Accessing RLHub Data
27 April 2023
Package
RLHub 1.6.0
1 Introduction
RLHub (“R-Loop Hub”) provides processed data sets for the RLSuite toolchain. It is an ExperimentHub package containing annotations of R-Loop consensus regions, genomic features directly relevant to R-loops, such as R-loop-forming sequences (RLFS), G-or-C skew regions, and other data of relevance to RLSuite.
All data were generated via the protocol in the RLBase-data repository.
2 Installation
RLHub can be installed from Bioconductor via the following command:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("RLHub")RLHub may also be installed from GitHub:
remotes::install_github("Bishop-Laboratory/RLHub")3 Accessing RLHub Data
library(RLHub)## Warning: replacing previous import 'utils::findMatches' by
## 'S4Vectors::findMatches' when loading 'AnnotationDbi'Data can be conveniently accessed through ExperimentHub functions or with the built-in accessors available through RLHub.
A summary of the data can also be found by running the following:
?`RLHub-package`The full manifest of the available data is found here:
DT::datatable(
read.csv(system.file("extdata", "metadata.csv", package = "RLHub")),
options = list(
scrollX=TRUE,
pageLength = 5
)
)The Tags column list the function names used to access each data set. This method of access is detailed below.
3.1 Built-in functions
In the below example, we show how one can access data using convenient built-in functions.
# Access the R-loop binding proteins (RLBPs) data set
rlbps <- RLHub::rlbps()
DT::datatable(rlbps)The data access function name is simply the value in Tags corresponding to the entry for that data set in the metadata.csv table. In this example,“rlbps” is the tag corresponding to entry #5: “R-loop-binding proteins discovered from mass-spec studies.” Therefore, the function to access this data is simply RLHub::rlbps().
For examples of all accessors, please run the following:
?`RLHub-package`3.2 ExperimentHub objects
library(ExperimentHub)In this example, we show how to access RLHub data using the ExperimentHub object.
eh <- ExperimentHub()
rlhub <- query(eh, "RLHub")
rlhub## ExperimentHub with 16 records
## # snapshotDate(): 2023-04-24
## # $dataprovider: Multiple
## # $species: Homo sapiens, Mus musculus
## # $rdataclass: tbl, list, SummarizedExperiment, preProcess, caretStack
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH6793"]]'
##
## title
## EH6793 | Primary Genomic Annotations (hg38)
## EH6794 | Primary Genomic Annotations (mm10)
## EH6795 | Full Genomic Annotations (hg38)
## EH6796 | Full Genomic Annotations (mm10)
## EH6797 | R-loop Binding Proteins
## ... ...
## EH6804 | RLFS-Test Results
## EH6805 | RLRegion Annotations
## EH6806 | RLRegion Metadata
## EH6807 | RLRegion Read Counts
## EH6808 | RLBase Sample ManifestIf we want to obtain the R-loop-binding proteins, for example, we can do so with corresponding ExperimentHub ID.
rlbps <- rlhub[["EH6797"]]
DT::datatable(rlbps)Finally, all package resources may be loaded as a list using loadResources().
rlhublst <- loadResources(rlhub, package = "RLHub")
names(rlhublst) <- listResources(rlhub, package = "RLHub")4 Session info
sessionInfo()## R version 4.3.0 RC (2023-04-13 r84269)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.17-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] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ExperimentHub_2.8.0 AnnotationHub_3.8.0 BiocFileCache_2.8.0
## [4] dbplyr_2.3.2 BiocGenerics_0.46.0 RLHub_1.6.0
## [7] BiocStyle_2.28.0
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.40.0 xfun_0.39
## [3] bslib_0.4.2 htmlwidgets_1.6.2
## [5] Biobase_2.60.0 crosstalk_1.2.0
## [7] bitops_1.0-7 vctrs_0.6.2
## [9] tools_4.3.0 generics_0.1.3
## [11] stats4_4.3.0 curl_5.0.0
## [13] tibble_3.2.1 fansi_1.0.4
## [15] AnnotationDbi_1.62.0 RSQLite_2.3.1
## [17] blob_1.2.4 pkgconfig_2.0.3
## [19] S4Vectors_0.38.0 GenomeInfoDbData_1.2.10
## [21] lifecycle_1.0.3 compiler_4.3.0
## [23] Biostrings_2.68.0 GenomeInfoDb_1.36.0
## [25] httpuv_1.6.9 htmltools_0.5.5
## [27] sass_0.4.5 RCurl_1.98-1.12
## [29] yaml_2.3.7 interactiveDisplayBase_1.38.0
## [31] pillar_1.9.0 later_1.3.0
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## [35] ellipsis_0.3.2 DT_0.27
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## [39] tidyselect_1.2.0 digest_0.6.31
## [41] purrr_1.0.1 dplyr_1.1.2
## [43] bookdown_0.33 BiocVersion_3.17.1
## [45] fastmap_1.1.1 cli_3.6.1
## [47] magrittr_2.0.3 utf8_1.2.3
## [49] withr_2.5.0 filelock_1.0.2
## [51] promises_1.2.0.1 rappdirs_0.3.3
## [53] bit64_4.0.5 rmarkdown_2.21
## [55] XVector_0.40.0 httr_1.4.5
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## [59] memoise_2.0.1 shiny_1.7.4
## [61] evaluate_0.20 knitr_1.42
## [63] IRanges_2.34.0 rlang_1.1.0
## [65] Rcpp_1.0.10 xtable_1.8-4
## [67] glue_1.6.2 DBI_1.1.3
## [69] BiocManager_1.30.20 jsonlite_1.8.4
## [71] R6_2.5.1 zlibbioc_1.46.0