systemPipeRdata 1.12.0
Note: the most recent version of this vignette can be found here and a short overview slide show here.
Note: if you use systemPipeR and systemPipeRdata in published research, please cite:
Backman, T.W.H and Girke, T. (2016). systemPipeR: NGS Workflow and Report Generation Environment. BMC Bioinformatics, 17: 388. 10.1186/s12859-016-1241-0.
systemPipeRdata is a helper package to generate with a single command NGS workflow templates that are intended to be used by its parent package systemPipeR (H Backman and Girke 2016). The latter is an environment for building end-to-end analysis pipelines with automated report generation for next generation sequence (NGS) applications such as RNA-Seq, Ribo-Seq, ChIP-Seq, VAR-Seq and many others. The directory structure of the workflow templates and the sample data used by systemPipeRdata are described here.
The R software for using systemPipeRdata can be downloaded from CRAN. The systemPipeRdata package can be installed from within R as follows:
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
BiocManager::install("systemPipeRdata") # Installs from Bioconductor once
# available there
BiocManager::install("tgirke/systemPipeR", build_vignettes = TRUE,
dependencies = TRUE) # Installs from githublibrary("systemPipeRdata") # Loads the packagelibrary(help = "systemPipeRdata") # Lists package info
vignette("systemPipeRdata") # Opens vignetteLoad one of the available NGS workflows into your current working directory. The following does this for the varseq template. The name of the resulting workflow directory can be specified under the mydirname argument. The default NULL uses the name of the chosen workflow. An error is issued if a directory of the same name and path exists already. Besides, it is possible to choose different version of the workflow template. Please check the available options here, or provide the download URL to your template. The URL can be specified under url argument and the file name in the urlname argument. The default NULL copies the current version available in the systemPipeRdata.
genWorkenvir(workflow = "varseq", mydirname = NULL, url = NULL,
urlname = NULL)
setwd("varseq")On Linux and OS X systems the same can be achieved from the command-line of a terminal with the following commands.
$ Rscript -e "systemPipeRdata::genWorkenvir(workflow='varseq', mydirname=NULL, url=NULL, urlname=NULL)"The workflow templates generated by genWorkenvir contain the following preconfigured directory structure:
Next, run from within R the chosen sample workflow by executing the code provided in the corresponding *.Rmd template file. If preferred the corresponding *.Rnw or *.R versions can be used instead. Alternatively, one can run an entire workflow from start to finish with a single command by executing from the command-line 'make -B' within the workflow directory (here 'varseq'). Much more detailed information on running and customizing systemPipeR workflows is available in its overview vignette here. This vignette can also be opened from R with the following command.
library("systemPipeR") # Loads systemPipeR which needs to be installed via BiocManager::install() from Bioconductorvignette("systemPipeR", package = "systemPipeR")The location of the sample data provided by systemPipeRdata can be returned as a list.
pathList()## $targets
