Biology subjects

Picard, F.

Publications and source records attributed to Picard, F..

4 recordsLinked to original sources

bioRxiv2018

Evolution of Replication Origins in Vertebrate Genomes: Rapid Turnover Despite Selective Constraints.

BackgroundThe replication programme of vertebrate genomes is driven by the chro-mosomal distribution and timing of activation of tens of thousands of replication origins. Genome-wide studies have shown the frequent association of origins with promoters and CpG islands, and their enrichment in G-quadruplex sequence motifs (G4). However, the genetic determinants driving their activity remain poorly understood. To gain insight on the functional constraints operating on replication origins and their spatial distribution, we conducted the first evolutionary comparison of genome-wide origins maps across vertebrates.\n\nResultsWe generated a high resolution genome-wide map of chicken replication origins (the first of a bird genome), and performed an extensive comparison with human and mouse maps. The analysis of intra-species polymorphism revealed a strong depletion of genetic diversity on an ~ 40 bp region centred on the replication initiation loci. Surprisingly, this depletion in genetic diversity was not linked to the presence of G4 motifs, nor to the association with promoters or CpG islands. In contrast, we also showed that origins experienced a rapid turnover during vertebrates evolution, since pairwise comparisons of origin maps revealed that only 4 to 24% of them were conserved between any two species.\n\nConclusionsThis study unravels the existence of a novel genetic determinant of replication origins, the precise functional role of which remains to be determined. Despite the importance of replication initiation activity for the fitness of organisms, the distribution of replication origins along vertebrate chromosomes is highly flexible.

evolutionary biology↗

bioRxiv2017

Modeling Spatial Genomic Interactions with the Hawkes model

The spatial localization of many DNA-protein interactions is now available thanks to the development of ChIP-Seq, and their investigation calls for adapted statistical methods. Many methods were developped for peak calling, but few were proposed for the downstream analysis of peak-like data, whereas the spatial structure of such data may contain relevant biological information, like binding constraints for instance. Associations between the occurrences of two genomic features are usually assessed by overlaps, but here we propose a statistical model to precisely quantify the spatial interactions between the location of binding events. Our methodology relies on a multivariate spatial process, the Hawkes model, that can also be interpreted in terms of a graphical model to highlight spatial dependencies between genomic features. Using our method, we explore the chromatinian landscape of replication origins, and we highlight attractive and repulsive patterns that can be related to the regulation of the spatial program of replication.

bioinformatics↗

bioRxiv2017

Probabilistic Count Matrix Factorization for Single Cell Expression Data Analysis

The development of high throughput single-cell technologies now allows the investigation of the genome-wide diversity of transcription. This diversity has shown two faces: the expression dynamics (gene to gene variability) can be quantified more accurately, thanks to the measurement of lowly-expressed genes. Second, the cell-to-cell variability is high, with a low proportion of cells expressing the same gene at the same time/level. Those emerging patterns appear to be very challenging from the statistical point of view, especially to represent and to provide a summarized view of single-cell expression data. PCA is one of the most powerful frameworks to provide a suitable representation of high dimensional datasets, by searching for new axes catching the most variability in the data. Unfortunately, classical PCA is based on Euclidean distances and projections that work poorly in presence of over-dispersed counts showing zero-inflation. We propose a probabilistic Count Matrix Factorization (pCMF) approach for single-cell expression data analysis, that relies on a sparse Gamma-Poisson factor model. This hierarchical model is inferred using a variational EM algorithm. We show how this probabilistic framework induces a geometry that is suitable for single-cell data, and produces a compression of the data that is very powerful for clustering purposes. Our method is competed to other standard representation methods like ZIFA and t-SNE, and we illustrate its performance on simulated and publicly available data.

bioinformatics↗

bioRxiv2017

Maternal X chromosome upregulation in both sexes initiates dosage compensation evolution

This preprint has been reviewed and recommended by Peer Community In Evolutionary Biology (http://dx.doi.org/10.24072/pci.evolbiol.100044).\n\nSex chromosomes have repeatedly evolved from a pair of autosomes1. Consequently, X and Y chromosomes initially have similar gene content, but ongoing Y degeneration leads to reduced Y gene expression and eventual Y gene loss. The resulting imbalance in gene expression between Y genes and the rest of the genome is expected to reduce male fitness, especially when protein networks have components from both autosomes and sex chromosomes. A diverse set of dosage compensating mechanisms that alleviates these negative effects has been described in animals2-4. However, the early steps in the evolution of dosage compensation remain unknown and dosage compensation is poorly understood in plants5. Here we show a novel dosage compensation mechanism in the evolutionarily young XY sex determination system of the plant Silene latifolia. Genomic imprinting results in higher expression from the maternal X chromosome in both males and females. This compensates for reduced Y expression in males but results in X overexpression in females and may be detrimental. It could represent a transient early stage in the evolution of dosage compensation. Our finding has striking resemblance to the first stage proposed by Ohno for the evolution of X inactivation in mammals.

evolutionary biology↗