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

Felix G.M. Ernst

#### 2023-04-26

#### Package

RNAmodR 1.14.0

# 1 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.

# 2 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")){
                ###
              }
    )

# 3 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.
              }
    )

## 3.1 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, ...)
    }

# 4 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.

# 5 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.

# 6 Sessioninfo

    sessionInfo()

    ## R version 4.3.0 RC (2023-04-13 r84269 ucrt)
    ## Platform: x86_64-w64-mingw32/x64 (64-bit)
    ## Running under: Windows Server 2022 x64 (build 20348)
    ##
    ## Matrix products: default
    ##
    ##
    ## locale:
    ## [1] LC_COLLATE=C
    ## [2] LC_CTYPE=English_United States.utf8
    ## [3] LC_MONETARY=English_United States.utf8
    ## [4] LC_NUMERIC=C
    ## [5] LC_TIME=English_United States.utf8
    ##
    ## time zone: America/New_York
    ## tzcode source: internal
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] RNAmodR_1.14.0           Modstrings_1.16.0        RNAmodR.Data_1.13.0
    ##  [4] ExperimentHubData_1.26.0 AnnotationHubData_1.30.0 futile.logger_1.4.3
    ##  [7] ExperimentHub_2.8.0      AnnotationHub_3.8.0      BiocFileCache_2.8.0
    ## [10] dbplyr_2.3.2             GenomicFeatures_1.52.0   AnnotationDbi_1.62.0
    ## [13] Biobase_2.60.0           Rsamtools_2.16.0         Biostrings_2.68.0
    ## [16] XVector_0.40.0           rtracklayer_1.60.0       GenomicRanges_1.52.0
    ## [19] GenomeInfoDb_1.36.0      IRanges_2.34.0           S4Vectors_0.38.0
    ## [22] BiocGenerics_0.46.0      BiocStyle_2.28.0
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] RColorBrewer_1.1-3            rstudioapi_0.14
    ##   [3] jsonlite_1.8.4                magrittr_2.0.3
    ##   [5] magick_2.7.4                  farver_2.1.1
    ##   [7] rmarkdown_2.21                BiocIO_1.10.0
    ##   [9] zlibbioc_1.46.0               vctrs_0.6.2
    ##  [11] ROCR_1.0-11                   memoise_2.0.1
    ##  [13] RCurl_1.98-1.12               base64enc_0.1-3
    ##  [15] htmltools_0.5.5               progress_1.2.2
    ##  [17] lambda.r_1.2.4                curl_5.0.0
    ##  [19] Formula_1.2-5                 sass_0.4.5
    ##  [21] bslib_0.4.2                   htmlwidgets_1.6.2
    ##  [23] plyr_1.8.8                    Gviz_1.44.0
    ##  [25] futile.options_1.0.1          cachem_1.0.7
    ##  [27] GenomicAlignments_1.36.0      mime_0.12
    ##  [29] lifecycle_1.0.3               pkgconfig_2.0.3
    ##  [31] Matrix_1.5-4                  R6_2.5.1
    ##  [33] fastmap_1.1.1                 GenomeInfoDbData_1.2.10
    ##  [35] BiocCheck_1.36.0              MatrixGenerics_1.12.0
    ##  [37] shiny_1.7.4                   digest_0.6.31
    ##  [39] colorspace_2.1-0              OrganismDbi_1.42.0
    ##  [41] Hmisc_5.0-1                   RSQLite_2.3.1
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    ##  [45] colorRamps_2.3.1              fansi_1.0.4
    ##  [47] httr_1.4.5                    compiler_4.3.0
    ##  [49] bit64_4.0.5                   withr_2.5.0
    ##  [51] backports_1.4.1               htmlTable_2.4.1
    ##  [53] biocViews_1.68.0              BiocParallel_1.34.0
    ##  [55] DBI_1.1.3                     highr_0.10
    ##  [57] biomaRt_2.56.0                rappdirs_0.3.3
    ##  [59] DelayedArray_0.26.0           rjson_0.2.21
    ##  [61] tools_4.3.0                   foreign_0.8-84
    ##  [63] interactiveDisplayBase_1.38.0 httpuv_1.6.9
    ##  [65] nnet_7.3-18                   glue_1.6.2
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    ##  [87] dplyr_1.1.2                   lattice_0.21-8
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    ##  [91] biovizBase_1.48.0             tidyselect_1.2.0
    ##  [93] RBGL_1.76.0                   knitr_1.42
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    ## [107] tibble_3.2.1                  BiocManager_1.30.20
    ## [109] graph_1.78.0                  cli_3.6.1
    ## [111] rpart_4.1.19                  xtable_1.8-4
    ## [113] munsell_0.5.0                 jquerylib_0.1.4
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