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

Abstract

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. However, the major challenge in tRNA examination is related to a large number of duplicated and point-mutated genes encoding those RNA molecules. Moreover, the existence of multiple isoacceptors/isodecoders complicates both the analysis and read mapping. Existing databases for tRNA sequencing provide near exhaustive listings of tRNA genes, but the use of such highly redundant reference sequences in RNA-seq analyses leads to a large number of ambiguously mapped sequencing reads. Here we describe a relatively simple computational strategy for semi-automatic collapsing of highly redundant tRNA datasets into a non-redundant collection of reference tRNA sequences. The relevance of the approach was validated by analysis of experimentally obtained tRNA-sequencing datasets for different prokaryotic and eukaryotic model organisms. The data demonstrate that non-redundant tRNA reference sequences allow improving unambiguous mapping of deep sequencing data.

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

“…While our previous investigations assessed the overall modification content of tRNA fraction and small RNA fragments by LC-MS [23] , we here chose a more sophisticated approach, namely pinpointing the location of tRNA modifications by RNA-seq based methods specifically tailored to mapping of (i) m 5 C residues by bisulfite sequencing [27] , (ii) ribose-methylated nucleotides Nm by RiboMethSeq [33] , and (iii) alkali-sensitive modifications D, m 3 C, and m 7 G, by AlkAnilineSeq [30] . In all three methods, reads were aligned to a synthetic “tRNA genome”, the tRNA reference sequences specifically designed for this type of analysis [32] . This reduced tRNA genome accounts for the occurrence of 39 cytoplasmic tRNA sequence families, each one consolidating sequences of high similarity, such as isoacceptors/isodecoders that differ only by a few SNPs.…”

“…Construction of reduced tRNA reference dataset for alignment was described previously [32] . In brief, predicted tRNA genes from gtRNAdb database (http://gtrnadb.ucsc.edu/) were clustered based on Euclidean distance between tRNA sequences.…”

The ribose methylation enzyme FTSJ1 has a conserved role in neuron morphology and learning performance

“…Analysis of human tRNA 2′-O-methylation by RiboMethSeq was performed using the optimized non-redundant collection of reference tRNA sequences. This reduced collection contains 43 tRNA species and was validated by analysis of several experimentally obtained RiboMethSeq datasets ( Pichot et al, 2021 ). Alignment of RiboMethSeq reads obtained in this study also confirmed low content in ambiguously mapped reads.…”

The ribose methylation enzyme FTSJ1 has a conserved role in neuron morphology and learning performance

“…Analysis of human tRNA 2’-O-methylation by RiboMethSeq was performed using the optimised non-redundant collection of reference tRNA sequences. This reduced collection contains 43 tRNA species and was validated by analysis of several experimentally obtained RiboMethSeq sequencing datasets (Pichot et al , 2021). Alignment of RiboMethSeq reads obtained in this study also confirmed low content in ambiguously mapped reads.…”