# EpiTxDb: creating an EpiTxDb object

Felix G.M. Ernst

#### 2025-07-23

#### Package

EpiTxDb 1.21.1

# 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`.

    ## Warning: replacing previous import 'GenomicFeatures::makeTxDbPackage' by
    ## 'txdbmaker::makeTxDbPackage' when loading 'EpiTxDb'

    ## Warning: replacing previous import
    ## 'GenomicFeatures::supportedUCSCFeatureDbTables' by
    ## 'txdbmaker::supportedUCSCFeatureDbTables' when loading 'EpiTxDb'

    ## Warning: replacing previous import 'GenomicFeatures::makeTxDbFromGFF' by
    ## 'txdbmaker::makeTxDbFromGFF' when loading 'EpiTxDb'

    ## Warning: replacing previous import
    ## 'GenomicFeatures::makeTxDbPackageFromBiomart' by
    ## 'txdbmaker::makeTxDbPackageFromBiomart' when loading 'EpiTxDb'

    ## Warning: replacing previous import
    ## 'GenomicFeatures::supportedMiRBaseBuildValues' by
    ## 'txdbmaker::supportedMiRBaseBuildValues' when loading 'EpiTxDb'

    ## Warning: replacing previous import
    ## 'GenomicFeatures::supportedUCSCFeatureDbTracks' by
    ## 'txdbmaker::supportedUCSCFeatureDbTracks' when loading 'EpiTxDb'

    ## Warning: replacing previous import 'GenomicFeatures::browseUCSCtrack' by
    ## 'txdbmaker::browseUCSCtrack' when loading 'EpiTxDb'

    ## Warning: replacing previous import 'GenomicFeatures::getChromInfoFromBiomart'
    ## by 'txdbmaker::getChromInfoFromBiomart' when loading 'EpiTxDb'

    ## Warning: replacing previous import 'GenomicFeatures::makeTxDbFromGRanges' by
    ## 'txdbmaker::makeTxDbFromGRanges' when loading 'EpiTxDb'

    ## Warning: replacing previous import 'GenomicFeatures::supportedUCSCtables' by
    ## 'txdbmaker::supportedUCSCtables' when loading 'EpiTxDb'

    ## Warning: replacing previous import 'GenomicFeatures::makeTxDbFromBiomart' by
    ## 'txdbmaker::makeTxDbFromBiomart' when loading 'EpiTxDb'

    ## Warning: replacing previous import 'GenomicFeatures::UCSCFeatureDbTableSchema'
    ## by 'txdbmaker::UCSCFeatureDbTableSchema' when loading 'EpiTxDb'

    ## Warning: replacing previous import 'GenomicFeatures::makeTxDbFromEnsembl' by
    ## 'txdbmaker::makeTxDbFromEnsembl' when loading 'EpiTxDb'

    ## Warning: replacing previous import 'GenomicFeatures::makeTxDb' by
    ## 'txdbmaker::makeTxDb' when loading 'EpiTxDb'

    ## Warning: replacing previous import 'GenomicFeatures::makeFDbPackageFromUCSC' by
    ## 'txdbmaker::makeFDbPackageFromUCSC' when loading 'EpiTxDb'

    ## Warning: replacing previous import 'GenomicFeatures::makeTxDbFromUCSC' by
    ## 'txdbmaker::makeTxDbFromUCSC' when loading 'EpiTxDb'

    ## Warning: replacing previous import 'GenomicFeatures::makeTxDbPackageFromUCSC'
    ## by 'txdbmaker::makeTxDbPackageFromUCSC' when loading 'EpiTxDb'

    ## Warning: replacing previous import 'GenomicFeatures::makePackageName' by
    ## 'txdbmaker::makePackageName' when loading 'EpiTxDb'

    ## Warning: replacing previous import 'GenomicFeatures::makeFeatureDbFromUCSC' by
    ## 'txdbmaker::makeFeatureDbFromUCSC' when loading 'EpiTxDb'

