The Use of Nanopore Sequencing to Analyze the Chloroplast Transcriptome Part II: Bioinformatic Analyzes and Virtual RNA Blots.

Nanopore sequencing of full-length cDNAs offers unprecedented details of the plastid RNA metabolism. After the generation of the nanopore reads, several bioinformatic steps are required to analyze the data. In this chapter, we describe in a few simple command lines the processing and mapping of the reads as well as the generation of virtual Northern blots as a simple and familiar way to visualize Nanopore sequencing data.

The Use of Nanopore Sequencing to Analyze the Chloroplast Transcriptome Part I: Library Preparation.

Global understanding of plastid gene expression has always been impaired by its complexity. RNA splicing, editing, and intercistronic processing create multiple transcripts isoforms that can hardly be resolved using traditional molecular biology techniques. During the last decade, the wide adoption of RNA-seq-based techniques has, however, allowed an unprecedented understanding of all the different steps of chloroplast gene expression, from transcription to translation. Current strategies are nonetheless unable to identify and quantify full length transcripts isoforms, a limitation that can now be overcome using Nanopore Sequencing. We here provide a complete protocol to produce, from total leaf RNA, cDNA libraries ready for Nanopore sequencing. While most Nanopore protocols take advantage of the mRNA polyA tail we here first ligate an RNA adapter to the 3' ends of the RNAs and use it to initiate the template switching reverse transcription. The cDNA is then prepared and indexed for use with the regular Oxford Nanopore v14 chemistry. This protocol is of particular interest to researchers willing to simultaneously study the multiple post-transcriptional processes prevalent in the chloroplast.

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

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.

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.

Full Length Transcriptome Highlights the Coordination of Plastid Transcript Processing.

Plastid 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 crucial, however, when it comes to understanding the interplay between the various steps of plastid gene expression. Here, we describe a protocol to study the plastid transcriptome using nanopore sequencing. In the leaf of Arabidopsis thaliana, with about 1.5 million strand-specific reads mapped to the chloroplast genome, we could recapitulate most of the complexity of the plastid transcriptome (polygenic transcripts, multiple isoforms associated with post-transcriptional processing) using virtual Northern blots. Even if the transcripts longer than about 2500 nucleotides were missing, the study of the co-occurrence of editing and splicing events identified 42 pairs of events that were not occurring independently. This study also highlighted a preferential chronology of maturation events with splicing happening after most sites were edited.

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

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.