Workflow Templates
Workflow templates
The intended way of running systemPipeR workflows is via *.Rmd files, which
can be executed either line-wise in interactive mode or with a single command from
R or the command-line. This way comprehensive and reproducible analysis reports
can be generated in PDF or HTML format in a fully automated manner by making use
of the highly functional reporting utilities available for R.
The following shows how to execute a workflow (e.g., systemPipeRNAseq.Rmd)
from the command-line.
Rscript -e "rmarkdown::render('systemPipeRNAseq.Rmd')"
Templates for setting up custom project reports are provided as **.Rmd* files by the helper package *systemPipeRdata* and in the vignettes subdirectory of systemPipeR. The corresponding HTML of these report templates are available here: systemPipeRNAseq, systemPipeRIBOseq, systemPipeChIPseq and systemPipeVARseq. To work with *.Rnw* or *.Rmd* files efficiently, basic knowledge of Sweave or knitr and Latex or R Markdown v2 is required.
RNA-Seq sample
Load the RNA-Seq sample workflow into your current working directory.
library(systemPipeRdata)
genWorkenvir(workflow = "rnaseq")
setwd("rnaseq")
Run workflow
Next, run the chosen sample workflow systemPipeRNAseq (PDF, Rmd) by executing from the command-line make -B within the rnaseq directory. Alternatively, one can run the code from the provided *.Rmd template file from within R interactively.
The workflow includes following steps:
- Read preprocessing
- Quality filtering (trimming)
- FASTQ quality report
- Alignments:
Tophat2(or any other RNA-Seq aligner) - Alignment stats
- Read counting
- Sample-wise correlation analysis
- Analysis of differentially expressed genes (DEGs)
- GO term enrichment analysis
- Gene-wise clustering
ChIP-Seq sample
Load the ChIP-Seq sample workflow into your current working directory.
library(systemPipeRdata)
genWorkenvir(workflow = "chipseq")
setwd("chipseq")
Run workflow
Next, run the chosen sample workflow systemPipeChIPseq_single (PDF, Rmd) by executing from the command-line make -B within the chipseq directory. Alternatively, one can run the code from the provided *.Rmd template file from within R interactively.
The workflow includes the following steps:
- Read preprocessing
- Quality filtering (trimming)
- FASTQ quality report
- Alignments:
Bowtie2orrsubread - Alignment stats
- Peak calling:
MACS2,BayesPeak - Peak annotation with genomic context
- Differential binding analysis
- GO term enrichment analysis
- Motif analysis
VAR-Seq sample
VAR-Seq workflow for the single machine
Load the VAR-Seq sample workflow into your current working directory.
library(systemPipeRdata)
genWorkenvir(workflow = "varseq")
setwd("varseq")
Run workflow
Next, run the chosen sample workflow systemPipeVARseq_single (PDF, Rmd) by executing from the command-line make -B within the varseq directory. Alternatively, one can run the code from the provided *.Rmd template file from within R interactively.
The workflow includes following steps:
- Read preprocessing
- Quality filtering (trimming)
- FASTQ quality report
- Alignments:
gsnap,bwa - Variant calling:
VariantTools,GATK,BCFtools - Variant filtering:
VariantToolsandVariantAnnotation - Variant annotation:
VariantAnnotation - Combine results from many samples
- Summary statistics of samples
VAR-Seq workflow for computer cluster
The workflow template provided for this step is called systemPipeVARseq.Rmd (PDF, Rmd).
It runs the above VAR-Seq workflow in parallel on multiple compute nodes of an HPC system using Slurm as the scheduler.
Ribo-Seq sample
Load the Ribo-Seq sample workflow into your current working directory.
library(systemPipeRdata)
genWorkenvir(workflow = "riboseq")
setwd("riboseq")
Run workflow
Next, run the chosen sample workflow systemPipeRIBOseq (PDF, Rmd) by executing from the command-line make -B within the ribseq directory. Alternatively, one can run the code from the provided *.Rmd template file from within R interactively.
