# EpiTxDb: creating an EpiTxDb object

Felix G.M. Ernst

#### 2024-01-16

#### Package

EpiTxDb 1.14.1

# 1 Introduction

To create an `EpiTxDb` object a number of different functions are available. The most univeral functions are `makeEpiTxDb` and `makeEpiTxDbFromGRanges`. `makeEpiTxDb` uses four `data.frame`s as input, whereas `makeEpiTxDbFromGRanges` is a wrapper for information available as a `GRanges` object.

The other functions are `makeEpiTxDbFromRMBase` and `makeEpiTxDbFromtRNAdb`, which are aimed to make data available from the RMBase v2.0 database (Xuan et al. 2017; Sun et al. 2015) or the tRNAdb (Jühling et al. 2009; Sprinzl and Vassilenko 2005). However, before creating your `EpiTxDb` objects, have a look at the already available resources for `H. sapiens`, `M. musculus` and `S. cerevisiae.`

Additional metadata can be provided as separate `data.frame` for all functions. The `data.frame` must have two columns `name` and `value`.

    library(GenomicRanges)
    library(EpiTxDb)

# 2 `makeEpiTxDb` and `makeEpiTxDbFromGRanges`

The creation of an etdb object is quite easy starting with a `GRanges` object.

    gr <- GRanges(seqnames = "test",
                  ranges = IRanges::IRanges(1,1),
                  strand = "+",
                  DataFrame(mod_id = 1L,
                            mod_type = "Am",
                            mod_name = "Am_1"))
    etdb <- makeEpiTxDbFromGRanges(gr, metadata = data.frame(name = "test",
                                                             value = "Yes"))

    ## Creating EpiTxDb object ... done

    etdb

    ## EpiTxDb object:
    ## # Db type: EpiTxDb
    ## # Supporting package: EpiTxDb
    ## # test: Yes
    ## # Nb of modifications: 1
    ## # Db created by: EpiTxDb package from Bioconductor
    ## # Creation time: 2024-01-16 17:08:28 -0500 (Tue, 16 Jan 2024)
    ## # EpiTxDb version at creation time: 1.14.1
    ## # RSQLite version at creation time: 2.3.4
    ## # DBSCHEMAVERSION: 1.0

    metadata(etdb)

Additional data can be provided via the metadata columns of the `GRanges` object. For supported columns have a look at `?makeEpiTxDb` or `?makeEpiTxDbFromGRanges`.

# 3 `makeEpiTxDbFromtRNAdb`

The information of the tRNAdb can be accessed via the `tRNAdbImport` package using the RNA database. As a result a `ModRNAStringSet` object is returned from which the modifications can be extracted using `separate()`.

The only input require is a valid organism name returned by `listAvailableOrganismsFromtRNAdb()`.

    # Currently not run since the server is not available
    etdb <- makeEpiTxDbFromtRNAdb("Saccharomyces cerevisiae")
    etdb

For additional information have a look at `?makeEpiTxDbFromtRNAdb`. The result returned from the tRNAdb is also available as `GRanges` object, if `gettRNAdbDataAsGRanges()` is used.

# 4 `makeEpiTxDbFromRMBase`

Analogous to the example above `makeEpiTxDbFromRMBase()` will download the data from the RMBase v2.0. Three inputs are required, `organism`, `genome` and `modtype`, which have to valid bia the functions `listAvailableOrganismsFromRMBase()`, `.listAvailableGenomesFromRMBase()` and `listAvailableModFromRMBase`.

    etdb <- makeEpiTxDbFromRMBase(organism = "Saccharomyces cerevisiae",
                                  genome = "sacCer3",
                                  modtype = "m1A")

Internally, the files are cached using the `BiocFileCache` package and passed to `makeEpiTxDbFromRMBaseFiles()`, which can also be used with locally stored files. The resuls for creating the `EpiTxDb` class are processed from these files via the `getRMBaseDataAsGRanges()` function.

