Computational studies of RNA modification-dependent RNA binding protein networks

The covalent modification of RNA nucleotides is a powerful layer of post-transcriptional control of gene expression across the tree of life. Historically, only abundant modifications on abundant RNAs such as tRNA and rRNA could be studied, due to methodological limitations. In the past decade, leaps forward in biochemistry and high throughput sequencing methods have enabled mapping of RNA modifications across all RNA species. In particular this thesis focuses on the most abundant internal modification of mRNA, N6-methyladenosine (m6A), and how RNA binding proteins (RBPs) interact with RNA modifications to impact RNA life cycle. Alongside these experimental developments have come new computational challenges. Integration of many datasets must be approached carefully, with a view to extract as much biological information as possible. Throughout this work I describe the development of open source computational tools for the analysis and visualisation of CLIP data. A computational pipeline based on hierarchical pre-mapping steps enables accurate quantification of non-coding RNAs from individual nucleotide resolution crosslinking and immunoprecipitation (iCLIP) datasets. Using the pipeline I describe novel tRNA binding for the DEAH-box helicase DDX3X and identify widespread binding of NSun2 and Trmt2A to pre-tRNAs. In collaboration with the lab of Prof. Folkert van Werven, I integrate m6A miCLIP with m6A-reader protein iCLIP data, alongside functional datasets in WT and methyltransferase deletion conditions in order to uncover the role of m6A in early budding yeast meiosis. Surprisingly, we find that the sole yeast m6A-binding protein, Pho92p, binds in both an m6A-dependent and an m6A-independent manner. m6A-dependent Pho92p binding partners are implicated in mRNA decay coupled to translation. Taken together I present powerful computational tools that will be of use to the wider community, alongside the interesting biological insights they have already enabled

clipplotr - a comparative visualisation and analysis tool for CLIP data

CLIP technologies are now widely used to study RNA-protein interactions and many datasets are now publicly available. An important first step in CLIP data exploration is the visual inspection and assessment of processed genomic data on selected genes or regions and performing comparisons: either across conditions within a particular project, or incorporating publicly available data. However, the output files produced by data processing pipelines or preprocessed files available to download from data repositories are often not suitable for direct comparison and usually need further processing. Furthermore, to derive biological insight it is usually necessary to visualise CLIP signal alongside other data such as annotations, or orthogonal functional genomic data (e.g. RNA-seq). We have developed a simple, but powerful, command-line tool: clipplotr, which facilitates these visual comparative and integrative analyses with normalisation and smoothing options for CLIP data and the ability to show these alongside reference annotation tracks and functional genomic data. These data can be supplied as input to clipplotr in a range of file formats, which will output a publication quality figure. It is written in R and can both run on a laptop computer independently, or be integrated into computational workflows on a high-performance cluster. Releases, source code and documentation are freely available at: https://github.com/ulelab/clipplotr

How Do You Identify m⁶A Methylation in Transcriptomes at High Resolution? A Comparison of Recent Datasets

A flurry of methods has been developed in recent years to identify N6-methyladenosine (m⁶A) sites across transcriptomes at high resolution. This raises the need to understand both the common features and those that are unique to each method. Here, we complement the analyses presented in the original papers by reviewing their various technical aspects and comparing the overlap between m⁶A-methylated messenger RNAs (mRNAs) identified by each. Specifically, we examine eight different methods that identify m⁶A sites in human cells with high resolution: two antibody-based crosslinking and immunoprecipitation (CLIP) approaches, two using endoribonuclease MazF, one based on deamination, two using Nanopore direct RNA sequencing, and finally, one based on computational predictions. We contrast the respective datasets and discuss the challenges in interpreting the overlap between them, including a prominent expression bias in detected genes. This overview will help guide researchers in making informed choices about using the available data and assist with the design of future experiments to expand our understanding of m⁶A and its regulation

Analysis of RNA-Seq datasets reveals enrichment of tissue-specific splice variants for nuclear envelope proteins

<p>Laminopathies yield tissue-specific pathologies, yet arise from mutation of ubiquitously-expressed genes. A little investigated hypothesis to explain this is that the mutated proteins or their partners have tissue-specific splice variants. To test this, we analyzed RNA-Seq datasets, finding novel isoforms or isoform tissue-specificity for: Lap2, linked to cardiomyopathy; Nesprin 2, linked to Emery-Dreifuss muscular dystrophy and Lmo7, that regulates the Emery-Dreifuss muscular dystrophy linked emerin gene. Interestingly, the muscle-specific Lmo7 exon is rich in serine phosphorylation motifs, suggesting regulatory function. Muscle-specific splice variants in non-nuclear envelope proteins linked to other muscular dystrophies were also found. Nucleoporins tissue-specific variants were found for Nup54, Nup133, Nup153 and Nup358/RanBP2. RT-PCR confirmed novel Lmo7 and RanBP2 variants and specific knockdown of the Lmo7 variantreduced myogenic index. Nuclear envelope proteins were enriched for tissue-specific splice variants compared to the rest of the genome, suggesting that splice variants contribute to its tissue-specific functions.</p

psiCLIP reveals dynamic RNA binding by DEAH-box helicases before and after exon ligation

