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

Felix G.M. Ernst

#### 2022-11-01

#### Package

RNAmodR 1.12.0

# 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 4.2.1 Patched (2022-07-09 r82577)
    ## Platform: x86_64-apple-darwin17.0 (64-bit)
    ## Running under: macOS Big Sur ... 10.16
    ##
    ## Matrix products: default
    ## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
    ## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
    ##
    ## locale:
    ## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_GB/en_US.UTF-8
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] RNAmodR_1.12.0           Modstrings_1.14.0        RNAmodR.Data_1.11.0
    ##  [4] ExperimentHubData_1.24.0 AnnotationHubData_1.28.0 futile.logger_1.4.3
    ##  [7] ExperimentHub_2.6.0      AnnotationHub_3.6.0      BiocFileCache_2.6.0
    ## [10] dbplyr_2.2.1             GenomicFeatures_1.50.0   AnnotationDbi_1.60.0
    ## [13] Biobase_2.58.0           Rsamtools_2.14.0         Biostrings_2.66.0
    ## [16] XVector_0.38.0           rtracklayer_1.58.0       GenomicRanges_1.50.0
    ## [19] GenomeInfoDb_1.34.0      IRanges_2.32.0           S4Vectors_0.36.0
    ## [22] BiocGenerics_0.44.0      BiocStyle_2.26.0
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] utf8_1.2.2                    RUnit_0.4.32
    ##   [3] tidyselect_1.2.0              RSQLite_2.2.18
    ##   [5] htmlwidgets_1.5.4             grid_4.2.1
    ##   [7] BiocParallel_1.32.0           munsell_0.5.0
    ##   [9] codetools_0.2-18              interp_1.1-3
    ##  [11] withr_2.5.0                   colorspace_2.0-3
    ##  [13] filelock_1.0.2                OrganismDbi_1.40.0
    ##  [15] highr_0.9                     knitr_1.40
    ##  [17] rstudioapi_0.14               ROCR_1.0-11
    ##  [19] MatrixGenerics_1.10.0         labeling_0.4.2
    ##  [21] GenomeInfoDbData_1.2.9        bit64_4.0.5
    ##  [23] farver_2.1.1                  vctrs_0.5.0
    ##  [25] generics_0.1.3                lambda.r_1.2.4
    ##  [27] xfun_0.34                     biovizBase_1.46.0
    ##  [29] R6_2.5.1                      AnnotationFilter_1.22.0
    ##  [31] bitops_1.0-7                  cachem_1.0.6
    ##  [33] DelayedArray_0.24.0           assertthat_0.2.1
    ##  [35] promises_1.2.0.1              BiocIO_1.8.0
    ##  [37] scales_1.2.1                  nnet_7.3-18
    ##  [39] gtable_0.3.1                  biocViews_1.66.0
    ##  [41] ensembldb_2.22.0              rlang_1.0.6
    ##  [43] splines_4.2.1                 lazyeval_0.2.2
    ##  [45] dichromat_2.0-0.1             checkmate_2.1.0
    ##  [47] BiocManager_1.30.19           yaml_2.3.6
    ##  [49] reshape2_1.4.4                backports_1.4.1
    ##  [51] httpuv_1.6.6                  Hmisc_4.7-1
    ##  [53] RBGL_1.74.0                   tools_4.2.1
    ##  [55] bookdown_0.29                 ggplot2_3.3.6
    ##  [57] ellipsis_0.3.2                jquerylib_0.1.4
    ##  [59] RColorBrewer_1.1-3            Rcpp_1.0.9
    ##  [61] plyr_1.8.7                    base64enc_0.1-3
    ##  [63] progress_1.2.2                zlibbioc_1.44.0
    ##  [65] purrr_0.3.5                   RCurl_1.98-1.9
    ##  [67] prettyunits_1.1.1             rpart_4.1.19
    ##  [69] deldir_1.0-6                  SummarizedExperiment_1.28.0
    ##  [71] cluster_2.1.4                 colorRamps_2.3.1
    ##  [73] magrittr_2.0.3                data.table_1.14.4
    ##  [75] futile.options_1.0.1          magick_2.7.3
    ##  [77] ProtGenerics_1.30.0           matrixStats_0.62.0
    ##  [79] hms_1.1.2                     mime_0.12
    ##  [81] evaluate_0.17                 xtable_1.8-4
    ##  [83] XML_3.99-0.12                 jpeg_0.1-9
    ##  [85] gridExtra_2.3                 compiler_4.2.1
    ##  [87] biomaRt_2.54.0                tibble_3.1.8
    ##  [89] crayon_1.5.2                  htmltools_0.5.3
    ##  [91] later_1.3.0                   Formula_1.2-4
    ##  [93] DBI_1.1.3                     formatR_1.12
    ##  [95] rappdirs_0.3.3                Matrix_1.5-1
    ##  [97] cli_3.4.1                     parallel_4.2.1
    ##  [99] Gviz_1.42.0                   pkgconfig_2.0.3
    ## [101] GenomicAlignments_1.34.0      foreign_0.8-83
    ## [103] xml2_1.3.3                    bslib_0.4.0
    ## [105] stringdist_0.9.9              AnnotationForge_1.40.0
    ## [107] BiocCheck_1.34.0              stringr_1.4.1
    ## [109] VariantAnnotation_1.44.0      digest_0.6.30
    ## [111] graph_1.76.0                  rmarkdown_2.17
    ## [113] htmlTable_2.4.1               restfulr_0.0.15
    ## [115] curl_4.3.3                    shiny_1.7.3
    ## [117] rjson_0.2.21                  lifecycle_1.0.3
    ## [119] jsonlite_1.8.3                BSgenome_1.66.0
    ## [121] fansi_1.0.3                   pillar_1.8.1
    ## [123] lattice_0.20-45               KEGGREST_1.38.0
    ## [125] fastmap_1.1.0                 httr_1.4.4
    ## [127] survival_3.4-0                interactiveDisplayBase_1.36.0
    ## [129] glue_1.6.2                    png_0.1-7
    ## [131] BiocVersion_3.16.0            bit_4.0.4
    ## [133] stringi_1.7.8                 sass_0.4.2
    ## [135] blob_1.2.3                    latticeExtra_0.6-30
    ## [137] memoise_2.0.1                 dplyr_1.0.10