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Your search keyword '"Ivo G, Gut"' showing total 6 results
Start Over Author "Ivo G, Gut" Journal nature communications
6 results on '"Ivo G, Gut"'

1. Impact of DNA methylation on 3D genome structure

Author

Diana Buitrago, Mireia Labrador, Juan Pablo Arcon, Rafael Lema, Oscar Flores, Anna Esteve-Codina, Julie Blanc, Nuria Villegas, David Bellido, Marta Gut, Pablo D. Dans, Simon C. Heath, Ivo G. Gut, Isabelle Brun Heath, and Modesto Orozco

Subjects
Science
Abstract

Multi-layered epigenetic regulation in higher eukaryotes makes it challenging to disentangle the individual effects of modifications on chromatin structure and function. Here, the authors expressed mammalian DNA methyltransferases in yeast, which have no DNA methylation, to show that methylation has intrinsic effects on chromatin structure.

Published
2021
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2. Framework for quality assessment of whole genome cancer sequences

Author

Justin P. Whalley, Ivo Buchhalter, Esther Rheinbay, Keiran M. Raine, Miranda D. Stobbe, Kortine Kleinheinz, Johannes Werner, Sergi Beltran, Marta Gut, Daniel Hübschmann, Barbara Hutter, Dimitri Livitz, Marc D. Perry, Mara Rosenberg, Gordon Saksena, Jean-Rémi Trotta, Roland Eils, Daniela S. Gerhard, Peter J. Campbell, Matthias Schlesner, and Ivo G. Gut

Subjects
Science
Abstract

Working with cancer genomes from multiple projects can increase investigative power, but quality of sequences can vary. Here, the authors present a framework for comparing whole genome sequencing quality to help researchers guide downstream analyses and exclude poor quality samples.

Published
2020
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3. Partially methylated domains are hypervariable in breast cancer and fuel widespread CpG island hypermethylation

Author

Arie B. Brinkman, Serena Nik-Zainal, Femke Simmer, F. Germán Rodríguez-González, Marcel Smid, Ludmil B. Alexandrov, Adam Butler, Sancha Martin, Helen Davies, Dominik Glodzik, Xueqing Zou, Manasa Ramakrishna, Johan Staaf, Markus Ringnér, Anieta Sieuwerts, Anthony Ferrari, Sandro Morganella, Thomas Fleischer, Vessela Kristensen, Marta Gut, Marc J. van de Vijver, Anne-Lise Børresen-Dale, Andrea L. Richardson, Gilles Thomas, Ivo G. Gut, John W. M. Martens, John A. Foekens, Michael R. Stratton, and Hendrik G. Stunnenberg

Subjects
Science
Abstract

In cancer, global DNA methylation loss and CpG island hypermethylation are commonly observed. Here, in breast cancer the authors find that hyper-variability of partially methylated domains is the prime source of DNA methylation variation and that these domains fuel CpG island hypermethylation.

Published
2019
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4. Increased DNA methylation variability in type 1 diabetes across three immune effector cell types

Author

Dirk S. Paul, Andrew E. Teschendorff, Mary A.N. Dang, Robert Lowe, Mohammed I. Hawa, Simone Ecker, Huriya Beyan, Stephanie Cunningham, Alexandra R. Fouts, Anita Ramelius, Frances Burden, Samantha Farrow, Sophia Rowlston, Karola Rehnstrom, Mattia Frontini, Kate Downes, Stephan Busche, Warren A. Cheung, Bing Ge, Marie-Michelle Simon, David Bujold, Tony Kwan, Guillaume Bourque, Avik Datta, Ernesto Lowy, Laura Clarke, Paul Flicek, Emanuele Libertini, Simon Heath, Marta Gut, Ivo G Gut, Willem H. Ouwehand, Tomi Pastinen, Nicole Soranzo, Sabine E. Hofer, Beate Karges, Thomas Meissner, Bernhard O. Boehm, Corrado Cilio, Helena Elding Larsson, Åke Lernmark, Andrea K. Steck, Vardhman K. Rakyan, Stephan Beck, and R. David Leslie

Subjects
Science
Abstract

The incidence of type 1 diabetes is increasing, potentially implicating non-genetic factors. Here the authors conduct an epigenome-wide association study in disease-discordant twins and find increased DNA methylation variability at genes associated with immune cell metabolism and the cell cycle.

Published
2016
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5. Information recovery from low coverage whole-genome bisulfite sequencing

Author

Emanuele Libertini, Simon C. Heath, Rifat A. Hamoudi, Marta Gut, Michael J. Ziller, Agata Czyz, Victor Ruotti, Hendrik G. Stunnenberg, Mattia Frontini, Willem H. Ouwehand, Alexander Meissner, Ivo G. Gut, and Stephan Beck

Subjects
Science
Abstract

Here, Libertini and colleagues devise a computation tool that can analyze whole-genome bisulfite sequencing (WGBS) data to recover of ∼30% of the lost differential methylation position information. They use COMETgazer and COMETvintage to analyze 13 diffferent methylome data to demonstrate their performance.

Published
2016
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6. Information recovery from low coverage whole-genome bisulfite sequencing

Author

Emanuele, Libertini, Simon C, Heath, Rifat A, Hamoudi, Marta, Gut, Michael J, Ziller, Agata, Czyz, Victor, Ruotti, Hendrik G, Stunnenberg, Mattia, Frontini, Willem H, Ouwehand, Alexander, Meissner, Ivo G, Gut, and Stephan, Beck

Subjects
Genotype, Haplotypes, Whole Genome Sequencing, Genome, Human, food and beverages, Computational Biology, Humans, Reproducibility of Results, Sulfites, CpG Islands, DNA Methylation, Algorithms, Article
Abstract

The cost of whole-genome bisulfite sequencing (WGBS) remains a bottleneck for many studies and it is therefore imperative to extract as much information as possible from a given dataset. This is particularly important because even at the recommend 30X coverage for reference methylomes, up to 50% of high-resolution features such as differentially methylated positions (DMPs) cannot be called with current methods as determined by saturation analysis. To address this limitation, we have developed a tool that dynamically segments WGBS methylomes into blocks of comethylation (COMETs) from which lost information can be recovered in the form of differentially methylated COMETs (DMCs). Using this tool, we demonstrate recovery of ∼30% of the lost DMP information content as DMCs even at very low (5X) coverage. This constitutes twice the amount that can be recovered using an existing method based on differentially methylated regions (DMRs). In addition, we explored the relationship between COMETs and haplotypes in lymphoblastoid cell lines of African and European origin. Using best fit analysis, we show COMETs to be correlated in a population-specific manner, suggesting that this type of dynamic segmentation may be useful for integrated (epi)genome-wide association studies in the future., Here, Libertini and colleagues devise a computation tool that can analyze whole-genome bisulfite sequencing (WGBS) data to recover of ∼30% of the lost differential methylation position information. They use COMETgazer and COMETvintage to analyze 13 diffferent methylome data to demonstrate their performance.

Published
2015

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