RNA helicases remodel the spliceosome to enable pre-mRNA splicing, but their binding and mechanism of action remain poorly understood. To define helicase-RNA contacts in specific spliceosomal states, we develop purified spliceosome iCLIP (psiCLIP), which reveals dynamic helicase-RNA contacts during splicing catalysis. The helicase Prp16 binds along the entire available single-stranded RNA region between the branchpoint and 3\u27-splice site, while Prp22 binds diffusely downstream of the branchpoint before exon ligation, but then switches to more narrow binding in the downstream exon after exon ligation, arguing against a mechanism of processive translocation. Depletion of the exon-ligation factor Prp18 destabilizes Prp22 binding to the pre-mRNA, suggesting that proofreading by Prp22 may sense the stability of the spliceosome during exon ligation. Thus, psiCLIP complements structural studies by providing key insights into the binding and proofreading activity of spliceosomal RNA helicases

RNA modifications detection by comparative Nanopore direct RNA sequencing.

RNA molecules undergo a vast array of chemical post-transcriptional modifications (PTMs) that can affect their structure and interaction properties. In recent years, a growing number of PTMs have been successfully mapped to the transcriptome using experimental approaches relying on high-throughput sequencing. Oxford Nanopore direct-RNA sequencing has been shown to be sensitive to RNA modifications. We developed and validated Nanocompore, a robust analytical framework that identifies modifications from these data. Our strategy compares an RNA sample of interest against a non-modified control sample, not requiring a training set and allowing the use of replicates. We show that Nanocompore can detect different RNA modifications with position accuracy in vitro, and we apply it to profile m6A in vivo in yeast and human RNAs, as well as in targeted non-coding RNAs. We confirm our results with orthogonal methods and provide novel insights on the co-occurrence of multiple modified residues on individual RNA molecules.The Kouzarides laboratory is supported by Cancer Research UK (grant reference RG72100) and core support from the Wellcome Trust (core grant reference WT203144) and Cancer Research UK (grant reference C6946/A24843). PPA was supported by a Borysiewicz Biomedical Sciences postdoctoral fellowship (University of Cambridge) and AL by a COFUND Marie Skłodowska-Curie Actions postdoctoral fellowship (EMBL). FW and TS are supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001203), the UK Medical Research Council (FC001203), and the Wellcome Trust (FC001203). IB and V Miano are supported by Cancer Research UK (grant reference RG86786) and by the Joseph Mitchell Fund

clipplotr—a comparative visualization and analysis tool for CLIP data

CLIP technologies are now widely used to study RNA–protein interactions and many data sets are now publicly available. An important first step in CLIP data exploration is the visual inspection and assessment of processed genomic data on selected genes or regions and performing comparisons: either across conditions within a particular project, or incorporating publicly available data. However, the output files produced by data processing pipelines or preprocessed files available to download from data repositories are often not suitable for direct comparison and usually need further processing. Furthermore, to derive biological insight it is usually necessary to visualize a CLIP signal alongside other data such as annotations, or orthogonal functional genomic data (e.g., RNA-seq). We have developed a simple, but powerful, command-line tool: clipplotr, which facilitates these visual comparative and integrative analyses with normalization and smoothing options for CLIP data and the ability to show these alongside reference annotation tracks and functional genomic data. These data can be supplied as input to clipplotr in a range of file formats, which will output a publication quality figure. It is written in R and can both run on a laptop computer independently or be integrated into computational workflows on a high-performance cluster. Releases, source code, and documentation are freely available at https://github.com/ulelab/clipplotr

Positional motif analysis reveals the extent of specificity of protein-RNA interactions observed by CLIP

BACKGROUND: Crosslinking and immunoprecipitation (CLIP) is a method used to identify in vivo RNA–protein binding sites on a transcriptome-wide scale. With the increasing amounts of available data for RNA-binding proteins (RBPs), it is important to understand to what degree the enriched motifs specify the RNA-binding profiles of RBPs in cells. RESULTS: We develop positionally enriched k-mer analysis (PEKA), a computational tool for efficient analysis of enriched motifs from individual CLIP datasets, which minimizes the impact of technical and regional genomic biases by internal data normalization. We cross-validate PEKA with mCross and show that the use of input control for background correction is not required to yield high specificity of enriched motifs. We identify motif classes with common enrichment patterns across eCLIP datasets and across RNA regions, while also observing variations in the specificity and the extent of motif enrichment across eCLIP datasets, between variant CLIP protocols, and between CLIP and in vitro binding data. Thereby, we gain insights into the contributions of technical and regional genomic biases to the enriched motifs, and find how motif enrichment features relate to the domain composition and low-complexity regions of the studied proteins. CONCLUSIONS: Our study provides insights into the overall contributions of regional binding preferences, protein domains, and low-complexity regions to the specificity of protein-RNA interactions, and shows the value of cross-motif and cross-RBP comparison for data interpretation. Our results are presented for exploratory analysis via an online platform in an RBP-centric and motif-centric manner (https://imaps.goodwright.com/apps/peka/). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-022-02755-2

ulelab/ultraplex: v1.2.6

&lt;p&gt;Improved reporting of read counts in the log file and automatic version updating.&lt;/p&gt