Reference : Surprisal analysis of genome-wide transcript profiling identifies differentially expr...
Scientific journals : Article
Life sciences : Multidisciplinary, general & others
Surprisal analysis of genome-wide transcript profiling identifies differentially expressed genes and pathways associated with four growth conditions in the microalga Chlamydomonas
Bogaert, K. A. [Theoretical Physical Chemistry, UR MOLSYS, University of Liège, Liège, Belgium]
Manoharan-Basil, S. S. [Genetics and Physiology of Microalgae, UR InBios, University of Liège, Liège, Belgium]
Perez, E. [Genetics and Physiology of Microalgae, UR InBios, University of Liège, Liège, Belgium]
Levine, Raphaël David [Fritz Haber Research Center for Molecular Dynamics, Institute of Chemistry, Hebrew University of Jerusalem, Jerusalem, Israel, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States]
Remacle, Françoise mailto [Université de Liège - ULiège > Département de chimie (sciences) > Laboratoire de chimie physique théorique >]
Remacle, Claire mailto [Université de Liège - ULiège > Département des sciences de la vie > Génétique et physiologie des microalgues >]
Public Library of Science
Yes (verified by ORBi)
[en] The usual cultivation mode of the green microalga Chlamydomonas is liquid medium and light. However, the microalga can also be grown on agar plates and in darkness. Our aim is to analyze and compare gene expression of cells cultivated in these different conditions. For that purpose, RNA-seq data are obtained from Chlamydomonas samples of two different labs grown in four environmental conditions (agar@light, agar@dark, liquid@light, liquid@dark). The RNA seq data are analyzed by surprisal analysis, which allows the simultaneous meta-analysis of all the samples. First we identify a balance state, which defines a state where the expression levels are similar in all the samples irrespectively of their growth conditions, or lab origin. In addition our analysis identifies additional constraints needed to quantify the deviation with respect to the balance state. The first constraint differentiates the agar samples versus the liquid ones; the second constraint the dark samples versus the light ones. The two constraints are almost of equal importance. Pathways involved in stress responses are found in the agar phenotype while the liquid phenotype comprises ATP and NADH production pathways. Remodeling of membrane is suggested in the dark phenotype while photosynthetic pathways characterize the light phenotype. The same trends are also present when performing purely statistical analysis such as K-means clustering and differentially expressed genes. © 2018 Bogaert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
FP7 ; 618024 - BAMBI - Bottom-up Approaches to Machines dedicated to Bayesian Inference

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