N1-methylation of adenosine (mA) in ND5 mRNA leads to complex I dysfunction in Alzheimer's disease.

One mechanism of particular interest to regulate mRNA fate post-transcriptionally is mRNA modification. Especially the extent of mA mRNA methylation is highly discussed due to methodological differences. However, one single mA site in mitochondrial ND5 mRNA was unanimously reported by different groups.

Data Analysis Pipeline for Detection and Quantification of Pseudouridine (ψ) in RNA by HydraPsiSeq.

Pseudouridine, a modified RNA residue formed by the isomerization of its parental U nucleotide, is prevalent in a majority of cellular RNAs; its presence was reported in tRNA, rRNA, and sn/snoRNA as well as in mRNA/lncRNA. Multiple analytical deep sequencing-based approaches have been proposed for pseudouridine detection and quantification, among which the most popular relies on the use of soluble carbodiimide (termed CMCT). Recently, we developed an alternative protocol for pseudouridine mapping and quantification.

Quantification of substoichiometric modification reveals global tsRNA hypomodification, preferences for angiogenin-mediated tRNA cleavage, and idiosyncratic epitranscriptomes of human neuronal cell-lines.

Modification of tRNA is an integral part of the epitranscriptome with a particularly pronounced potential to generate diversity in RNA expression. Eukaryotic tRNA contains modifications in up to 20% of their nucleotides, but not all sites are always fully modified. Combinations and permutations of partially modified sites in tRNAs can generate a plethora of tRNA isoforms, termed modivariants.

Machine learning algorithm for precise prediction of 2'-O-methylation (Nm) sites from experimental RiboMethSeq datasets.

Analysis of epitranscriptomic RNA modifications by deep sequencing-based approaches brings an essential contribution to the general knowledge on their precise locations and relative stoichiometry in cellular RNAs. To reveal RNA modifications, several analytical approaches have been proposed, including antibody-driven enrichment, analysis of RT-signatures and specific chemical treatments. However, analysis and interpretation of these massive datasets, especially for low abundant cellular RNAs (e.

Non-Redundant tRNA Reference Sequences for Deep Sequencing Analysis of tRNA Abundance and Epitranscriptomic RNA Modifications.

Analysis of RNA by deep-sequencing approaches has found widespread application in modern biology. In addition to measurements of RNA abundance under various physiological conditions, such techniques are now widely used for mapping and quantification of RNA modifications. Transfer RNA (tRNA) molecules are among the frequent targets of such investigation, since they contain multiple modified residues.