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

Felix G.M. Ernst

#### 2024-05-01

#### Package

RNAmodR 1.18.0

# 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 4.4.0 beta (2024-04-14 r86421)
    ## Platform: x86_64-apple-darwin20
    ## Running under: macOS Monterey 12.7.1
    ##
    ## Matrix products: default
    ## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib
    ## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
    ##
    ## locale:
    ## [1] en_US.UTF-8/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_1.18.0       Modstrings_1.20.0    Biostrings_2.72.0
    ##  [4] XVector_0.44.0       GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
    ##  [7] IRanges_2.38.0       S4Vectors_0.42.0     BiocGenerics_0.50.0
    ## [10] BiocStyle_2.32.0
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] RColorBrewer_1.1-3          rstudioapi_0.16.0
    ##   [3] jsonlite_1.8.8              magrittr_2.0.3
    ##   [5] GenomicFeatures_1.56.0      rmarkdown_2.26
    ##   [7] BiocIO_1.14.0               zlibbioc_1.50.0
    ##   [9] vctrs_0.6.5                 ROCR_1.0-11
    ##  [11] memoise_2.0.1               Rsamtools_2.20.0
    ##  [13] RCurl_1.98-1.14             base64enc_0.1-3
    ##  [15] htmltools_0.5.8.1           S4Arrays_1.4.0
    ##  [17] progress_1.2.3              curl_5.2.1
    ##  [19] SparseArray_1.4.0           Formula_1.2-5
    ##  [21] sass_0.4.9                  bslib_0.7.0
    ##  [23] htmlwidgets_1.6.4           plyr_1.8.9
    ##  [25] Gviz_1.48.0                 httr2_1.0.1
    ##  [27] cachem_1.0.8                GenomicAlignments_1.40.0
    ##  [29] lifecycle_1.0.4             pkgconfig_2.0.3
    ##  [31] Matrix_1.7-0                R6_2.5.1
    ##  [33] fastmap_1.1.1               GenomeInfoDbData_1.2.12
    ##  [35] MatrixGenerics_1.16.0       digest_0.6.35
    ##  [37] colorspace_2.1-0            AnnotationDbi_1.66.0
    ##  [39] Hmisc_5.1-2                 RSQLite_2.3.6
    ##  [41] filelock_1.0.3              colorRamps_2.3.4
    ##  [43] fansi_1.0.6                 httr_1.4.7
    ##  [45] abind_1.4-5                 compiler_4.4.0
    ##  [47] bit64_4.0.5                 htmlTable_2.4.2
    ##  [49] backports_1.4.1             BiocParallel_1.38.0
    ##  [51] DBI_1.2.2                   biomaRt_2.60.0
    ##  [53] rappdirs_0.3.3              DelayedArray_0.30.0
    ##  [55] rjson_0.2.21                tools_4.4.0
    ##  [57] foreign_0.8-86              nnet_7.3-19
    ##  [59] glue_1.7.0                  restfulr_0.0.15
    ##  [61] grid_4.4.0                  checkmate_2.3.1
    ##  [63] reshape2_1.4.4              cluster_2.1.6
    ##  [65] generics_0.1.3              gtable_0.3.5
    ##  [67] BSgenome_1.72.0             ensembldb_2.28.0
    ##  [69] data.table_1.15.4           hms_1.1.3
    ##  [71] xml2_1.3.6                  utf8_1.2.4
    ##  [73] pillar_1.9.0                stringr_1.5.1
    ##  [75] dplyr_1.1.4                 BiocFileCache_2.12.0
    ##  [77] lattice_0.22-6              rtracklayer_1.64.0
    ##  [79] bit_4.0.5                   deldir_2.0-4
    ##  [81] biovizBase_1.52.0           tidyselect_1.2.1
    ##  [83] knitr_1.46                  gridExtra_2.3
    ##  [85] bookdown_0.39               ProtGenerics_1.36.0
    ##  [87] SummarizedExperiment_1.34.0 xfun_0.43
    ##  [89] Biobase_2.64.0              matrixStats_1.3.0
    ##  [91] stringi_1.8.3               UCSC.utils_1.0.0
    ##  [93] lazyeval_0.2.2              yaml_2.3.8
    ##  [95] evaluate_0.23               codetools_0.2-20
    ##  [97] interp_1.1-6                tibble_3.2.1
    ##  [99] BiocManager_1.30.22         cli_3.6.2
    ## [101] rpart_4.1.23                munsell_0.5.1
    ## [103] jquerylib_0.1.4             dichromat_2.0-0.1
    ## [105] Rcpp_1.0.12                 dbplyr_2.5.0
    ## [107] png_0.1-8                   XML_3.99-0.16.1
    ## [109] parallel_4.4.0              ggplot2_3.5.1
    ## [111] blob_1.2.4                  prettyunits_1.2.0
    ## [113] latticeExtra_0.6-30         jpeg_0.1-10
    ## [115] AnnotationFilter_1.28.0     bitops_1.0-7
    ## [117] txdbmaker_1.0.0             VariantAnnotation_1.50.0
    ## [119] scales_1.3.0                crayon_1.5.2
    ## [121] rlang_1.1.3                 KEGGREST_1.44.0