# EpiTxDb: creating an EpiTxDb object

Felix G.M. Ernst

#### 2022-11-01

#### Package

EpiTxDb 1.10.0

# 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: 2022-11-01 20:09:20 -0400 (Tue, 01 Nov 2022)
    ## # EpiTxDb version at creation time: 1.10.0
    ## # RSQLite version at creation time: 2.2.18
    ## # 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()`.

    etdb <- makeEpiTxDbFromtRNAdb("Saccharomyces cerevisiae")

    ## Loading data from tRNAdb ...

    ## Assembling data ...

    ## Creating EpiTxDb object ... done

    etdb

    ## EpiTxDb object:
    ## # Db type: EpiTxDb
    ## # Supporting package: EpiTxDb
    ## # Nb of modifications: 557
    ## # Db created by: EpiTxDb package from Bioconductor
    ## # Creation time: 2022-11-01 20:09:48 -0400 (Tue, 01 Nov 2022)
    ## # EpiTxDb version at creation time: 1.10.0
    ## # RSQLite version at creation time: 2.2.18
    ## # DBSCHEMAVERSION: 1.0

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 4.2.1 (2022-06-23 ucrt)
    ## Platform: x86_64-w64-mingw32/x64 (64-bit)
    ## Running under: Windows Server 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
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] EpiTxDb_1.10.0       Modstrings_1.14.0    Biostrings_2.66.0
    ##  [4] XVector_0.38.0       AnnotationDbi_1.60.0 Biobase_2.58.0
    ##  [7] GenomicRanges_1.50.0 GenomeInfoDb_1.34.0  IRanges_2.32.0
    ## [10] S4Vectors_0.36.0     BiocGenerics_0.44.0  BiocStyle_2.26.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] MatrixGenerics_1.10.0       httr_1.4.4
    ##  [3] sass_0.4.2                  bit64_4.0.5
    ##  [5] jsonlite_1.8.3              bslib_0.4.0
    ##  [7] assertthat_0.2.1            BiocManager_1.30.19
    ##  [9] BiocFileCache_2.6.0         blob_1.2.3
    ## [11] GenomeInfoDbData_1.2.9      Rsamtools_2.14.0
    ## [13] yaml_2.3.6                  progress_1.2.2
    ## [15] lattice_0.20-45             pillar_1.8.1
    ## [17] RSQLite_2.2.18              glue_1.6.2
    ## [19] digest_0.6.30               Structstrings_1.14.0
    ## [21] colorspace_2.0-3            tRNA_1.16.0
    ## [23] Matrix_1.5-1                htmltools_0.5.3
    ## [25] XML_3.99-0.12               pkgconfig_2.0.3
    ## [27] biomaRt_2.54.0              bookdown_0.29
    ## [29] zlibbioc_1.44.0             scales_1.2.1
    ## [31] BiocParallel_1.32.0         tibble_3.1.8
    ## [33] KEGGREST_1.38.0             ggplot2_3.3.6
    ## [35] generics_0.1.3              ellipsis_0.3.2
    ## [37] SummarizedExperiment_1.28.0 cachem_1.0.6
    ## [39] GenomicFeatures_1.50.0      cli_3.4.1
    ## [41] magrittr_2.0.3              crayon_1.5.2
    ## [43] memoise_2.0.1               evaluate_0.17
    ## [45] fansi_1.0.3                 xml2_1.3.3
    ## [47] tools_4.2.1                 prettyunits_1.1.1
    ## [49] hms_1.1.2                   matrixStats_0.62.0
    ## [51] BiocIO_1.8.0                lifecycle_1.0.3
    ## [53] stringr_1.4.1               munsell_0.5.0
    ## [55] tRNAdbImport_1.16.0         DelayedArray_0.24.0
    ## [57] compiler_4.2.1              jquerylib_0.1.4
    ## [59] rlang_1.0.6                 grid_4.2.1
    ## [61] RCurl_1.98-1.9              rjson_0.2.21
    ## [63] rappdirs_0.3.3              bitops_1.0-7
    ## [65] rmarkdown_2.17              gtable_0.3.1
    ## [67] restfulr_0.0.15             codetools_0.2-18
    ## [69] DBI_1.1.3                   curl_4.3.3
    ## [71] R6_2.5.1                    GenomicAlignments_1.34.0
    ## [73] knitr_1.40                  dplyr_1.0.10
    ## [75] rtracklayer_1.58.0          fastmap_1.1.0
    ## [77] bit_4.0.4                   utf8_1.2.2
    ## [79] filelock_1.0.2              stringi_1.7.8
    ## [81] parallel_4.2.1              Rcpp_1.0.9
    ## [83] vctrs_0.5.0                 png_0.1-7
    ## [85] dbplyr_2.2.1                tidyselect_1.2.0
    ## [87] xfun_0.34

# 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.