An mTRAN-mRNA interaction mediates mitochondrial translation initiation in plants

Plant mitochondria represent the largest group of respiring organelles on the planet. Plant mitochondrial messenger RNAs (mRNAs) lack Shine-Dalgarno-like ribosome-binding sites, so it is unknown how plant mitoribosomes recognize mRNA. We show that “mitochondrial translation factors” mTRAN1 and mTRAN2 are land plant–specific proteins, required for normal mitochondrial respiration chain biogenesis. Our studies suggest that mTRANs are noncanonical pentatricopeptide repeat (PPR)–like RNA binding proteins of the mitoribosomal “small” subunit. We identified conserved Adenosine (A)/Uridine (U)-rich motifs in the 5′ regions of plant mitochondrial mRNAs. mTRAN1 binds this motif, suggesting that it is a mitoribosome homing factor to identify mRNAs. We demonstrate that mTRANs are likely required for translation of all plant mitochondrial mRNAs. Plant mitochondrial translation initiation thus appears to use a protein-mRNA interaction that is divergent from bacteria or mammalian mitochondria

Ms.FPOP: An Exact and Fast Segmentation Algorithm With a Multiscale Penalty

38 pages, 17 figuresGiven a time series in $R^n$ with a piecewise constant mean and independent noises, we propose an exact dynamic programming algorithm to minimize a least square criterion with a multiscale penalty promoting well-spread changepoints. Such a penalty has been proposed in Verzelen et al. (2020), and it achieves optimal rates for changepoint detection and changepoint localization. Our proposed algorithm, named Ms.FPOP, extends functional pruning ideas of Rigaill (2015) and Maidstone et al. (2017) to multiscale penalties. For large signals, $n \geq 10^5$, with relatively few real changepoints, Ms.FPOP is typically quasi-linear and an order of magnitude faster than PELT. We propose an efficient C++ implementation interfaced with R of Ms.FPOP allowing to segment a profile of up to $n = 10^6$ in a matter of seconds. Finally, we illustrate on simple simulations that for large enough profiles ($n \geq 10^4$) Ms.FPOP using the multiscale penalty of Verzelen et al. (2020) is typically more powerfull than FPOP using the classical BIC penalty of Yao (1989)

Exponential Integral Solutions for Fixation Time in Wright-Fisher Model With Selection

In this work we derive new analytic expressions for fixation time in Wright-Fisher model with selection. The three standard cases for fixation are considered: fixation to zero, to one or both. Second order differential equations for fixation time are obtained by a simplified approach using only the law of total probability and Taylor expansions. The obtained solutions are given by a combination of exponential integral functions with elementary functions. We then state approximate formulas involving only elementary functions valid for small selection effects. The quality of our results are explored throughout an extensive simulation study. We show that our results approximate the discrete problem very accurately even for small population size (a few hundreds) and large selection coefficients

Increased peak detection accuracy in over-dispersed ChIP-seq data with supervised segmentation models

Abstract Background Histone modification constitutes a basic mechanism for the genetic regulation of gene expression. In early 2000s, a powerful technique has emerged that couples chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq). This technique provides a direct survey of the DNA regions associated to these modifications. In order to realize the full potential of this technique, increasingly sophisticated statistical algorithms have been developed or adapted to analyze the massive amount of data it generates. Many of these algorithms were built around natural assumptions such as the Poisson distribution to model the noise in the count data. In this work we start from these natural assumptions and show that it is possible to improve upon them. Results Our comparisons on seven reference datasets of histone modifications (H3K36me3 & H3K4me3) suggest that natural assumptions are not always realistic under application conditions. We show that the unconstrained multiple changepoint detection model with alternative noise assumptions and supervised learning of the penalty parameter reduces the over-dispersion exhibited by count data. These models, implemented in the R package CROCS ( https://github.com/aLiehrmann/CROCS ), detect the peaks more accurately than algorithms which rely on natural assumptions. Conclusion The segmentation models we propose can benefit researchers in the field of epigenetics by providing new high-quality peak prediction tracks for H3K36me3 and H3K4me3 histone modifications

DiffSegR: An RNA-Seq data driven method for differential expression analysis using changepoint detection

