# EpiTxDb: creating an EpiTxDb object

Felix G.M. Ernst

#### 2021-05-19

#### Package

EpiTxDb 1.4.0

# 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: 2021-05-19 19:57:57 -0400 (Wed, 19 May 2021)
    ## # EpiTxDb version at creation time: 1.4.0
    ## # RSQLite version at creation time: 2.2.7
    ## # 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: 2021-05-19 19:58:28 -0400 (Wed, 19 May 2021)
    ## # EpiTxDb version at creation time: 1.4.0
    ## # RSQLite version at creation time: 2.2.7
    ## # 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 4.1.0 RC (2021-05-10 r80283)
    ## Platform: x86_64-w64-mingw32/x64 (64-bit)
    ## Running under: Windows Server x64 (build 17763)
    ##
    ## Matrix products: default
    ##
    ## locale:
    ## [1] LC_COLLATE=C
    ## [2] LC_CTYPE=English_United States.1252
    ## [3] LC_MONETARY=English_United States.1252
    ## [4] LC_NUMERIC=C
    ## [5] LC_TIME=English_United States.1252
    ##
    ## attached base packages:
    ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets
    ## [8] methods   base
    ##
    ## other attached packages:
    ##  [1] EpiTxDb_1.4.0        Modstrings_1.8.0     Biostrings_2.60.0
    ##  [4] XVector_0.32.0       AnnotationDbi_1.54.0 Biobase_2.52.0
    ##  [7] GenomicRanges_1.44.0 GenomeInfoDb_1.28.0  IRanges_2.26.0
    ## [10] S4Vectors_0.30.0     BiocGenerics_0.38.0  BiocStyle_2.20.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] MatrixGenerics_1.4.0        httr_1.4.2
    ##  [3] sass_0.4.0                  bit64_4.0.5
    ##  [5] jsonlite_1.7.2              bslib_0.2.5.1
    ##  [7] assertthat_0.2.1            BiocManager_1.30.15
    ##  [9] BiocFileCache_2.0.0         blob_1.2.1
    ## [11] GenomeInfoDbData_1.2.6      Rsamtools_2.8.0
    ## [13] yaml_2.2.1                  progress_1.2.2
    ## [15] lattice_0.20-44             pillar_1.6.1
    ## [17] RSQLite_2.2.7               glue_1.4.2
    ## [19] digest_0.6.27               Structstrings_1.8.0
    ## [21] colorspace_2.0-1            tRNA_1.10.0
    ## [23] Matrix_1.3-3                htmltools_0.5.1.1
    ## [25] XML_3.99-0.6                pkgconfig_2.0.3
    ## [27] biomaRt_2.48.0              bookdown_0.22
    ## [29] zlibbioc_1.38.0             purrr_0.3.4
    ## [31] scales_1.1.1                BiocParallel_1.26.0
    ## [33] tibble_3.1.2                KEGGREST_1.32.0
    ## [35] ggplot2_3.3.3               generics_0.1.0
    ## [37] ellipsis_0.3.2              cachem_1.0.5
    ## [39] SummarizedExperiment_1.22.0 GenomicFeatures_1.44.0
    ## [41] magrittr_2.0.1              crayon_1.4.1
    ## [43] memoise_2.0.0               evaluate_0.14
    ## [45] fansi_0.4.2                 xml2_1.3.2
    ## [47] tools_4.1.0                 prettyunits_1.1.1
    ## [49] hms_1.1.0                   matrixStats_0.58.0
    ## [51] BiocIO_1.2.0                lifecycle_1.0.0
    ## [53] stringr_1.4.0               munsell_0.5.0
    ## [55] tRNAdbImport_1.10.0         DelayedArray_0.18.0
    ## [57] compiler_4.1.0              jquerylib_0.1.4
    ## [59] rlang_0.4.11                grid_4.1.0
    ## [61] RCurl_1.98-1.3              rjson_0.2.20
    ## [63] rappdirs_0.3.3              bitops_1.0-7
    ## [65] rmarkdown_2.8               gtable_0.3.0
    ## [67] restfulr_0.0.13             DBI_1.1.1
    ## [69] curl_4.3.1                  R6_2.5.0
    ## [71] GenomicAlignments_1.28.0    knitr_1.33
    ## [73] dplyr_1.0.6                 rtracklayer_1.52.0
    ## [75] fastmap_1.1.0               bit_4.0.4
    ## [77] utf8_1.2.1                  filelock_1.0.2
    ## [79] stringi_1.6.2               Rcpp_1.0.6
    ## [81] vctrs_0.3.8                 png_0.1-7
    ## [83] dbplyr_2.1.1                tidyselect_1.1.1
    ## [85] xfun_0.23

# 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.