# EpiTxDb: creating an EpiTxDb object

Felix G.M. Ernst

#### 2023-10-24

#### Package

EpiTxDb 1.14.0

# 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: 2023-10-24 17:08:39 -0400 (Tue, 24 Oct 2023)
    ## # EpiTxDb version at creation time: 1.14.0
    ## # RSQLite version at creation time: 2.3.1
    ## # 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: 2023-10-24 17:09:15 -0400 (Tue, 24 Oct 2023)
    ## # EpiTxDb version at creation time: 1.14.0
    ## # RSQLite version at creation time: 2.3.1
    ## # 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 4.3.1 (2023-06-16)
    ## Platform: x86_64-pc-linux-gnu (64-bit)
    ## Running under: Ubuntu 22.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
    ## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_GB              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
    ##
    ## time zone: America/New_York
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] Structstrings_1.18.0 EpiTxDb_1.14.0       Modstrings_1.18.0
    ##  [4] Biostrings_2.70.0    XVector_0.42.0       AnnotationDbi_1.64.0
    ##  [7] Biobase_2.62.0       GenomicRanges_1.54.0 GenomeInfoDb_1.38.0
    ## [10] IRanges_2.36.0       S4Vectors_0.40.0     BiocGenerics_0.48.0
    ## [13] BiocStyle_2.30.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] tRNAdbImport_1.20.0         tidyselect_1.2.0
    ##  [3] dplyr_1.1.3                 blob_1.2.4
    ##  [5] filelock_1.0.2              bitops_1.0-7
    ##  [7] fastmap_1.1.1               RCurl_1.98-1.12
    ##  [9] BiocFileCache_2.10.0        GenomicAlignments_1.38.0
    ## [11] XML_3.99-0.14               digest_0.6.33
    ## [13] lifecycle_1.0.3             KEGGREST_1.42.0
    ## [15] RSQLite_2.3.1               magrittr_2.0.3
    ## [17] compiler_4.3.1              rlang_1.1.1
    ## [19] sass_0.4.7                  progress_1.2.2
    ## [21] tools_4.3.1                 utf8_1.2.4
    ## [23] yaml_2.3.7                  rtracklayer_1.62.0
    ## [25] knitr_1.44                  prettyunits_1.2.0
    ## [27] S4Arrays_1.2.0              bit_4.0.5
    ## [29] curl_5.1.0                  DelayedArray_0.28.0
    ## [31] xml2_1.3.5                  abind_1.4-5
    ## [33] BiocParallel_1.36.0         grid_4.3.1
    ## [35] fansi_1.0.5                 colorspace_2.1-0
    ## [37] ggplot2_3.4.4               scales_1.2.1
    ## [39] biomaRt_2.58.0              SummarizedExperiment_1.32.0
    ## [41] cli_3.6.1                   rmarkdown_2.25
    ## [43] crayon_1.5.2                generics_0.1.3
    ## [45] httr_1.4.7                  rjson_0.2.21
    ## [47] DBI_1.1.3                   cachem_1.0.8
    ## [49] stringr_1.5.0               zlibbioc_1.48.0
    ## [51] parallel_4.3.1              BiocManager_1.30.22
    ## [53] restfulr_0.0.15             matrixStats_1.0.0
    ## [55] vctrs_0.6.4                 Matrix_1.6-1.1
    ## [57] jsonlite_1.8.7              bookdown_0.36
    ## [59] hms_1.1.3                   bit64_4.0.5
    ## [61] GenomicFeatures_1.54.0      jquerylib_0.1.4
    ## [63] glue_1.6.2                  codetools_0.2-19
    ## [65] gtable_0.3.4                stringi_1.7.12
    ## [67] BiocIO_1.12.0               munsell_0.5.0
    ## [69] tibble_3.2.1                pillar_1.9.0
    ## [71] rappdirs_0.3.3              htmltools_0.5.6.1
    ## [73] GenomeInfoDbData_1.2.11     R6_2.5.1
    ## [75] dbplyr_2.3.4                lattice_0.22-5
    ## [77] evaluate_0.22               png_0.1-8
    ## [79] Rsamtools_2.18.0            memoise_2.0.1
    ## [81] bslib_0.5.1                 tRNA_1.20.0
    ## [83] SparseArray_1.2.0           xfun_0.40
    ## [85] MatrixGenerics_1.14.0       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.