EpiTxDb 1.14.1
To create an EpiTxDb object a number of different functions are available.
The most univeral functions are makeEpiTxDb and makeEpiTxDbFromGRanges.
makeEpiTxDb uses four data.frames 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)makeEpiTxDb and makeEpiTxDbFromGRangesThe 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 ... doneetdb## EpiTxDb object:
## # Db type: EpiTxDb
## # Supporting package: EpiTxDb
## # test: Yes
## # Nb of modifications: 1
## # Db created by: EpiTxDb package from Bioconductor
## # Creation time: 2024-01-16 16:58:52 -0500 (Tue, 16 Jan 2024)
## # EpiTxDb version at creation time: 1.14.1
## # RSQLite version at creation time: 2.3.4
## # DBSCHEMAVERSION: 1.0metadata(etdb)Additional data can be provided via the metadata columns of the GRanges
object. For supported columns have a look at ?makeEpiTxDb or
?makeEpiTxDbFromGRanges.
makeEpiTxDbFromtRNAdbThe 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")
etdbFor additional information have a look at ?makeEpiTxDbFromtRNAdb. The result
returned from the tRNAdb is also available as GRanges object, if
gettRNAdbDataAsGRanges() is used.
makeEpiTxDbFromRMBaseAnalogous 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.
sessionInfo()## R version 4.3.2 Patched (2023-11-13 r85521)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
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## BLAS: /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
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## 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] EpiTxDb_1.14.1 Modstrings_1.18.0 Biostrings_2.70.1
## [4] XVector_0.42.0 AnnotationDbi_1.64.1 Biobase_2.62.0
## [7] GenomicRanges_1.54.1 GenomeInfoDb_1.38.5 IRanges_2.36.0
## [10] S4Vectors_0.40.2 BiocGenerics_0.48.1 BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] tRNAdbImport_1.20.1 tidyselect_1.2.0
## [3] dplyr_1.1.4 blob_1.2.4
## [5] filelock_1.0.3 bitops_1.0-7
## [7] fastmap_1.1.1 RCurl_1.98-1.14
## [9] BiocFileCache_2.10.1 GenomicAlignments_1.38.2
## [11] XML_3.99-0.16 digest_0.6.34
## [13] lifecycle_1.0.4 KEGGREST_1.42.0
## [15] RSQLite_2.3.4 magrittr_2.0.3
## [17] compiler_4.3.2 rlang_1.1.3
## [19] sass_0.4.8 progress_1.2.3
## [21] tools_4.3.2 utf8_1.2.4
## [23] yaml_2.3.8 rtracklayer_1.62.0
## [25] knitr_1.45 prettyunits_1.2.0
## [27] S4Arrays_1.2.0 bit_4.0.5
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## [37] ggplot2_3.4.4 scales_1.3.0
## [39] biomaRt_2.58.0 SummarizedExperiment_1.32.0
## [41] cli_3.6.2 rmarkdown_2.25
## [43] crayon_1.5.2 generics_0.1.3
## [45] httr_1.4.7 rjson_0.2.21
## [47] DBI_1.2.1 cachem_1.0.8
## [49] stringr_1.5.1 zlibbioc_1.48.0
## [51] parallel_4.3.2 BiocManager_1.30.22
## [53] restfulr_0.0.15 matrixStats_1.2.0
## [55] vctrs_0.6.5 Matrix_1.6-5
## [57] jsonlite_1.8.8 bookdown_0.37
## [59] hms_1.1.3 bit64_4.0.5
## [61] GenomicFeatures_1.54.1 jquerylib_0.1.4
## [63] glue_1.7.0 codetools_0.2-19
## [65] gtable_0.3.4 stringi_1.8.3
## [67] BiocIO_1.12.0 munsell_0.5.0
## [69] tibble_3.2.1 pillar_1.9.0
## [71] rappdirs_0.3.3 htmltools_0.5.7
## [73] GenomeInfoDbData_1.2.11 httr2_1.0.0
## [75] R6_2.5.1 dbplyr_2.4.0
## [77] lattice_0.22-5 evaluate_0.23
## [79] png_0.1-8 Rsamtools_2.18.0
## [81] memoise_2.0.1 bslib_0.6.1
## [83] Structstrings_1.18.0 tRNA_1.20.0
## [85] SparseArray_1.2.3 xfun_0.41
## [87] MatrixGenerics_1.14.0 pkgconfig_2.0.3Jühling, 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.