No abstract

Recent genome-wide maps of nucleosome positions in different eukaryotes revealed patterns around transcription start sites featuring a nucleosome-free region flanked by a periodic modulation of the nucleosome density. For Saccharomyces cerevisiae, the average in vivo pattern was previously shown to be quantitatively described by a "nucleosome gas" model based on the statistical positioning mechanism. However, this simple physical description is challenged by the fact that the pattern differs quantitatively between species and by recent experiments that appear incompatible with statistical positioning, indicating important roles for chromatin remodelers. We undertake a data-driven search for a unified physical model to describe the nucleosome patterns of 12 yeast species and also consider an extension of the model to capture remodeling effects. We are led to a nucleosome gas that takes into account nucleosome breathing, i.e., transient unwrapping of nucleosomal DNA segments. This known biophysical property of nucleosomes rationalizes a "pressure"-induced dependence of the effective nucleosome size that is suggested by the data. By fitting this model to the data, we find an average energy cost for DNA unwrapping consistent with previous biophysical experiments. Although the available data are not sufficient to reconstruct chromatin remodeling mechanisms, a minimal model extension by one mechanism yields an "active nucleosome gas" that can rationalize the behavior of systems with reduced histone-DNA ratio and remodeler knockouts. We therefore establish a basis for a physical description of nucleosome patterns that can serve as a null model for sequence-specific effects at individual genes and in models of transcription regulation.nucleosome maps | chromatin structure | quantitative biology

In bioinformatics, as well as other computationally intensive research fields, there is a need for workflows that can reliably produce consistent output, from known sources, independent of the software environment or configuration settings of the machine on which they are executed. Indeed, this is essential for controlled comparison between different observations and for the wider dissemination of workflows. However, providing this type of reproducibility and traceability is often complicated by the need to accommodate the myriad dependencies included in a larger body of software, each of which generally comes in various versions. Moreover, in many fields (bioinformatics being a prime example), these versions are subject to continual change due to rapidly evolving technologies, further complicating problems related to reproducibility. Here, we propose a principled approach for building analysis pipelines and managing their dependencies with GNU Guix. As a case study to demonstrate the utility of our approach, we present a set of highly reproducible pipelines called PiGx for the analysis of RNA sequencing, chromatin immunoprecipitation sequencing, bisulfite-treated DNA sequencing, and single-cell resolution RNA sequencing. All pipelines process raw experimental data and generate reports containing publication-ready plots and figures, with interactive report elements and standard observables. Users may install these highly reproducible packages and apply them to their own datasets without any special computational expertise beyond the use of the command line. We hope such a toolkit will provide immediate benefit to laboratory workers wishing to process their own datasets or bioinformaticians seeking to automate all, or parts of, their analyses. In the long term, we hope our approach to reproducibility will serve as a blueprint for reproducible workflows in other areas. Our pipelines, along with their corresponding documentation and sample reports, are available at http://bioinformatics.mdc-berlin.de/pigx

The first level of genome packaging in eukaryotic cells involves the formation of dense nucleosome arrays, with DNA coverage near 90% in yeasts. How cells achieve such high coverage within a short time, e.g. after DNA replication, remains poorly understood. It is known that random sequential adsorption of impenetrable particles on a line reaches high density extremely slowly, due to a jamming phenomenon. The nucleosome-shifting action of remodeling enzymes has been proposed as a mechanism to resolve such jams. Here, we suggest two biophysical mechanisms which assist rapid filling of DNA with nucleosomes, and we quantitatively characterize these mechanisms within mathematical models. First, we show that the ‘softness’ of nucleosomes, due to nucleosome breathing and stepwise nucleosome assembly, significantly alters the filling behavior, speeding up the process relative to ‘hard’ particles with fixed, mutually exclusive DNA footprints. Second, we explore model scenarios in which the progression of the replication fork could eliminate nucleosome jamming, either by rapid filling in its wake or via memory of the parental nucleosome positions. Taken together, our results suggest that biophysical effects promote rapid nucleosome filling, making the reassembly of densely packed nucleosomes after DNA replication a simpler task for cells than was previously thought.

Adsorption-desorption processes are ubiquitous in physics, chemistry, and biology. Models usually assume hard particles, but within the realm of soft matter physics the adsorbing particles are compressible. A minimal 1D model reveals that softness fundamentally changes the kinetics: Below the desorption time scale, a logarithmic increase of the particle density replaces the usual Rényi jamming plateau, and the subsequent relaxation to equilibrium can be nonmonotonic and much faster than for hard particles. These effects will impact the kinetics of self-assembly and reaction-diffusion processes.