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@@ -7,6 +7,18 @@ steps:
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- name: lintr
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image: jupyter/datascience-notebook
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commands:
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- export TAR="/bin/tar"
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- Rscript -e 'devtools::install_github("jimhester/lintr")'
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- Rscript -e 'lintr::lint_package()'
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# Lint the document, printing output.
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- |
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Rscript -e 'lintr::lint_dir(
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path=".", pattern=rex::rex(".", one_of("Rr"), maybe("md"), end)
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)'
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# Return non-zero exit code (no output is printed).
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- |
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Rscript -e 'quit(
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save="no",
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status=length(
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lintr::lint_dir(path=".", pattern=rex::rex(".", one_of("Rr"), maybe("md"), end))
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)
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)'
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@@ -0,0 +1,3 @@
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# RNAseq pipeline
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Snippets for the RNAseq pipeline:
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https://docs.google.com/document/d/1lM1wlz950Q8H6Oi7CWW28FO2tGv5nXYtW0wp8vEUizA
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@@ -0,0 +1,18 @@
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## Generate Raw Counts using FeatureCounts/HTSeqCounts
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## Load package Subread (packge within which FeatureCounts is present)
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+ ml Subread/1.6.4-foss-2015b FeatureCounts
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## Features used in FeatureCounts :
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## -s : reversely stranded
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## -T : number of threads used
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## -a : reference genome
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## -o : output file
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+ featureCounts \
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+ --a "path to reference genome" \
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+ --T 6 \
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+ --o "path to output file" \
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+ --s 2
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