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

Felix G.M. Ernst

#### 2024-10-30

#### Package

RNAmodR 1.20.0

# 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 4.4.1 (2024-06-14 ucrt)
    ## Platform: x86_64-w64-mingw32/x64
    ## 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_1.20.0           Modstrings_1.22.0        RNAmodR.Data_1.19.0
    ##  [4] ExperimentHubData_1.32.0 AnnotationHubData_1.36.0 futile.logger_1.4.3
    ##  [7] ExperimentHub_2.14.0     AnnotationHub_3.14.0     BiocFileCache_2.14.0
    ## [10] dbplyr_2.5.0             txdbmaker_1.2.0          GenomicFeatures_1.58.0
    ## [13] AnnotationDbi_1.68.0     Biobase_2.66.0           Rsamtools_2.22.0
    ## [16] Biostrings_2.74.0        XVector_0.46.0           rtracklayer_1.66.0
    ## [19] GenomicRanges_1.58.0     GenomeInfoDb_1.42.0      IRanges_2.40.0
    ## [22] S4Vectors_0.44.0         BiocGenerics_0.52.0      BiocStyle_2.34.0
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] BiocIO_1.16.0               bitops_1.0-9
    ##   [3] filelock_1.0.3              tibble_3.2.1
    ##   [5] graph_1.84.0                XML_3.99-0.17
    ##   [7] rpart_4.1.23                lifecycle_1.0.4
    ##   [9] httr2_1.0.5                 lattice_0.22-6
    ##  [11] ensembldb_2.30.0            OrganismDbi_1.48.0
    ##  [13] backports_1.5.0             magrittr_2.0.3
    ##  [15] Hmisc_5.2-0                 sass_0.4.9
    ##  [17] rmarkdown_2.28              jquerylib_0.1.4
    ##  [19] yaml_2.3.10                 RUnit_0.4.33
    ##  [21] Gviz_1.50.0                 DBI_1.2.3
    ##  [23] RColorBrewer_1.1-3          abind_1.4-8
    ##  [25] zlibbioc_1.52.0             purrr_1.0.2
    ##  [27] AnnotationFilter_1.30.0     biovizBase_1.54.0
    ##  [29] RCurl_1.98-1.16             nnet_7.3-19
    ##  [31] VariantAnnotation_1.52.0    rappdirs_0.3.3
    ##  [33] GenomeInfoDbData_1.2.13     AnnotationForge_1.48.0
    ##  [35] codetools_0.2-20            DelayedArray_0.32.0
    ##  [37] xml2_1.3.6                  tidyselect_1.2.1
    ##  [39] UCSC.utils_1.2.0            farver_2.1.2
    ##  [41] matrixStats_1.4.1           base64enc_0.1-3
    ##  [43] GenomicAlignments_1.42.0    jsonlite_1.8.9
    ##  [45] Formula_1.2-5               tools_4.4.1
    ##  [47] progress_1.2.3              snow_0.4-4
    ##  [49] stringdist_0.9.12           Rcpp_1.0.13
    ##  [51] glue_1.8.0                  gridExtra_2.3
    ##  [53] SparseArray_1.6.0           BiocBaseUtils_1.8.0
    ##  [55] xfun_0.48                   MatrixGenerics_1.18.0
    ##  [57] dplyr_1.1.4                 withr_3.0.2
    ##  [59] formatR_1.14                BiocManager_1.30.25
    ##  [61] fastmap_1.2.0               latticeExtra_0.6-30
    ##  [63] fansi_1.0.6                 digest_0.6.37
    ##  [65] R6_2.5.1                    mime_0.12
    ##  [67] colorspace_2.1-1            jpeg_0.1-10
    ##  [69] dichromat_2.0-0.1           biomaRt_2.62.0
    ##  [71] RSQLite_2.3.7               utf8_1.2.4
    ##  [73] generics_0.1.3              data.table_1.16.2
    ##  [75] prettyunits_1.2.0           httr_1.4.7
    ##  [77] htmlwidgets_1.6.4           S4Arrays_1.6.0
    ##  [79] pkgconfig_2.0.3             gtable_0.3.6
    ##  [81] blob_1.2.4                  htmltools_0.5.8.1
    ##  [83] bookdown_0.41               RBGL_1.82.0
    ##  [85] ProtGenerics_1.38.0         scales_1.3.0
    ##  [87] png_0.1-8                   colorRamps_2.3.4
    ##  [89] knitr_1.48                  lambda.r_1.2.4
    ##  [91] rstudioapi_0.17.1           reshape2_1.4.4
    ##  [93] rjson_0.2.23                checkmate_2.3.2
    ##  [95] curl_5.2.3                  biocViews_1.74.0
    ##  [97] cachem_1.1.0                stringr_1.5.1
    ##  [99] BiocVersion_3.20.0          parallel_4.4.1
    ## [101] foreign_0.8-87              restfulr_0.0.15
    ## [103] pillar_1.9.0                grid_4.4.1
    ## [105] vctrs_0.6.5                 cluster_2.1.6
    ## [107] htmlTable_2.4.3             evaluate_1.0.1
    ## [109] magick_2.8.5                tinytex_0.53
    ## [111] cli_3.6.3                   compiler_4.4.1
    ## [113] futile.options_1.0.1        rlang_1.1.4
    ## [115] crayon_1.5.3                labeling_0.4.3
    ## [117] interp_1.1-6                plyr_1.8.9
    ## [119] stringi_1.8.4               deldir_2.0-4
    ## [121] BiocParallel_1.40.0         BiocCheck_1.42.0
    ## [123] munsell_0.5.1               lazyeval_0.2.2
    ## [125] Matrix_1.7-1                BSgenome_1.74.0
    ## [127] hms_1.1.3                   bit64_4.5.2
    ## [129] ggplot2_3.5.1               KEGGREST_1.46.0
    ## [131] SummarizedExperiment_1.36.0 highr_0.11
    ## [133] ROCR_1.0-11                 memoise_2.0.1
    ## [135] bslib_0.8.0                 bit_4.5.0