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We present txtools, an R package that enables the processing, analysis, and visualization of RNA-seq data at the nucleotide-level resolution, seamlessly integrating alignments to the genome with transcriptomic representation. txtools' main inputs are BAM files and a transcriptome annotation, and the main output is a table, capturing mismatches, deletions, and the number of reads beginning and ending at each nucleotide in the transcriptomic space. txtools further facilitates downstream visualization and analyses. We showcase, using examples from the epitranscriptomic field, how a few calls to txtools functions can yield insightful and ready-to-publish results. txtools is of broad utility also in the context of structural mapping and RNA:protein interaction mapping. By providing a simple and intuitive framework, we believe that txtools will be a useful and convenient tool and pave the path for future discovery. txtools is available for installation from its GitHub repository at https://github.com/AngelCampos/txtools., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Background: N6-methyladenosine (m6A) is the most abundant mRNA modification, and controls mRNA stability. m6A distribution varies considerably between and within species. Yet, it is unclear to what extent this variability is driven by changes in genetic sequences ('cis') or cellular environments ('trans') and via which mechanisms., Results: Here we dissect the determinants governing RNA methylation via interspecies and intraspecies hybrids in yeast and mammalian systems, coupled with massively parallel reporter assays and m6A-QTL reanalysis. We find that m6A evolution and variability is driven primarily in 'cis', via two mechanisms: (1) variations altering m6A consensus motifs, and (2) variation impacting mRNA secondary structure. We establish that mutations impacting RNA structure - even when distant from an m6A consensus motif - causally dictate methylation propensity. Finally, we demonstrate that allele-specific differences in m6A levels lead to allele-specific changes in gene expression., Conclusions: Our findings define the determinants governing m6A evolution and diversity and characterize the consequences thereof on gene expression regulation., (© 2024. The Author(s).)

N 6 -methyladenosine (m6A) is the most prevalent modification of messenger RNA in mammals. To interrogate its functions and dynamics, there is a critical need to quantify m6A at three levels: site, gene and sample. Current approaches address these needs in a limited manner. Here we develop m6A-seq2, relying on multiplexed m6A-immunoprecipitation of barcoded and pooled samples. m6A-seq2 allows a big increase in throughput while reducing technical variability, requirements of input material and cost. m6A-seq2 is furthermore uniquely capable of providing sample-level relative quantitations of m6A, serving as an orthogonal alternative to mass spectrometry-based approaches. Finally, we develop a computational approach for gene-level quantitation of m6A. We demonstrate that using this metric, roughly 30% of the variability in RNA half life in mouse embryonic stem cells can be explained, establishing m6A as a main driver of RNA stability. m6A-seq2 thus provides an experimental and analytic framework for dissecting m6A-mediated regulation at three different levels., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

N6-methyladenosine (m 6 A) is the most abundant modification on mRNA and is implicated in critical roles in development, physiology, and disease. A major limitation has been the inability to quantify m 6 A stoichiometry and the lack of antibody-independent methodologies for interrogating m 6 A. Here, we develop MAZTER-seq for systematic quantitative profiling of m6A at single-nucleotide resolution at 16%-25% of expressed sites, building on differential cleavage by an RNase. MAZTER-seq permits validation and de novo discovery of m 6 A sites, calibration of the performance of antibody-based approaches, and quantitative tracking of m 6 A dynamics in yeast gametogenesis and mammalian differentiation. We discover that m6A stoichiometry is "hard coded" in cis via a simple and predictable code, accounting for 33%-46% of the variability in methylation levels and allowing accurate prediction of m 6 A loss and acquisition events across evolution. MAZTER-seq allows quantitative investigation of m 6 A regulation in subcellular fractions, diverse cell types, and disease states., (Copyright © 2019 Elsevier Inc. All rights reserved.)