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

Felix G.M. Ernst

#### 2023-05-15

#### 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 r84257)
    ## Platform: x86_64-apple-darwin20 (64-bit)
    ## Running under: macOS Monterey 12.6.4
    ##
    ## Matrix products: default
    ## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
    ## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
    ##
    ## locale:
    ## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    ##
    ## 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_1.14.0
    ##  [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_1.62.1
    ## [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_0.38.1
    ## [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               S4Arrays_1.0.4
    ##  [17] progress_1.2.2                lambda.r_1.2.4
    ##  [19] curl_5.0.0                    Formula_1.2-5
    ##  [21] sass_0.4.6                    bslib_0.4.2
    ##  [23] htmlwidgets_1.6.2             plyr_1.8.8
    ##  [25] Gviz_1.44.0                   futile.options_1.0.1
    ##  [27] cachem_1.0.8                  GenomicAlignments_1.36.0
    ##  [29] mime_0.12                     lifecycle_1.0.3
    ##  [31] pkgconfig_2.0.3               Matrix_1.5-4
    ##  [33] R6_2.5.1                      fastmap_1.1.1
    ##  [35] GenomeInfoDbData_1.2.10       BiocCheck_1.36.0
    ##  [37] MatrixGenerics_1.12.0         shiny_1.7.4
    ##  [39] digest_0.6.31                 colorspace_2.1-0
    ##  [41] OrganismDbi_1.42.0            Hmisc_5.1-0
    ##  [43] RSQLite_2.3.1                 labeling_0.4.2
    ##  [45] filelock_1.0.2                colorRamps_2.3.1
    ##  [47] fansi_1.0.4                   httr_1.4.6
    ##  [49] compiler_4.3.0                bit64_4.0.5
    ##  [51] withr_2.5.0                   backports_1.4.1
    ##  [53] htmlTable_2.4.1               biocViews_1.68.1
    ##  [55] BiocParallel_1.34.1           DBI_1.1.3
    ##  [57] highr_0.10                    biomaRt_2.56.0
    ##  [59] rappdirs_0.3.3                DelayedArray_0.26.2
    ##  [61] rjson_0.2.21                  tools_4.3.0
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    ##  [65] httpuv_1.6.11                 nnet_7.3-19
    ##  [67] glue_1.6.2                    restfulr_0.0.15
    ##  [69] promises_1.2.0.1              grid_4.3.0
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    ##  [83] BiocVersion_3.17.1            pillar_1.9.0
    ##  [85] stringr_1.5.0                 later_1.3.1
    ##  [87] dplyr_1.1.2                   lattice_0.21-8
    ##  [89] deldir_1.0-6                  bit_4.0.5
    ##  [91] biovizBase_1.48.0             tidyselect_1.2.0
    ##  [93] RBGL_1.76.0                   knitr_1.42
    ##  [95] gridExtra_2.3                 bookdown_0.34
    ##  [97] ProtGenerics_1.32.0           SummarizedExperiment_1.30.1
    ##  [99] xfun_0.39                     matrixStats_0.63.0
    ## [101] stringi_1.7.12                lazyeval_0.2.2
    ## [103] yaml_2.3.7                    evaluate_0.21
    ## [105] codetools_0.2-19              interp_1.1-4
    ## [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
    ## [115] dichromat_2.0-0.1             Rcpp_1.0.10
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    ## [119] RUnit_0.4.32                  parallel_4.3.0
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