ArticlePloS one2018
Surprisal analysis of genome-wide transcript profiling identifies differentially expressed genes and pathways associated with four growth conditions in the microalga Chlamydomonas.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
- SurprisalAnalysis: an open-source software for information-theoretic analysis of gene expression.Bioinformatics advances · 2025Article
- Type II metacaspase mediates light-dependent programmed cell death in Chlamydomonas reinhardtii.Plant physiology · 2024Article
- A novel estimator of the interaction matrix in Graphical Gaussian Model of omics data using the entropy of non-equilibrium systems.Bioinformatics (Oxford, England) · 2021Article
- TMEA: A Thermodynamically Motivated Framework for Functional Characterization of Biological Responses to System Acclimation.Entropy (Basel, Switzerland) · 2020Article
- Metabolic, Physiological, and Transcriptomics Analysis of Batch Cultures of the Green MicroalgaCells · 2019Article
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Authors and funding
6 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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