RNAmodR: RNAmodR

In RNAmodR: Detection of post-transcriptional modifications in high throughput sequencing data

Description

Post-transcriptional modifications can be found abundantly in rRNA and tRNA and can be detected classically via several strategies. However, difficulties arise if the identity and the position of the modified nucleotides is to be determined at the same time. Classically, a primer extension, a form of reverse transcription (RT), would allow certain modifications to be accessed by blocks during the RT changes or changes in the cDNA sequences. Other modification would need to be selectively treated by chemical reactions to influence the outcome of the reverse transcription.

With the increased availability of high throughput sequencing, these classical methods were adapted to high throughput methods allowing more RNA molecules to be accessed at the same time. With these advances post-transcriptional modifications were also detected on mRNA. Among these high throughput techniques are for example Pseudo-Seq (Carlile et al. 2014), RiboMethSeq (Birkedal et al. 2015) and AlkAnilineSeq (Marchand et al. 2018) each able to detect a specific type of modification from footprints in RNA-Seq data prepared with the selected methods.

Since similar pattern can be observed from some of these techniques, overlaps of the bioinformatical pipeline already are and will become more frequent with new emerging sequencing techniques.

RNAmodR implements classes and a workflow to detect post-transcriptional RNA modifications in high throughput sequencing data. It is easily adaptable to new methods and can help during the phase of initial method development as well as more complex screenings.

Briefly, from the SequenceData, specific subclasses are derived for accessing specific aspects of aligned reads, e.g. 5’-end positions or pileup data. With this a Modifier class can be used to detect specific patterns for individual types of modifications. The SequenceData classes can be shared by different Modifier classes allowing easy adaptation to new methods.

Author(s)

Felix G M Ernst [aut], Denis L.J. Lafontaine [ctb]

References

- Carlile TM, Rojas-Duran MF, Zinshteyn B, Shin H, Bartoli KM, Gilbert WV (2014): "Pseudouridine profiling reveals regulated mRNA pseudouridylation in yeast and human cells." Nature 515 (7525), P. 143–146. DOI: 10.1038/nature13802.

- Birkedal U, Christensen-Dalsgaard M, Krogh N, Sabarinathan R, Gorodkin J, Nielsen H (2015): "Profiling of ribose methylations in RNA by high-throughput sequencing." Angewandte Chemie (International ed. in English) 54 (2), P. 451–455. DOI: 10.1002/anie.201408362.

- Marchand V, Ayadi L, __Ernst FGM__, Hertler J, Bourguignon-Igel V, Galvanin A, Kotter A, Helm M, __Lafontaine DLJ__, Motorin Y (2018): "AlkAniline-Seq: Profiling of m7 G and m3 C RNA Modifications at Single Nucleotide Resolution." Angewandte Chemie (International ed. in English) 57 (51), P. 16785–16790. DOI: 10.1002/anie.201810946.

See Also

The RNAmodR.RiboMethSeq and RNAmodR.AlkAnilineSeq package.

RNAmodR documentation built on Dec. 15, 2020, 2 a.m.