The workflow includes following steps:
- Read preprocessing
- Adaptor trimming and quality filtering
- FASTQ quality report
- Alignments:
Tophat2(or any other RNA-Seq aligner) - Alignment stats
- Compute read distribution across genomic features
- Adding custom features to the workflow (e.g. uORFs)
- Genomic read coverage along with transcripts
- Read counting
- Sample-wise correlation analysis
- Analysis of differentially expressed genes (DEGs)
- GO term enrichment analysis
- Gene-wise clustering
- Differential ribosome binding (translational efficiency)
Version information
Note: the most recent version of this tutorial can be found here.
sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /home/dcassol/src/R-4.0.3/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [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 utils datasets
## [8] methods base
##
## other attached packages:
## [1] magrittr_2.0.1 batchtools_0.9.15
## [3] ape_5.4-1 ggplot2_3.3.3
## [5] systemPipeR_1.25.6 ShortRead_1.48.0
## [7] GenomicAlignments_1.26.0 SummarizedExperiment_1.20.0
## [9] Biobase_2.50.0 MatrixGenerics_1.2.1
## [11] matrixStats_0.58.0 BiocParallel_1.24.1
## [13] Rsamtools_2.6.0 Biostrings_2.58.0
## [15] XVector_0.30.0 GenomicRanges_1.42.0
## [17] GenomeInfoDb_1.26.2 IRanges_2.24.1
## [19] S4Vectors_0.28.1 BiocGenerics_0.36.0
## [21] BiocStyle_2.18.1
##
## loaded via a namespace (and not attached):
## [1] colorspace_2.0-0 rjson_0.2.20 hwriter_1.3.2
## [4] ellipsis_0.3.1 bit64_4.0.5 AnnotationDbi_1.52.0
## [7] xml2_1.3.2 codetools_0.2-18 splines_4.0.3
## [10] cachem_1.0.3 knitr_1.31 jsonlite_1.7.2
## [13] annotate_1.68.0 GO.db_3.12.1 dbplyr_2.1.0
## [16] png_0.1-7 pheatmap_1.0.12 graph_1.68.0
## [19] BiocManager_1.30.10 compiler_4.0.3 httr_1.4.2
## [22] GOstats_2.56.0 backports_1.2.1 assertthat_0.2.1
## [25] Matrix_1.3-2 fastmap_1.1.0 limma_3.46.0
## [28] formatR_1.7 htmltools_0.5.1.1 prettyunits_1.1.1
## [31] tools_4.0.3 gtable_0.3.0 glue_1.4.2
## [34] GenomeInfoDbData_1.2.4 Category_2.56.0 dplyr_1.0.4
## [37] rsvg_2.1 rappdirs_0.3.3 V8_3.4.0
## [40] Rcpp_1.0.6 vctrs_0.3.6 nlme_3.1-152
## [43] blogdown_1.1.7 rtracklayer_1.50.0 xfun_0.21
## [46] stringr_1.4.0 lifecycle_1.0.0.9000 XML_3.99-0.5
## [49] edgeR_3.32.1 zlibbioc_1.36.0 scales_1.1.1
## [52] BSgenome_1.58.0 VariantAnnotation_1.36.0 hms_1.0.0
## [55] RBGL_1.66.0 RColorBrewer_1.1-2 yaml_2.2.1
## [58] curl_4.3 memoise_2.0.0 biomaRt_2.46.3
## [61] latticeExtra_0.6-29 stringi_1.5.3 RSQLite_2.2.3
## [64] genefilter_1.72.1 checkmate_2.0.0 GenomicFeatures_1.42.1
## [67] DOT_0.1 rlang_0.4.10 pkgconfig_2.0.3
## [70] bitops_1.0-6 evaluate_0.14 lattice_0.20-41
## [73] purrr_0.3.4 bit_4.0.4 tidyselect_1.1.0
## [76] GSEABase_1.52.1 AnnotationForge_1.32.0 bookdown_0.21
## [79] R6_2.5.0 generics_0.1.0 base64url_1.4
## [82] DelayedArray_0.16.1 DBI_1.1.1 withr_2.4.1
## [85] pillar_1.4.7 survival_3.2-7 RCurl_1.98-1.2
## [88] tibble_3.0.6 crayon_1.4.1 BiocFileCache_1.14.0
## [91] rmarkdown_2.6 jpeg_0.1-8.1 progress_1.2.2
## [94] locfit_1.5-9.4 grid_4.0.3 data.table_1.13.6
## [97] blob_1.2.1 Rgraphviz_2.34.0 digest_0.6.27
## [100] xtable_1.8-4 brew_1.0-6 openssl_1.4.3
## [103] munsell_0.5.0 askpass_1.1