Among the various post-transcriptional RNA modifications, 2'-O methylations are quite common in rRNA and tRNA. They confere resistance to alkaline degradation by preventing a nucleophilic attack on the 3'-phosphate especially in flexible RNA, which is fascilitated by high pH conditions. This property can be queried using a method called RiboMethSeq (Birkedahl et al. 2015, Marchand et al. 2017) for which RNA is treated in alkaline conditions and RNA fragments are used to prepare a sequencing library.
At position containing a 2'-O methylations, read ends are less frequent, which is used to detect and score the2'-O methylations.
ModRiboMethSeq class uses the the
class to store and aggregate data along the transcripts. The calculated
scores follow the nomenclature of Birkedahl et al. (2015) with the names
The ScoreMax as described by Marchand et al. (2017) are not implemented, yet, since an unambigeous description is not available from the literature.
The ScoreMean as described by Galvanin et al. (2018) is implemented. However, use with caution, since the description is not unambigeous. Currently it is calculated as as: 1 - (n / mean(areaL + areaR)). (n: counts at position, areaL: counts from x position upstream, areaR: counts from x position downstream)
Only samples named
treated are used for this analysis. Normalization
to untreated samples is currently not used.
ModRiboMethSeq5 class can be used as well. However, as
End5SequenceData is employed using
only the 5'-end positions of reads.
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the input which can be of the different types depending on whether
annotation data, which must match the information contained
in the BAM files. This is parameter is only required if
sequences matching the target sequences the reads were
mapped onto. This must match the information contained in the BAM files. This
is parameter is only required if
Optional arguments overwriting default values, which are
To disable minimal values for modification calling, set them to
Felix G.M. Ernst [aut]
- 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<e2><80><93>455. DOI: 10.1002/anie.201408362.
- Marchand V, Ayadi L, El Hajj A, Blanloeil-Oillo F, Helm M, Motorin Y (2017): "High-Throughput Mapping of 2'-O-Me Residues in RNA Using Next-Generation Sequencing (Illumina RiboMethSeq Protocol)." Methods in molecular biology (Clifton, N.J.) 1562, P. 171<e2><80><93>187. DOI: 10.1007/978-1-4939-6807-7_12.
- Galvanin A, Ayadi L, Helm M, Motorin Y, Marchand V (2017): "Mapping and Quantification of tRNA 2'-O-Methylation by RiboMethSeq". Wajapeyee N., Gupta R. (eds) Epitranscriptomics. Methods in Molecular Biology (Humana Press, New York, NY) 1870, P. 273-295. DOI: 10.1007/978-1-4939-8808-2_21
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library(RNAmodR.Data) library(rtracklayer) annotation <- GFF3File(RNAmodR.Data.example.RMS.gff3()) sequences <- RNAmodR.Data.example.RMS.fasta() files <- list("Sample1" = c(treated = RNAmodR.Data.example.RMS.1()), "Sample2" = c(treated = RNAmodR.Data.example.RMS.1())) # Creating a Modifier object of type ModRiboMethSeq mrms <- ModRiboMethSeq(files[], annotation = annotation, sequences = sequences) # Creating a ModifierSet object of type ModSetRiboMethSeq msrms <- ModSetRiboMethSeq(files, annotation = annotation, sequences = sequences)
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