# 5 Session info

    sessionInfo()

    ## R version 4.3.2 Patched (2023-11-01 r85457)
    ## Platform: x86_64-apple-darwin20 (64-bit)
    ## Running under: macOS Monterey 12.7.1
    ##
    ## 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] EpiTxDb_1.14.1       Modstrings_1.18.0    Biostrings_2.70.1
    ##  [4] XVector_0.42.0       AnnotationDbi_1.64.1 Biobase_2.62.0
    ##  [7] GenomicRanges_1.54.1 GenomeInfoDb_1.38.5  IRanges_2.36.0
    ## [10] S4Vectors_0.40.2     BiocGenerics_0.48.1  BiocStyle_2.30.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] tRNAdbImport_1.20.1         tidyselect_1.2.0
    ##  [3] dplyr_1.1.4                 blob_1.2.4
    ##  [5] filelock_1.0.3              bitops_1.0-7
    ##  [7] fastmap_1.1.1               RCurl_1.98-1.14
    ##  [9] BiocFileCache_2.10.1        GenomicAlignments_1.38.2
    ## [11] XML_3.99-0.16               digest_0.6.34
    ## [13] lifecycle_1.0.4             KEGGREST_1.42.0
    ## [15] RSQLite_2.3.4               magrittr_2.0.3
    ## [17] compiler_4.3.2              rlang_1.1.3
    ## [19] sass_0.4.8                  progress_1.2.3
    ## [21] tools_4.3.2                 utf8_1.2.4
    ## [23] yaml_2.3.8                  rtracklayer_1.62.0
    ## [25] knitr_1.45                  prettyunits_1.2.0
    ## [27] S4Arrays_1.2.0              bit_4.0.5
    ## [29] curl_5.2.0                  DelayedArray_0.28.0
    ## [31] xml2_1.3.6                  abind_1.4-5
    ## [33] BiocParallel_1.36.0         grid_4.3.2
    ## [35] fansi_1.0.6                 colorspace_2.1-0
    ## [37] ggplot2_3.4.4               scales_1.3.0
    ## [39] biomaRt_2.58.0              SummarizedExperiment_1.32.0
    ## [41] cli_3.6.2                   rmarkdown_2.25
    ## [43] crayon_1.5.2                generics_0.1.3
    ## [45] httr_1.4.7                  rjson_0.2.21
    ## [47] DBI_1.2.1                   cachem_1.0.8
    ## [49] stringr_1.5.1               zlibbioc_1.48.0
    ## [51] parallel_4.3.2              BiocManager_1.30.22
    ## [53] restfulr_0.0.15             matrixStats_1.2.0
    ## [55] vctrs_0.6.5                 Matrix_1.6-5
    ## [57] jsonlite_1.8.8              bookdown_0.37
    ## [59] hms_1.1.3                   bit64_4.0.5
    ## [61] GenomicFeatures_1.54.1      jquerylib_0.1.4
    ## [63] glue_1.7.0                  codetools_0.2-19
    ## [65] gtable_0.3.4                stringi_1.8.3
    ## [67] BiocIO_1.12.0               munsell_0.5.0
    ## [69] tibble_3.2.1                pillar_1.9.0
    ## [71] rappdirs_0.3.3              htmltools_0.5.7
    ## [73] GenomeInfoDbData_1.2.11     httr2_1.0.0
    ## [75] R6_2.5.1                    dbplyr_2.4.0
    ## [77] lattice_0.22-5              evaluate_0.23
    ## [79] png_0.1-8                   Rsamtools_2.18.0
    ## [81] memoise_2.0.1               bslib_0.6.1
    ## [83] Structstrings_1.18.0        tRNA_1.20.0
    ## [85] SparseArray_1.2.3           xfun_0.41
    ## [87] MatrixGenerics_1.14.0       pkgconfig_2.0.3

# References

Jühling, Frank, Mario Mörl, Roland K. Hartmann, Mathias Sprinzl, Peter F. Stadler, and Joern Pütz. 2009. “TRNAdb 2009: Compilation of tRNA Sequences and tRNA Genes.” Nucleic Acids Research 37: D159–D162. https://doi.org/10.1093/nar/gkn772.

Sprinzl, Mathias, and Konstantin S. Vassilenko. 2005. “Compilation of tRNA Sequences and Sequences of tRNA Genes.” Nucleic Acids Research 33: D139–D140. https://doi.org/10.1093/nar/gki012.

Sun, Wen-Ju, Jun-Hao Li, Shun Liu, Jie Wu, Hui Zhou, Liang-Hu Qu, and Jian-Hua Yang. 2015. “RMBase: a resource for decoding the landscape of RNA modifications from high-throughput sequencing data.” Nucleic Acids Research 44 (D1): D259–D265. https://doi.org/10.1093/nar/gkv1036.

Xuan, Jia-Jia, Wen-Ju Sun, Peng-Hui Lin, Ke-Ren Zhou, Shun Liu, Ling-Ling Zheng, Liang-Hu Qu, and Jian-Hua Yang. 2017. “RMBase v2.0: deciphering the map of RNA modifications from epitranscriptome sequencing data.” Nucleic Acids Research 46 (D1): D327–D334. https://doi.org/10.1093/nar/gkx934.