Abstract:During particular stress conditions, transfer RNAs (tRNAs) become substrates of stressinduced endonucleases, resulting in the production of distinct tRNA-derived small RNAs (tsRNAs). These small RNAs have been implicated in a wide range of biological processes, but how isoacceptor and even isodecoder-specific tsRNAs act at the molecular level is still poorly understood. Importantly, stress-induced tRNA cleavage affects only a few tRNAs of a given isoacceptor or isodecoder, raising the question as to how such l… Show more
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.
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.
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