start sites (TSSs), resulting in a nucleosome-free region (NFR) that is flanked by two well-positioned H2A.Z-containing nucleosomes. The nucleosomes downstream of the TSS are equally spaced in a nucleosome array. DNA sequences, especially 10–11 bp periodicities of some specific dinucleotides, partly determine the nucleosome positioning. Nucleosome occupancy can be determined with high throughput sequencing techniques. Importantly, nucleosome positions are dynamic in different cell types and different environments. Histones depletions, histones mutations, heat shock and changes in carbon source will profoundly change nucleosome organization. In the yeast cells, upon mutating the histones, the nucleosomes change drastically at promoters and the highly expressed genes, such as ribosome genes, undergo more change. The changes of nucleosomes tightly associate the transcription initiation, elongation and termination. H2A.Z is contained in the +1 and −1 nucleosomes and thus in transcription. Chaperon Chz1 and elongation factor Spt16 function in H2A.Z deposition on chromatin. The chapter covers the basic concept of nucleosomes, nucleosome determinant, the techniques of mapping nucleosomes, nucleosome alteration upon stress and mutation, and Htz1 dynamics on chromatin

Safety quantification in gene editing experiments using machine learning on rationally designed feature spaces

With ongoing development of the CRISPR/Cas programmable nuclease system, applications in the area of \textit{in vivo} therapeutic gene editing are increasingly within reach. However, non-negligible off-target effects remain a major concern for clinical applications. Even though a multitude of off-target cleavage datasets have been published, a comprehensive, transparent overview tool has not yet been established. The first part of this thesis presents the creation of crisprSQL (http://www.crisprsql.com), a large, diverse, interactive and bioinformatically enhanced collection of CRISPR/Cas9 off-target cleavage studies aimed at enriching the fields of cleavage profiling, gene editing safety analysis and transcriptomics. Having established this data source, we use it to train novel deep learning algorithms and explore feature encodings for off-target prediction, systematically sampling the resulting model space in order to find optimal models and inform future modelling efforts. We lay emphasis on physically informed features which capture the biological environment of the cleavage site, hence terming our approach piCRISPR. We find that our best-performing model highlights the importance of sequence context and chromatin accessibility for cleavage prediction and compares favourably with state-of-the-art prediction performance. We further show that our novel, environmentally sensitive features are crucial to accurate prediction on sequence-identical locus pairs, making them highly relevant for clinical guide design. We then turn our attention to the cell-intrinsic repair mechanisms that follow CRISPR/Cas-induced cleavage and provide a prediction algorithm for the outcome genotype distribution based on thermodynamic features of the DNA repair process. In a pioneering approach, we utilise structural calculations for the generation of these features and show that this novel approach surpasses published outcome prediction algorithms within our testing regime. Through interpretation of the trained model, we elucidate the thermodynamic factors driving DNA repair and provide a computational tool that allows experts to assess the severity of the genotypic changes predicted for a given edit. Together, these efforts provide a comprehensive, one-stop computational source to assess and improve CRISPR/Cas9 gene editing safety

Nucleosomes in gene regulation: theoretical approaches

This work reviews current theoretical approaches of biophysics and bioinformatics for the description of nucleosome arrangements in chromatin and transcription factor binding to nucleosomal organized DNA. The role of nucleosomes in gene regulation is discussed from molecular-mechanistic and biological point of view. In addition to classical problems of this field, actual questions of epigenetic regulation are discussed. The authors selected for discussion what seem to be the most interesting concepts and hypotheses. Mathematical approaches are described in a simplified language to attract attention to the most important directions of this field