bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/064113

Influence of rotational nucleosome positioning on transcription start site selection in animals promoters

Rene Dreos·Giovanna Ambrosini·Philipp Bucher
View original source ↗Export metadata ↓

Abstract

The recruitment of RNA-Pol-II to the transcription start site (TSS) is an important step in gene regulation in all organisms. Core promoter elements (CPE) are conserved sequence motifs that guide Pol-II to the TSS by interacting with specific transcription factors (TFs). However, only a minority of animal promoters contains CPEs. It is still unknown how Pol-II selects the TSS in their absence. Here we present a comparative analysis of promoters sequence composition and chromatin architecture in five eukaryotic model organisms, which shows the presence of common and unique DNA-encoded features used to organize chromatin. Analysis of Pol-II initiation patterns uncovers that, in the absence of certain CPEs, there is a strong correlation between the spread of initiation and the intensity of the 10 bp periodic signal in the nearest downstream nucleosome. Moreover, promoters primary and secondary initiation sites show a characteristic 10 bp periodicity in the absence of CPEs. We also show that DNA natural variants in the region immediately downstream the TSS are able to affect both the nucleosome-DNA affinity and Pol-II initiation pattern. These findings support the notion that, in addition to CPEs mediated selection, sequence-induced nucleosome positioning could be a common and conserved mechanismof TSS selection in animals.\n\nAuthor SummaryGene transcription is a complex process that starts with the recruitment and positioning of Pol-II enzyme at the transcription start site (TSS). Specific promoter sequences, known as core promoter elements (CPEs) facilitate this process. Surprisingly, only a fraction of promoters contain them. It is still unknown how Pol-II choses the start site in their absence. Arecently proposed alternative mechanism implicates positioned nucleosomes in the TSS selection. Here, we provide new evidence of the existence of such mechanism with a comparative analysis of promoters features across the animal kingdom. We analysed the promoters DNA sequence composition in 5 organisms and found conserved and unique consensus sequencesused to organize chromatin in the region of the first nucleosome downstream the TSS (N+1). Moreover, we found that all organisms show a strong correlation between the spread of Pol-II initiation and the strength of the DNA-encoded signal in the N+1 region. A detailed analysis of Pol-II initiation sites reveals also the presence of a 10 bp periodicity that is correlated with the intensity of the DNA signal in the N+1 region. Importantly, we report that genetic variants that alter the DNA-nucleosome affinity in that region alter Pol-II initiation spread as well.

Source connections

Explore related subjects

BioinformaticsPreprint

Keep this discovery

BibTeXRIS

Rene Dreos, Giovanna Ambrosini, Philipp Bucher. 2016-07-15. Influence of rotational nucleosome positioning on transcription start site selection in animals promoters. https://doi.org/10.1101/064113

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2016

iCARE: An R Package to Build and Apply Absolute Risk Models

This report describes a R package, called the Individualized Coherent Absolute Risk Estimation (iCARE) tool, that allows researchers to build and evaluate models for absolute risk and apply them to estimate an individuals risk of developing disease during a specified time interval based on a set of user defined input parameters. An attractive feature of the software is that it gives users flexibility to update models rapidly based on new knowledge on risk factors and tailor models to different populations by specifying three input arguments: (1) a model for relative risk, (2) an age-specific disease incidence rate, (3) the distribution of risk factors for the population of interest. The tool can handle missing information on risk factors for individuals for whom risks are to be predicted using a coherent approach where all estimates are derived from a single model after appropriate model averaging. The software allows single nucleotide polymorphisms (SNPs) to be incorporated into the model using published odds ratios and allele frequencies. The validation component of the software implements the methods for evaluation of model calibration, discrimination and risk-stratification based on independent validation datasets. We provide an illustration of the utility of iCARE for building, validating and applying absolute risk models using breast cancer as an example.

Bioinformatics↗
bioRxiv2016

deSPI: efficient classification of metagenomic reads with lightweight de Bruijn graph-based reference indexing

SummaryIn metagenomic studies, fast and effective tools are on wide demand to implement taxonomy classification for upto billions of reads. Herein, we propose deSPI, a novel read classification method that classifies reads by recognizing and analyzing the matches between reads and reference with de Bruijn graph-based lightweight reference indexing. deSPI has faster speed with relatively small memory footprint, meanwhile, it can also achieve higher or similar sensitivity and accuracy.\n\nAvailabilitythe C++ source code of deSPI is available at https://github.com/hitbc/deSPI\n\nContactydwang@hit.edu.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

Bioinformatics↗
bioRxiv2016

De novo assembly of viral quasispecies using overlap graphs

A viral quasispecies, the ensemble of viral strains populating an infected person, can be highly diverse. For optimal assessment of virulence, pathogenesis and therapy selection, determining the haplotypes of the individual strains can play a key role. As many viruses are subject to high mutation and recombination rates, high-quality reference genomes are often not available at the time of a new disease outbreak. We present SAVAGE, a computational tool for reconstructing individual haplotypes of intrahost virus strains without the need for a high-quality reference genome. SAVAGE makes use of either FM-index based data structures or ad-hoc consensus reference sequence for constructing overlap graphs from patient sample data. In this overlap graph, nodes represent reads and/or contigs, while edges reflect that two reads/contigs, based on sound statistical considerations, represent identical haplotypic sequence. Following an iterative scheme, a new overlap assembly algorithm that is based on the enumeration of statistically well-calibrated groups of reads/contigs then efficiently reconstructs the individual haplotypes from this overlap graph. In benchmark experiments on simulated and on real deep coverage data, SAV-AGE drastically outperforms generic de novo assemblers as well as the only specialized de novo viral quasispecies assembler available so far. When run on ad-hoc consensus reference sequence, SAVAGE performs very favorably in comparison with state-of-the-art reference genome guided tools. We also apply SAVAGE on two deep coverage samples of patients infected by the Zika and the hepatitis C virus, respectively, which sheds light on the genetic structures of the respective viral quasispecies.

Bioinformatics↗