# EpiTxDb: creating an EpiTxDb object

Felix G.M. Ernst

#### 2020-10-27

#### Package

EpiTxDb 1.2.0

# 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: 2020-10-27 20:01:47 -0400 (Tue, 27 Oct 2020)
    ## # EpiTxDb version at creation time: 1.2.0
    ## # RSQLite version at creation time: 2.2.1
    ## # 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: 2020-10-27 20:02:31 -0400 (Tue, 27 Oct 2020)
    ## # EpiTxDb version at creation time: 1.2.0
    ## # RSQLite version at creation time: 2.2.1
    ## # 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 4.0.3 (2020-10-10)
    ## Platform: x86_64-pc-linux-gnu (64-bit)
    ## Running under: Ubuntu 18.04.5 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
    ## LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## attached base packages:
    ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets
    ## [8] methods   base
    ##
    ## other attached packages:
    ##  [1] EpiTxDb_1.2.0        Modstrings_1.6.0     Biostrings_2.58.0
    ##  [4] XVector_0.30.0       AnnotationDbi_1.52.0 Biobase_2.50.0
    ##  [7] GenomicRanges_1.42.0 GenomeInfoDb_1.26.0  IRanges_2.24.0
    ## [10] S4Vectors_0.28.0     BiocGenerics_0.36.0  BiocStyle_2.18.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] MatrixGenerics_1.2.0        httr_1.4.2
    ##  [3] jsonlite_1.7.1              bit64_4.0.5
    ##  [5] assertthat_0.2.1            askpass_1.1
    ##  [7] BiocManager_1.30.10         BiocFileCache_1.14.0
    ##  [9] blob_1.2.1                  GenomeInfoDbData_1.2.4
    ## [11] Rsamtools_2.6.0             yaml_2.2.1
    ## [13] progress_1.2.2              pillar_1.4.6
    ## [15] RSQLite_2.2.1               lattice_0.20-41
    ## [17] glue_1.4.2                  digest_0.6.27
    ## [19] Structstrings_1.6.0         colorspace_1.4-1
    ## [21] htmltools_0.5.0             tRNA_1.8.0
    ## [23] Matrix_1.2-18               XML_3.99-0.5
    ## [25] pkgconfig_2.0.3             biomaRt_2.46.0
    ## [27] bookdown_0.21               zlibbioc_1.36.0
    ## [29] purrr_0.3.4                 scales_1.1.1
    ## [31] BiocParallel_1.24.0         tibble_3.0.4
    ## [33] openssl_1.4.3               generics_0.0.2
    ## [35] ggplot2_3.3.2               ellipsis_0.3.1
    ## [37] SummarizedExperiment_1.20.0 GenomicFeatures_1.42.0
    ## [39] magrittr_1.5                crayon_1.3.4
    ## [41] memoise_1.1.0               evaluate_0.14
    ## [43] xml2_1.3.2                  tools_4.0.3
    ## [45] prettyunits_1.1.1           hms_0.5.3
    ## [47] lifecycle_0.2.0             matrixStats_0.57.0
    ## [49] stringr_1.4.0               munsell_0.5.0
    ## [51] tRNAdbImport_1.8.0          DelayedArray_0.16.0
    ## [53] compiler_4.0.3              rlang_0.4.8
    ## [55] grid_4.0.3                  RCurl_1.98-1.2
    ## [57] rappdirs_0.3.1              bitops_1.0-6
    ## [59] rmarkdown_2.5               gtable_0.3.0
    ## [61] DBI_1.1.0                   curl_4.3
    ## [63] R6_2.4.1                    GenomicAlignments_1.26.0
    ## [65] knitr_1.30                  dplyr_1.0.2
    ## [67] rtracklayer_1.50.0          bit_4.0.4
    ## [69] stringi_1.5.3               Rcpp_1.0.5
    ## [71] vctrs_0.3.4                 dbplyr_1.4.4
    ## [73] tidyselect_1.1.0            xfun_0.18

# 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.