Reference : Identification of genes under dynamic post-transcriptional regulation from time-serie...
Scientific journals : Article
Life sciences : Biochemistry, biophysics & molecular biology
http://hdl.handle.net/10993/39449
Identification of genes under dynamic post-transcriptional regulation from time-series epigenomic data
English
Becker, Julia Christina mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
Gerard, Déborah mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Ginolhac, Aurélien mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
Sauter, Thomas mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
Sinkkonen, Lasse mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
2-May-2019
Epigenomics
Future Medicine
Yes (verified by ORBilu)
International
1750-1911
1750-192X
London
United Kingdom
[en] chromatin ; microrna ; linear regression
[en] Aim: Prediction of genes under dynamic post-transcriptional regulation from epigenomic data. Materials & methods: We used time-series profiles of chromatin immunoprecipitation-seq data of histone modifications from differentiation of mesenchymal progenitor cells toward adipocytes and osteoblasts to predict gene expression levels at five time points in both lineages and estimated the deviation of those predictions from the RNA-seq measured expression levels using linear regression. Results & conclusion: The genes with biggest changes in their estimated stability across the time series are enriched for noncoding RNAs and lineage-specific biological processes. Clustering mRNAs according to their stability dynamics allows identification of post-transcriptionally coregulated mRNAs and their shared regulators through sequence enrichment analysis. We identify miR-204 as an early induced adipogenic microRNA targeting Akr1c14 and Il1rl1.
Fonds National de la Recherche - FnR
Researchers
http://hdl.handle.net/10993/39449
10.2217/epi-2018-0084
https://www.futuremedicine.com/doi/10.2217/epi-2018-0084
The original publication is available at www.futuremedicine.com as an open access publication.
FnR ; FNR11191283 > Thomas Sauter > AlgoReCell > Computational Models and Algorithms for Predicting Cell Reprogramming Determinants with High Efficiency and High Fidelity > 01/03/2017 > 29/02/2020 > 2016

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