# EpiTxDb: creating an EpiTxDb object

Felix G.M. Ernst

#### 2025-10-29

#### Package

EpiTxDb 1.22.0

# 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: 2025-10-29 22:43:13 -0400 (Wed, 29 Oct 2025)
    ## # EpiTxDb version at creation time: 1.22.0
    ## # RSQLite version at creation time: 2.4.3
    ## # 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 = "yeast",
                                  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 4.5.1 Patched (2025-09-10 r88807)
    ## Platform: aarch64-apple-darwin20
    ## Running under: macOS Ventura 13.7.7
    ##
    ## Matrix products: default
    ## BLAS:   /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
    ## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
    ##
    ## 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_1.22.0       Modstrings_1.26.0    Biostrings_2.78.0
    ##  [4] XVector_0.50.0       AnnotationDbi_1.72.0 Biobase_2.70.0
    ##  [7] GenomicRanges_1.62.0 Seqinfo_1.0.0        IRanges_2.44.0
    ## [10] S4Vectors_0.48.0     BiocGenerics_0.56.0  generics_0.1.4
    ## [13] BiocStyle_2.38.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] tRNAdbImport_1.28.0         tidyselect_1.2.1
    ##  [3] farver_2.1.2                dplyr_1.1.4
    ##  [5] blob_1.2.4                  S7_0.2.0
    ##  [7] filelock_1.0.3              bitops_1.0-9
    ##  [9] fastmap_1.2.0               RCurl_1.98-1.17
    ## [11] BiocFileCache_3.0.0         GenomicAlignments_1.46.0
    ## [13] rex_1.2.1                   XML_3.99-0.19
    ## [15] digest_0.6.37               lifecycle_1.0.4
    ## [17] KEGGREST_1.50.0             RSQLite_2.4.3
    ## [19] magrittr_2.0.4              compiler_4.5.1
    ## [21] progress_1.2.3              rlang_1.1.6
    ## [23] sass_0.4.10                 tools_4.5.1
    ## [25] yaml_2.3.10                 rtracklayer_1.70.0
    ## [27] knitr_1.50                  prettyunits_1.2.0
    ## [29] S4Arrays_1.10.0             bit_4.6.0
    ## [31] curl_7.0.0                  DelayedArray_0.36.0
    ## [33] xml2_1.4.1                  RColorBrewer_1.1-3
    ## [35] abind_1.4-8                 BiocParallel_1.44.0
    ## [37] txdbmaker_1.6.0             grid_4.5.1
    ## [39] ggplot2_4.0.0               scales_1.4.0
    ## [41] dichromat_2.0-0.1           biomaRt_2.66.0
    ## [43] SummarizedExperiment_1.40.0 cli_3.6.5
    ## [45] rmarkdown_2.30              crayon_1.5.3
    ## [47] httr_1.4.7                  rjson_0.2.23
    ## [49] DBI_1.2.3                   cachem_1.1.0
    ## [51] stringr_1.5.2               parallel_4.5.1
    ## [53] BiocManager_1.30.26         restfulr_0.0.16
    ## [55] matrixStats_1.5.0           vctrs_0.6.5
    ## [57] Matrix_1.7-4                jsonlite_2.0.0
    ## [59] bookdown_0.45               hms_1.1.4
    ## [61] bit64_4.6.0-1               GenomicFeatures_1.62.0
    ## [63] jquerylib_0.1.4             glue_1.8.0
    ## [65] codetools_0.2-20            gtable_0.3.6
    ## [67] stringi_1.8.7               GenomeInfoDb_1.46.0
    ## [69] BiocIO_1.20.0               UCSC.utils_1.6.0
    ## [71] tibble_3.3.0                pillar_1.11.1
    ## [73] rappdirs_0.3.3              htmltools_0.5.8.1
    ## [75] R6_2.6.1                    dbplyr_2.5.1
    ## [77] httr2_1.2.1                 evaluate_1.0.5
    ## [79] lattice_0.22-7              png_0.1-8
    ## [81] Rsamtools_2.26.0            cigarillo_1.0.0
    ## [83] memoise_2.0.1               bslib_0.9.0
    ## [85] Structstrings_1.26.0        tRNA_1.28.0
    ## [87] SparseArray_1.10.0          xfun_0.53
    ## [89] MatrixGenerics_1.22.0       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 (suppl_1): D159–62. 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 (suppl_1): D139–40. 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): D259–65. 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): D327–34. https://doi.org/10.1093/nar/gkx934.