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

Felix G.M. Ernst

#### 2020-08-30

#### Package

RNAmodR 1.2.3

# 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 4.0.2 (2020-06-22)
    ## Platform: x86_64-apple-darwin17.0 (64-bit)
    ## Running under: macOS Mojave 10.14.6
    ##
    ## Matrix products: default
    ## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
    ## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
    ##
    ## locale:
    ## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    ##
    ## attached base packages:
    ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets
    ## [8] methods   base
    ##
    ## other attached packages:
    ##  [1] RNAmodR_1.2.3            Modstrings_1.4.1         RNAmodR.Data_1.2.0
    ##  [4] ExperimentHubData_1.14.1 AnnotationHubData_1.18.1 futile.logger_1.4.3
    ##  [7] ExperimentHub_1.14.2     AnnotationHub_2.20.2     BiocFileCache_1.12.1
    ## [10] dbplyr_1.4.4             GenomicFeatures_1.40.1   AnnotationDbi_1.50.3
    ## [13] Biobase_2.48.0           Rsamtools_2.4.0          Biostrings_2.56.0
    ## [16] XVector_0.28.0           rtracklayer_1.48.0       GenomicRanges_1.40.0
    ## [19] GenomeInfoDb_1.24.2      IRanges_2.22.2           S4Vectors_0.26.1
    ## [22] BiocGenerics_0.34.0      BiocStyle_2.16.0
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] backports_1.1.9               Hmisc_4.4-1
    ##   [3] plyr_1.8.6                    lazyeval_0.2.2
    ##   [5] splines_4.0.2                 BiocParallel_1.22.0
    ##   [7] ggplot2_3.3.2                 digest_0.6.25
    ##   [9] ensembldb_2.12.1              htmltools_0.5.0
    ##  [11] magick_2.4.0                  magrittr_1.5
    ##  [13] checkmate_2.0.0               memoise_1.1.0
    ##  [15] BSgenome_1.56.0               cluster_2.1.0
    ##  [17] ROCR_1.0-11                   matrixStats_0.56.0
    ##  [19] askpass_1.1                   prettyunits_1.1.1
    ##  [21] jpeg_0.1-8.1                  colorspace_1.4-1
    ##  [23] blob_1.2.1                    rappdirs_0.3.1
    ##  [25] xfun_0.16                     dplyr_1.0.2
    ##  [27] crayon_1.3.4                  RCurl_1.98-1.2
    ##  [29] jsonlite_1.7.0                graph_1.66.0
    ##  [31] VariantAnnotation_1.34.0      survival_3.2-3
    ##  [33] glue_1.4.2                    gtable_0.3.0
    ##  [35] zlibbioc_1.34.0               DelayedArray_0.14.1
    ##  [37] scales_1.1.1                  futile.options_1.0.1
    ##  [39] DBI_1.1.0                     Rcpp_1.0.5
    ##  [41] xtable_1.8-4                  progress_1.2.2
    ##  [43] htmlTable_2.0.1               foreign_0.8-80
    ##  [45] bit_4.0.4                     OrganismDbi_1.30.0
    ##  [47] Formula_1.2-3                 AnnotationForge_1.30.1
    ##  [49] htmlwidgets_1.5.1             httr_1.4.2
    ##  [51] getopt_1.20.3                 RColorBrewer_1.1-2
    ##  [53] ellipsis_0.3.1                farver_2.0.3
    ##  [55] pkgconfig_2.0.3               XML_3.99-0.5
    ##  [57] Gviz_1.32.0                   nnet_7.3-14
    ##  [59] labeling_0.3                  reshape2_1.4.4
    ##  [61] tidyselect_1.1.0              rlang_0.4.7
    ##  [63] later_1.1.0.1                 munsell_0.5.0
    ##  [65] biocViews_1.56.2              BiocVersion_3.11.1
    ##  [67] tools_4.0.2                   generics_0.0.2
    ##  [69] RSQLite_2.2.0                 evaluate_0.14
    ##  [71] stringr_1.4.0                 fastmap_1.0.1
    ##  [73] yaml_2.2.1                    knitr_1.29
    ##  [75] bit64_4.0.5                   purrr_0.3.4
    ##  [77] AnnotationFilter_1.12.0       RBGL_1.64.0
    ##  [79] mime_0.9                      formatR_1.7
    ##  [81] biomaRt_2.44.1                compiler_4.0.2
    ##  [83] rstudioapi_0.11               curl_4.3
    ##  [85] png_0.1-7                     interactiveDisplayBase_1.26.3
    ##  [87] tibble_3.0.3                  stringi_1.4.6
    ##  [89] highr_0.8                     lattice_0.20-41
    ##  [91] ProtGenerics_1.20.0           Matrix_1.2-18
    ##  [93] vctrs_0.3.4                   stringdist_0.9.6
    ##  [95] pillar_1.4.6                  BiocCheck_1.24.0
    ##  [97] lifecycle_0.2.0               RUnit_0.4.32
    ##  [99] optparse_1.6.6                BiocManager_1.30.10
    ## [101] data.table_1.13.0             bitops_1.0-6
    ## [103] colorRamps_2.3                httpuv_1.5.4
    ## [105] R6_2.4.1                      latticeExtra_0.6-29
    ## [107] bookdown_0.20                 promises_1.1.1
    ## [109] gridExtra_2.3                 codetools_0.2-16
    ## [111] lambda.r_1.2.4                dichromat_2.0-0
    ## [113] assertthat_0.2.1              SummarizedExperiment_1.18.2
    ## [115] openssl_1.4.2                 rBiopaxParser_2.28.0
    ## [117] GenomicAlignments_1.24.0      GenomeInfoDbData_1.2.3
    ## [119] hms_0.5.3                     grid_4.0.2
    ## [121] rpart_4.1-15                  rmarkdown_2.3
    ## [123] biovizBase_1.36.0             shiny_1.5.0
    ## [125] base64enc_0.1-3