# RNAmodR: creating classes for additional modification detection from high throughput sequencing.

# Introduction

For users interested in the general aspect of any `RNAmodR` based package please have a look at the main vignette of the package.

This vignette is aimed at developers and researchers, who want to use the functionality of the `RNAmodR` package to develop a new modification strategy based on high throughput sequencing data.

    library(RNAmodR)

Two classes have to be considered to establish a new analysis pipeline using `RNAmodR`. These are the `SequenceData` and the `Modifier` class.

# A new `SequenceData` class

First, the `SequenceData` class has to be considered. Several classes are already implemented, which are:

- `End5SequenceData`
- `End3SequenceData`
- `EndSequenceData`
- `ProtectedEndSequenceData`
- `CoverageSequenceData`
- `PileupSequenceData`
- `NormEnd5SequenceData`
- `NormEnd3SequenceData`

If these cannot be reused, a new class can be implemented quite easily. First the DataFrame class, the Data class and a constructor has to defined. The only value, which has to be provided, is a default `minQuality` integer value and some basic information.

    setClass(Class = "ExampleSequenceDataFrame",
             contains = "SequenceDFrame")
    ExampleSequenceDataFrame <- function(df, ranges, sequence, replicate,
                                          condition, bamfiles, seqinfo){
      RNAmodR:::.SequenceDataFrame("Example",df, ranges, sequence, replicate,
                                   condition, bamfiles, seqinfo)
    }
    setClass(Class = "ExampleSequenceData",
             contains = "SequenceData",
             slots = c(unlistData = "ExampleSequenceDataFrame"),
             prototype = list(unlistData = ExampleSequenceDataFrame(),
                              unlistType = "ExampleSequenceDataFrame",
                              minQuality = 5L,
                              dataDescription = "Example data"))
    ExampleSequenceData <- function(bamfiles, annotation, sequences, seqinfo, ...){
      RNAmodR:::SequenceData("Example", bamfiles = bamfiles,
                             annotation = annotation, sequences = sequences,
                             seqinfo = seqinfo, ...)
    }

Second, the `getData` function has to be implemented. This is used to load the data from a bam file and must return a named list `IntegerList`, `NumericList` or `CompressedSplitDataFrameList` per file.

    setMethod("getData",
              signature = c(x = "ExampleSequenceData",
                            bamfiles = "BamFileList",
                            grl = "GRangesList",
                            sequences = "XStringSet",
                            param = "ScanBamParam"),
              definition = function(x, bamfiles, grl, sequences, param, args){
                ###
              }
    )

Third, the `aggregate` function has to be implemented. This function is used to aggregate data over replicates for all or one of the conditions. The resulting data is passed on to the `Modifier` class.

    setMethod("aggregateData",
              signature = c(x = "ExampleSequenceData"),
              function(x, condition = c("Both","Treated","Control")){
                ###
              }
    )

# A new `Modifier` class

A new `Modifier` class is probably the main class, which needs to be implemented. Three variable have to be set. `mod` must be a single element from the `Modstrings::shortName(Modstrings::ModRNAString())`. `score` is the default score, which is used for several function. A column with this name should be returned from the `aggregate` function. `dataType` defines the `SequenceData` class to be used. `dataType` can contain multiple names of a `SequenceData` class, which are then combined to form a `SequenceDataSet`.

    setClass("ModExample",
             contains = c("RNAModifier"),
             prototype = list(mod = "X",
                              score = "score",
                              dataType = "ExampleSequenceData"))
    ModExample <- function(x, annotation, sequences, seqinfo, ...){
      RNAmodR:::Modifier("ModExample", x = x, annotation = annotation,
                         sequences = sequences, seqinfo = seqinfo, ...)
    }

`dataType` can also be a `list` of `character` vectors, which leads then to the creation of `SequenceDataList`. However, for now this is a hypothetical case and should only be used, if the detection of a modification requires bam files from two or more different methods to be used to detect one modification.