ABSTRACT To fully understand gene regulation, it is necessary to have a thorough understanding of both the transcriptome and the enzymatic and RNA-binding activities that shape it. While many RNA-Seq-based tools have been developed to analyze the transcriptome, most only consider the abundance of sequencing reads along annotated patterns (such as genes). These annotations are typically incomplete, leading to errors in the differential expression analysis. To address this issue, we present DiffSegR - an R package that enables the discovery of transcriptome-wide expression differences between two biological conditions using RNA-Seq data. DiffSegR does not require prior annotation and uses a multiple changepoints detection algorithm to identify the boundaries of differentially expressed regions in the per-base log2 fold change. In a few minutes of computation, DiffSegR could rightfully predict the role of chloroplast ribonuclease Mini-III in rRNA maturation and chloroplast ribonuclease PNPase in (3’/5’)-degradation of rRNA, mRNA, and tRNA precursors as well as intron accumulation. We believe DiffSegR will benefit biologists working on transcriptomics as it allows access to information from a layer of the transcriptome overlooked by the classical differential expression analysis pipelines widely used today. DiffSegR is available at https://aliehrmann.github.io/DiffSegR/index.html

Full Length Transcriptome Highlights the Coordination of Plastid Transcript Processing

International audiencePlastid gene expression involves many post-transcriptional maturation steps resulting in a complex transcriptome composed of multiple isoforms. Although short read RNA-seq has considerably improved our understanding of the molecular mechanisms controlling these processes, it is unable to sequence full-length transcripts. This information is however crucial when it comes to understand the interplay between the various steps of plastid gene expression. Here, the study of the Arabidopsis leaf plastid transcriptome using Nanopore sequencing showed that many splicing and editing events were not independent but co-occurring. For a given transcript, maturation events also appeared to be chronologically ordered with splicing happening after most sites are edited

DiffSegR: an RNA-seq data driven method for differential expression analysis using changepoint detection

International audienceAbstract To fully understand gene regulation, it is necessary to have a thorough understanding of both the transcriptome and the enzymatic and RNA-binding activities that shape it. While many RNA-Seq-based tools have been developed to analyze the transcriptome, most only consider the abundance of sequencing reads along annotated patterns (such as genes). These annotations are typically incomplete, leading to errors in the differential expression analysis. To address this issue, we present DiffSegR - an R package that enables the discovery of transcriptome-wide expression differences between two biological conditions using RNA-Seq data. DiffSegR does not require prior annotation and uses a multiple changepoints detection algorithm to identify the boundaries of differentially expressed regions in the per-base log2 fold change. In a few minutes of computation, DiffSegR could rightfully predict the role of chloroplast ribonuclease Mini-III in rRNA maturation and chloroplast ribonuclease PNPase in (3′/5′)-degradation of rRNA, mRNA and tRNA precursors as well as intron accumulation. We believe DiffSegR will benefit biologists working on transcriptomics as it allows access to information from a layer of the transcriptome overlooked by the classical differential expression analysis pipelines widely used today. DiffSegR is available at https://aliehrmann.github.io/DiffSegR/index.html

An mTRAN-mRNA interaction mediates mitochondrial translation initiation in plants

International audiencePlant mitochondria represent the largest group of respiring organelles on the planet. Plant mitochondrial messenger RNAs (mRNAs) lack Shine-Dalgarno-like ribosome-binding sites, so it is unknown how plant mitoribosomes recognize mRNA. We show that “mitochondrial translation factors” mTRAN1 and mTRAN2 are land plant–specific proteins, required for normal mitochondrial respiration chain biogenesis. Our studies suggest that mTRANs are noncanonical pentatricopeptide repeat (PPR)–like RNA binding proteins of the mitoribosomal “small” subunit. We identified conserved Adenosine (A)/Uridine (U)-rich motifs in the 5′ regions of plant mitochondrial mRNAs. mTRAN1 binds this motif, suggesting that it is a mitoribosome homing factor to identify mRNAs. We demonstrate that mTRANs are likely required for translation of all plant mitochondrial mRNAs. Plant mitochondrial translation initiation thus appears to use a protein-mRNA interaction that is divergent from bacteria or mammalian mitochondria