Caroline Bérard
Agro ParisTech
9 Papers
Caroline Bérard is an academic researcher from Agro ParisTech. The author has contributed to research in topics: Parametric family & Tiling array. The author has an hindex of 5, co-authored 7 publications.
Chat about Author
Papers
Integrative epigenomic mapping defines four main chromatin states in Arabidopsis.
François Roudier,Ikhlak Ahmed,Caroline Bérard,Alexis Sarazin,Tristan Mary-Huard,Sandra Cortijo,Daniel Bouyer,Erwann Caillieux,Evelyne Duvernois-Berthet,Liza Al-Shikhley,Laurène Giraut,Barbara Després,Stéphanie Drevensek,Fredy Barneche,Sandra Dèrozier,Véronique Brunaud,Sébastien Aubourg,Arp Schnittger,Chris Bowler,Marie-Laure Martin-Magniette,Marie-Laure Martin-Magniette,Stéphane Robin,Michel Caboche,Vincent Colot +23 more
TL;DR: This first combinatorial view of the Arabidopsis epigenome points to simple principles of organization as in metazoans and provides a framework for further studies of chromatin‐based regulatory mechanisms in plants.
Unsupervised classification for tiling arrays: ChIP-chip and transcriptome.
Caroline Bérard,Marie-Laure Martin-Magniette,Véronique Brunaud,Sébastien Aubourg,Stéphane Robin +4 more
TL;DR: This work proposes to consider both questions simultaneously as an unsupervised classification problem by modeling the joint distribution of the two conditions by accounting for all available information on the probes as well as biological knowledge such as annotation and spatial dependence between probes.
UMI-Gen: A UMI-based read simulator for variant calling evaluation in paired-end sequencing NGS libraries
Vincent Sater,Pierre-Julien Viailly,Thierry Lecroq,Philippe Ruminy,Caroline Bérard,Élise Prieur-Gaston,Fabrice Jardin +6 more
TL;DR: UMI-Gen, a UMI-based read simulator for targeted sequencing paired-end data that generates reference reads covering the targeted regions at a user customizable depth and modifies the generated reads to mimic real biological data.
6
•Posted Content
Hidden Markov Models with mixtures as emission distributions
TL;DR: In this article, a semiparametric model where the emission distributions are a mixture of parametric distributions is proposed to estimate the number of hidden states, and a simulation study is carried out both to determine the best combination between merging criteria and the model selection criteria and to evaluate the accuracy of classification.
1
•Posted Content
Unsupervised Classification for Tiling Arrays: ChIP-chip and Transcriptome
Caroline Bérard,Marie-Laure Martin-Magniette,Véronique Brunaud,Sébastien Aubourg,Stéphane Robin +4 more
TL;DR: In this paper, an unsupervised classification of the expression difference between two conditions or the detection of transcribed regions is considered. But the authors do not consider the biological knowledge on the probes.