    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: 2025-07-23 18:56:31 -0400 (Wed, 23 Jul 2025)
    ## # EpiTxDb version at creation time: 1.21.1
    ## # RSQLite version at creation time: 2.4.2
    ## # 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 (2025-06-13 ucrt)
    ## Platform: x86_64-w64-mingw32/x64
    ## Running under: Windows Server 2022 x64 (build 20348)
    ##
    ## Matrix products: default
    ##   LAPACK version 3.12.1
    ##
    ## 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] EpiTxDb_1.21.1       Modstrings_1.25.0    Biostrings_2.77.2
    ##  [4] XVector_0.49.0       AnnotationDbi_1.71.0 Biobase_2.69.0
    ##  [7] GenomicRanges_1.61.1 Seqinfo_0.99.2       IRanges_2.43.0
    ## [10] S4Vectors_0.47.0     BiocGenerics_0.55.0  generics_0.1.4
    ## [13] BiocStyle_2.37.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] tRNAdbImport_1.27.0         tidyselect_1.2.1
    ##  [3] farver_2.1.2                dplyr_1.1.4
    ##  [5] blob_1.2.4                  filelock_1.0.3
    ##  [7] bitops_1.0-9                fastmap_1.2.0
    ##  [9] RCurl_1.98-1.17             BiocFileCache_2.99.5
    ## [11] GenomicAlignments_1.45.1    rex_1.2.1
    ## [13] XML_3.99-0.18               digest_0.6.37
    ## [15] lifecycle_1.0.4             KEGGREST_1.49.1
    ## [17] RSQLite_2.4.2               magrittr_2.0.3
    ## [19] compiler_4.5.1              rlang_1.1.6
    ## [21] sass_0.4.10                 progress_1.2.3
    ## [23] tools_4.5.1                 yaml_2.3.10
    ## [25] rtracklayer_1.69.1          knitr_1.50
    ## [27] prettyunits_1.2.0           S4Arrays_1.9.1
    ## [29] bit_4.6.0                   curl_6.4.0
    ## [31] DelayedArray_0.35.2         RColorBrewer_1.1-3
    ## [33] xml2_1.3.8                  abind_1.4-8
    ## [35] BiocParallel_1.43.4         txdbmaker_1.5.6
    ## [37] grid_4.5.1                  ggplot2_3.5.2
    ## [39] scales_1.4.0                dichromat_2.0-0.1
    ## [41] biomaRt_2.65.0              SummarizedExperiment_1.39.1
    ## [43] cli_3.6.5                   rmarkdown_2.29
    ## [45] crayon_1.5.3                httr_1.4.7
    ## [47] rjson_0.2.23                DBI_1.2.3
    ## [49] cachem_1.1.0                stringr_1.5.1
    ## [51] parallel_4.5.1              BiocManager_1.30.26
    ## [53] restfulr_0.0.16             matrixStats_1.5.0
    ## [55] vctrs_0.6.5                 Matrix_1.7-3
    ## [57] jsonlite_2.0.0              bookdown_0.43
    ## [59] hms_1.1.3                   bit64_4.6.0-1
    ## [61] GenomicFeatures_1.61.5      jquerylib_0.1.4
    ## [63] glue_1.8.0                  codetools_0.2-20
    ## [65] gtable_0.3.6                stringi_1.8.7
    ## [67] GenomeInfoDb_1.45.9         BiocIO_1.19.0
    ## [69] UCSC.utils_1.5.0            tibble_3.3.0
    ## [71] pillar_1.11.0               rappdirs_0.3.3
    ## [73] htmltools_0.5.8.1           R6_2.6.1
    ## [75] dbplyr_2.5.0                httr2_1.2.1
    ## [77] evaluate_1.0.4              lattice_0.22-7
    ## [79] png_0.1-8                   Rsamtools_2.25.1
    ## [81] memoise_2.0.1               bslib_0.9.0
    ## [83] Structstrings_1.25.0        tRNA_1.27.0
    ## [85] SparseArray_1.9.1           xfun_0.52
    ## [87] MatrixGenerics_1.21.0       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.