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---
kind: pipeline
type: docker
name: default
steps:
- name: lintr
image: jupyter/datascience-notebook
commands:
- export TAR="/bin/tar"
- Rscript -e 'devtools::install_github("jimhester/lintr")'
# Lint the document, printing output.
- |
Rscript -e 'lintr::lint_dir(
path=".", pattern=rex::rex(".", one_of("Rr"), maybe("md"), end)
)'
# Return non-zero exit code (no output is printed).
- |
Rscript -e 'quit(
save="no",
status=length(
lintr::lint_dir(path=".", pattern=rex::rex(".", one_of("Rr"), maybe("md"), end))
)
)'
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# RNAseq pipeline
Snippets for the RNAseq pipeline:
https://docs.google.com/document/d/1lM1wlz950Q8H6Oi7CWW28FO2tGv5nXYtW0wp8vEUizA
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## Generate Raw Counts using FeatureCounts/HTSeqCounts
## Load package Subread (packge within which FeatureCounts is present)
+ ml Subread/1.6.4-foss-2015b FeatureCounts
## Features used in FeatureCounts :
## -s : reversely stranded
## -T : number of threads used
## -a : reference genome
## -o : output file
+ featureCounts \
+ --a "path to reference genome" \
+ --T 6 \
+ --o "path to output file" \
+ --s 2
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