The `settings<-` function can be amended to save specifc settings ( `.norm_example_args` must be defined seperatly to normalize input arguments in any way one sees fit).

    setReplaceMethod(f = "settings",
                     signature = signature(x = "ModExample"),
                     definition = function(x, value){
                       x <- callNextMethod()
                       # validate special setting here
                       x@settings[names(value)] <- unname(.norm_example_args(value))
                       x
                     })

The `aggregateData` function is used to take the aggregated data from the `SequenceData` object and to calculate the specific scores, which are then stored in the `aggregate` slot.

    setMethod(f = "aggregateData",
              signature = signature(x = "ModExample"),
              definition =
                function(x, force = FALSE){
                  # Some data with element per transcript
                }
    )

The `findMod` function takes the aggregate data and searches for modifications, which are then returned as a GRanges object and stored in the `modifications` slot.

    setMethod("findMod",
              signature = c(x = "ModExample"),
              function(x){
                # an element per modification found.
              }
    )

## A new `ModifierSet` class

The `ModifierSet` class is implemented very easily by defining the class and the constructor. The functionality is defined by the `Modifier` class.

    setClass("ModSetExample",
             contains = "ModifierSet",
             prototype = list(elementType = "ModExample"))
    ModSetExample <- function(x, annotation, sequences, seqinfo, ...){
      RNAmodR:::ModifierSet("ModExample", x = x, annotation = annotation,
                            sequences = sequences, seqinfo = seqinfo, ...)
    }

# Visualization functions

Additional functions, which need to be implemented, are `getDataTrack` for the new `SequenceData` and new `Modifier` classes and `plotData`/`plotDataByCoord` for the new `Modifier` and `ModifierSet` classes. `name` defines a transcript name found in `names(ranges(x))` and `type` is the data type typically found as a column in the `aggregate` slot.

    setMethod(
      f = "getDataTrack",
      signature = signature(x = "ExampleSequenceData"),
      definition = function(x, name, ...) {
        ###
      }
    )
    setMethod(
      f = "getDataTrack",
      signature = signature(x = "ModExample"),
      definition = function(x, name, type, ...) {
      }
    )
    setMethod(
      f = "plotDataByCoord",
      signature = signature(x = "ModExample", coord = "GRanges"),
      definition = function(x, coord, type = "score", window.size = 15L, ...) {
      }
    )
    setMethod(
      f = "plotData",
      signature = signature(x = "ModExample"),
      definition = function(x, name, from, to, type = "score", ...) {
      }
    )
    setMethod(
      f = "plotDataByCoord",
      signature = signature(x = "ModSetExample", coord = "GRanges"),
      definition = function(x, coord, type = "score", window.size = 15L, ...) {
      }
    )
    setMethod(
      f = "plotData",
      signature = signature(x = "ModSetExample"),
      definition = function(x, name, from, to, type = "score", ...) {
      }
    )

If unsure, how to modify these functions, have a look a the code in the `Modifier-Inosine-viz.R` file of this package.

# Summary

As suggested directly above, for a more detailed example have a look at the `ModInosine` class source code found in the `Modifier-Inosine-class.R` and `Modifier-Inosine-viz.R` files of this package.

# Sessioninfo

    sessionInfo()

    ## R version 4.5.2 (2025-10-31)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.4 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## time zone: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] RNAmodR_1.24.0           Modstrings_1.26.0        RNAmodR.Data_1.24.0
    ##  [4] ExperimentHubData_1.36.0 AnnotationHubData_1.40.0 futile.logger_1.4.9
    ##  [7] ExperimentHub_3.0.0      AnnotationHub_4.0.0      BiocFileCache_3.0.0
    ## [10] dbplyr_2.5.2             txdbmaker_1.6.2          GenomicFeatures_1.62.0
    ## [13] AnnotationDbi_1.72.0     Biobase_2.70.0           Rsamtools_2.26.0
    ## [16] Biostrings_2.78.0        XVector_0.50.0           rtracklayer_1.70.1
    ## [19] GenomicRanges_1.62.1     Seqinfo_1.0.0            IRanges_2.44.0
    ## [22] S4Vectors_0.48.0         BiocGenerics_0.56.0      generics_0.1.4
    ## [25] BiocStyle_2.38.0
    ##
    ## loaded via a namespace (and not attached):
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    ##   [3] sys_3.4.3                   jsonlite_2.0.0
    ##   [5] magrittr_2.0.4              farver_2.1.2
    ##   [7] rmarkdown_2.30              BiocIO_1.20.0
    ##   [9] vctrs_0.7.1                 ROCR_1.0-12
    ##  [11] memoise_2.0.1               RCurl_1.98-1.17
    ##  [13] base64enc_0.1-6             htmltools_0.5.9
    ##  [15] S4Arrays_1.10.1             BiocBaseUtils_1.12.0
    ##  [17] progress_1.2.3              lambda.r_1.2.4
    ##  [19] curl_7.0.0                  SparseArray_1.10.8
    ##  [21] Formula_1.2-5               sass_0.4.10
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    ##  [95] UCSC.utils_1.6.1            lazyeval_0.2.2
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