1 Introduction

7-methyl guanosine (m7G), 3-methyl cytidine (m3C) and Dihydrouridine (D) are commonly found in rRNA and tRNA and can be detected classically by primer extension analysis. However, since the modifications do not interfere with Watson-Crick base pairing, a specific chemical treatment needs to be employed to cause strand breaks specifically at the modified positions. Initially, this involved a sodium borhydride treatment to create abasic sites and cleaving the RNA at abasic sites with aniline.

This classical protocol was converted to a high throughput sequencing method call AlkAnilineSeq and allows modified position be detected by an accumulation of 5’-ends at the N+1 position [Marchand et al. (2018)]. It was found, that m3C is susceptible to this treatment, which allows m7G, m3C and D to be detected by the same method from the same data sets, since the identify of the unmodified nucleotide informs about the three modified nucleotides.

The ModAlkAnilineSeq class uses the the NormEnd5SequenceData class to store and aggregate data along the transcripts. The calculated scores follow the nomenclature of [Marchand et al. (2018)] with the names scoreNC (default) and scoreSR.

## snapshotDate(): 2019-10-22
library(rtracklayer)
library(GenomicRanges)
library(RNAmodR.AlkAnilineSeq)
library(RNAmodR.Data)

2 Example workflow

The example workflow is limited to 18S rRNA and some tRNA from S.cerevisiae. As annotation data either a gff file or a TxDb object and for sequence data a fasta file or a BSgenome object can be used. The data is provided as bam files.

annotation <- GFF3File(RNAmodR.Data.example.AAS.gff3())
sequences <- RNAmodR.Data.example.AAS.fasta()
files <- list("wt" = c(treated = RNAmodR.Data.example.wt.1(),
                       treated = RNAmodR.Data.example.wt.2(),
                       treated = RNAmodR.Data.example.wt.3()),
              "Bud23del" = c(treated = RNAmodR.Data.example.bud23.1(),
                             treated = RNAmodR.Data.example.bud23.2()),
              "Trm8del" = c(treated = RNAmodR.Data.example.trm8.1(),
                            treated = RNAmodR.Data.example.trm8.2()))

The analysis is triggered by the construction of a ModSetAlkAnilineSeq object. Internally parallelization is used via the BiocParallel package, which would allow optimization depending on number/size of input files (number of samples, number of replicates, number of transcripts, etc).

msaas <- ModSetAlkAnilineSeq(files, annotation = annotation, sequences = sequences)
## Import genomic features from the file as a GRanges object ... OK
## Prepare the 'metadata' data frame ... OK
## Make the TxDb object ... OK
msaas
## ModSetAlkAnilineSeq of length 3
## names(3): wt Bud23del Trm8del
## | Modification type(s):  m7G / m3C / D                                               
##                             wt Bud23del Trm8del
## | Modifications found: yes (9)  yes (8) yes (7)
## | Settings:
##          minCoverage minReplicate  find.mod minLength minSignal minScoreNC
##            <integer>    <integer> <logical> <integer> <integer>  <integer>
## wt                10            1      TRUE         9        10         50
## Bud23del          10            1      TRUE         9        10         50
## Trm8del           10            1      TRUE         9        10         50
##          minScoreSR minScoreBaseScore scoreOperator
##           <numeric>         <numeric>   <character>
## wt              0.5               0.9             &
## Bud23del        0.5               0.9             &
## Trm8del         0.5               0.9             &

As expected the m7G1575 is missing from the Bud23del samples.

mod <- modifications(msaas)
lapply(mod,head, n = 2)
## $wt
## GRanges object with 2 ranges and 6 metadata columns:
##       seqnames    ranges strand |         mod                source
##          <Rle> <IRanges>  <Rle> | <character>           <character>
##   [1]     chr1      1575      + |         m7G RNAmodR.AlkAnilineSeq
##   [2]     chr3        46      + |         m7G RNAmodR.AlkAnilineSeq
##              type            score           scoreSR      Parent
##       <character>        <numeric>         <numeric> <character>
##   [1]      RNAMOD 162.228212107694 0.984208782283283           1
##   [2]      RNAMOD 373.772919088153 0.841166009007199           3
##   -------
##   seqinfo: 11 sequences from an unspecified genome; no seqlengths
## 
## $Bud23del
## GRanges object with 2 ranges and 6 metadata columns:
##       seqnames    ranges strand |         mod                source
##          <Rle> <IRanges>  <Rle> | <character>           <character>
##   [1]     chr3        46      + |         m7G RNAmodR.AlkAnilineSeq
##   [2]     chr5        50      + |         m7G RNAmodR.AlkAnilineSeq
##              type            score           scoreSR      Parent
##       <character>        <numeric>         <numeric> <character>
##   [1]      RNAMOD 254.640318503864 0.858100599724882           3
##   [2]      RNAMOD 86.3555787142384 0.605248614086998           5
##   -------
##   seqinfo: 11 sequences from an unspecified genome; no seqlengths
## 
## $Trm8del
## GRanges object with 2 ranges and 6 metadata columns:
##       seqnames    ranges strand |         mod                source
##          <Rle> <IRanges>  <Rle> | <character>           <character>
##   [1]     chr1      1575      + |         m7G RNAmodR.AlkAnilineSeq
##   [2]     chr3        37      + |         m7G RNAmodR.AlkAnilineSeq
##              type            score           scoreSR      Parent
##       <character>        <numeric>         <numeric> <character>
##   [1]      RNAMOD  117.24788113029 0.987290026350527           1
##   [2]      RNAMOD 69.9604450056439 0.979530326399486           3
##   -------
##   seqinfo: 11 sequences from an unspecified genome; no seqlengths

3 Visualizing the results

As outlined in the RNAmodR package we can compare the samples using the plotCompareByCoord to prepare a heatmap. For this we select some position from the found modifications. In addition we prepare an alias table.

coord <- mod[[1]]
alias <- data.frame(tx_id = c(1,3,5,6,7,8,10,11),
                    name = c("18S rRNA","tF(GAA)B","tG(GCC)B","tT(AGT)B",
                             "tQ(TTG)B","tC(GCA)B","tS(CGA)C","tV(AAC)E1"),
                    stringsAsFactors = FALSE)
plotCompareByCoord(msaas, coord, score = "scoreSR", alias = alias,
                   normalize = TRUE)
Heatmap showing Stop ratio scores for detected m7G, m3C and D positions.

Figure 1: Heatmap showing Stop ratio scores for detected m7G, m3C and D positions

plotCompareByCoord(msaas, coord[1], score = "scoreSR", alias = alias)