Landscape of the Noncoding Transcriptome Response of Two Arabidopsis Ecotypes to Phosphate Starvation

Blein, Thomas; Balzergue, Coline; Roulé, Thomas; Gabriel, Marc; Scalisi, Laetitia; François, Tracy; Sorin, Céline; Christ, Aurélie; Godon, Christian; Delannoy, Étienne; Martin‐Magniette, Marie‐Laure; Nussaume, Laurent; Hartmann, Caroline; Gautheret, Daniel; Desnos, Thierry; Crespi, Martín

doi:10.1104/pp.20.00446

Cited by 26 publications

(28 citation statements)

References 65 publications

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“…The application of our model to a series of datasets gathered from public sources, including GSE137631 ( 27 ), GSE141027 ( 28 ), GSE99374 ( 29 ), GSE151005 ( 30 ), Mangiola_2018 ( 26 ) and Atkins_2019 ( 31 ), revealed that a median of 10.6%, 3.3% and 10.4% of differentially abundant transcripts inferred by edgeR, the robust edgeR implementation (using estimateGLMRobustDisp) and DESeq2 respectively (using recommended analysis pipelines for data filtering, normalisation and modelling ( 32 , 33 )) had inflated statistics caused by the presence of outliers (Figure 4A ). The analyses were performed using tidybulk framework ( 34 ) and broom ( 35 ).…”

Section: Resultsmentioning

confidence: 99%

Probabilistic outlier identification for RNA sequencing generalized linear models

Mangiola

Thomas

Modrák

et al. 2021

NAR Genomics and Bioinformatics

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Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers.

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Section: Resultsmentioning

confidence: 99%

Probabilistic outlier identification for RNA sequencing generalized linear models

Mangiola

Thomas

Modrák

et al. 2021

NAR Genomics and Bioinformatics

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“…The exploration of the noncoding transcriptome in Arabidopsis recently served to identify ecotype-specific lncRNA-mediated responses to the environment 50 . It was suggested that the noncoding genome may participate in multiple mechanisms involved in ecotype adaptation.…”

Section: Discussionmentioning

confidence: 99%

The lncRNA MARS modulates the epigenetic reprogramming of the marneral cluster in response to ABA

Roulé¹,

Christ²,

Hussain³

et al. 2022

Molecular Plant

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Clustered organization of biosynthetic non-homologous genes is emerging as a characteristic feature of plant genomes. The co-regulation of clustered genes seems to largely depend on epigenetic reprogramming and three-dimensional chromatin conformation. Here we identified the long noncoding RNA (lncRNA) MARneral Silencing (MARS), localized inside the Arabidopsis marneral cluster, and which controls the local epigenetic activation of its surrounding region in response to ABA. MARS modulates the POLYCOMB REPRESSIVE COMPLEX 1 (PRC1) component LIKE-HETEROCHROMATIN PROTEIN 1 (LHP1) binding throughout the cluster in a dose-dependent manner, determining H3K27me3 deposition and chromatin condensation. In response to ABA, MARS decoys LHP1 away from the cluster and promotes the formation of a chromatin loop bringing together the MARNERAL SYNTHASE 1 (MRN1) locus and a distal ABA-responsive enhancer. The enrichment of co-regulated lncRNAs in clustered metabolic genes suggests that the acquisition of noncoding transcriptional units constitute an additional regulatory layer driving the evolution of biosynthetic pathways.

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“…CircRNA numbers varied considerably in different lotus rhizomes, ranging from 186 to 437, and most of the circRNAs were exonic, which is concordant with those in Arabidopsis and wheat [ [2] , [48] ]. Furthermore, the expression files of ncRNAs indicated that many ncRNAs exhibited extensive expression divergence between two lotus ecotypes, and circRNAs are more highly accession-specific or noisier in expression than linear RNA transcripts [ [2] , [49] ]. More importantly, the biological functions of genes, targeted by differentially or accession-specifically expressed ncRNAs in rhizomes, were significantly enriched in plant growth, membrane trafficking, carbohydrate metabolism, and plant hormone signaling.…”

Section: Discussionmentioning

confidence: 99%

Expression rewiring and methylation of non-coding RNAs involved in rhizome phenotypic variations of lotus ecotypes

Zhang

Chen

et al. 2022

Computational and Structural Biotechnology Journal

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Landscape of the Noncoding Transcriptome Response of Two Arabidopsis Ecotypes to Phosphate Starvation

Cited by 26 publications

References 65 publications

Probabilistic outlier identification for RNA sequencing generalized linear models

Probabilistic outlier identification for RNA sequencing generalized linear models

The lncRNA MARS modulates the epigenetic reprogramming of the marneral cluster in response to ABA

Expression rewiring and methylation of non-coding RNAs involved in rhizome phenotypic variations of lotus ecotypes

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