## [1] "/tmp/RtmpbW4kAN/Rinst7c9f8a8fd1/systemPipeRdata/extdata/param/targets.txt"
##
## $targetsPE
## [1] "/tmp/RtmpbW4kAN/Rinst7c9f8a8fd1/systemPipeRdata/extdata/param/targetsPE.txt"
##
## $annotationdir
## [1] "/tmp/RtmpbW4kAN/Rinst7c9f8a8fd1/systemPipeRdata/extdata/annotation/"
##
## $fastqdir
## [1] "/tmp/RtmpbW4kAN/Rinst7c9f8a8fd1/systemPipeRdata/extdata/fastq/"
##
## $bamdir
## [1] "/tmp/RtmpbW4kAN/Rinst7c9f8a8fd1/systemPipeRdata/extdata/bam/"
##
## $paramdir
## [1] "/tmp/RtmpbW4kAN/Rinst7c9f8a8fd1/systemPipeRdata/extdata/param/"
##
## $workflows
## [1] "/tmp/RtmpbW4kAN/Rinst7c9f8a8fd1/systemPipeRdata/extdata/workflows/"
##
## $chipseq
## [1] "/tmp/RtmpbW4kAN/Rinst7c9f8a8fd1/systemPipeRdata/extdata/workflows/chipseq/"
##
## $rnaseq
## [1] "/tmp/RtmpbW4kAN/Rinst7c9f8a8fd1/systemPipeRdata/extdata/workflows/rnaseq/"
##
## $riboseq
## [1] "/tmp/RtmpbW4kAN/Rinst7c9f8a8fd1/systemPipeRdata/extdata/workflows/riboseq/"
##
## $varseq
## [1] "/tmp/RtmpbW4kAN/Rinst7c9f8a8fd1/systemPipeRdata/extdata/workflows/varseq/"sessionInfo()## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.9-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.9-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] stats4 parallel stats graphics grDevices
## [6] utils datasets methods base
##
## other attached packages:
## [1] systemPipeRdata_1.12.0 systemPipeR_1.18.0
## [3] ShortRead_1.42.0 GenomicAlignments_1.20.0
## [5] SummarizedExperiment_1.14.0 DelayedArray_0.10.0
## [7] matrixStats_0.54.0 Biobase_2.44.0
## [9] BiocParallel_1.18.0 Rsamtools_2.0.0
## [11] Biostrings_2.52.0 XVector_0.24.0
## [13] GenomicRanges_1.36.0 GenomeInfoDb_1.20.0
## [15] IRanges_2.18.0 S4Vectors_0.22.0
## [17] BiocGenerics_0.30.0 BiocStyle_2.12.0
##
## loaded via a namespace (and not attached):
## [1] Category_2.50.0 bitops_1.0-6
## [3] bit64_0.9-7 RColorBrewer_1.1-2
## [5] progress_1.2.0 httr_1.4.0
## [7] Rgraphviz_2.28.0 backports_1.1.4
## [9] tools_3.6.0 R6_2.4.0
## [11] DBI_1.0.0 lazyeval_0.2.2
## [13] colorspace_1.4-1 withr_2.1.2
## [15] tidyselect_0.2.5 prettyunits_1.0.2
## [17] bit_1.1-14 compiler_3.6.0
## [19] graph_1.62.0 formatR_1.6
## [21] rtracklayer_1.44.0 bookdown_0.9
## [23] scales_1.0.0 checkmate_1.9.3
## [25] genefilter_1.66.0 RBGL_1.60.0
## [27] rappdirs_0.3.1 stringr_1.4.0
## [29] digest_0.6.18 rmarkdown_1.12
## [31] AnnotationForge_1.26.0 pkgconfig_2.0.2
## [33] htmltools_0.3.6 BSgenome_1.52.0
## [35] limma_3.40.0 rlang_0.3.4
## [37] RSQLite_2.1.1 GOstats_2.50.0
## [39] hwriter_1.3.2 dplyr_0.8.0.1
## [41] VariantAnnotation_1.30.0 RCurl_1.95-4.12
## [43] magrittr_1.5 GO.db_3.8.2
## [45] GenomeInfoDbData_1.2.1 Matrix_1.2-17
## [47] Rcpp_1.0.1 munsell_0.5.0
## [49] stringi_1.4.3 yaml_2.2.0
## [51] edgeR_3.26.0 zlibbioc_1.30.0
## [53] plyr_1.8.4 grid_3.6.0
## [55] blob_1.1.1 crayon_1.3.4
## [57] lattice_0.20-38 splines_3.6.0
## [59] GenomicFeatures_1.36.0 annotate_1.62.0
## [61] hms_0.4.2 batchtools_0.9.11
## [63] locfit_1.5-9.1 knitr_1.22
## [65] pillar_1.3.1 rjson_0.2.20
## [67] base64url_1.4 codetools_0.2-16
## [69] biomaRt_2.40.0 XML_3.98-1.19
## [71] glue_1.3.1 evaluate_0.13
## [73] latticeExtra_0.6-28 data.table_1.12.2
## [75] BiocManager_1.30.4 gtable_0.3.0
## [77] purrr_0.3.2 assertthat_0.2.1
## [79] ggplot2_3.1.1 xfun_0.6
## [81] xtable_1.8-4 survival_2.44-1.1
## [83] tibble_2.1.1 pheatmap_1.0.12
## [85] AnnotationDbi_1.46.0 memoise_1.1.0
## [87] brew_1.0-6 GSEABase_1.46.0This project was supported by funds from the National Institutes of Health (NIH) and the National Science Foundation (NSF).
H Backman, Tyler W, and Thomas Girke. 2016. “systemPipeR: NGS workflow and report generation environment.” BMC Bioinformatics 17 (1):388. https://doi.org/10.1186/s12859-016-1241-0.