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author | Ricardo Wurmus <rekado@elephly.net> | 2019-08-15 17:39:11 +0200 |
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committer | Ricardo Wurmus <rekado@elephly.net> | 2019-08-15 17:44:25 +0200 |
commit | bb88417fb70f8881d46e2f2e8235a1aa16591a36 (patch) | |
tree | aecf52e8076a5a932fe99ba64ed79c0c75cef225 /gnu/packages | |
parent | 19f1aac0e7405b99c96823dda36e5389f18f64ec (diff) | |
download | patches-bb88417fb70f8881d46e2f2e8235a1aa16591a36.tar patches-bb88417fb70f8881d46e2f2e8235a1aa16591a36.tar.gz |
gnu: Add r-rcistarget.
* gnu/packages/bioconductor.scm (r-rcistarget): New variable.
Diffstat (limited to 'gnu/packages')
-rw-r--r-- | gnu/packages/bioconductor.scm | 35 |
1 files changed, 35 insertions, 0 deletions
diff --git a/gnu/packages/bioconductor.scm b/gnu/packages/bioconductor.scm index 74620a2cbe..0a0aee7309 100644 --- a/gnu/packages/bioconductor.scm +++ b/gnu/packages/bioconductor.scm @@ -5089,6 +5089,41 @@ by a sparse number of variables, this method can reduce the complexity of data, to only emphasize the data that actually matters.") (license license:expat))) +(define-public r-rcistarget + (package + (name "r-rcistarget") + (version "1.4.0") + (source + (origin + (method url-fetch) + (uri (bioconductor-uri "RcisTarget" version)) + (sha256 + (base32 + "133x2vr86ifbk82q08x1c8q19zsk5za7b6qrzz77dhsyf4bhcvpd")))) + (properties `((upstream-name . "RcisTarget"))) + (build-system r-build-system) + (propagated-inputs + `(("r-aucell" ,r-aucell) + ("r-biocgenerics" ,r-biocgenerics) + ("r-data-table" ,r-data-table) + ("r-feather" ,r-feather) + ("r-gseabase" ,r-gseabase) + ("r-r-utils" ,r-r-utils) + ("r-summarizedexperiment" ,r-summarizedexperiment))) + (home-page "https://aertslab.org/#scenic") + (synopsis "Identify transcription factor binding motifs enriched on a gene list") + (description + "RcisTarget identifies @dfn{transcription factor binding motifs} (TFBS) +over-represented on a gene list. In a first step, RcisTarget selects DNA +motifs that are significantly over-represented in the surroundings of the +@dfn{transcription start site} (TSS) of the genes in the gene-set. This is +achieved by using a database that contains genome-wide cross-species rankings +for each motif. The motifs that are then annotated to TFs and those that have +a high @dfn{Normalized Enrichment Score} (NES) are retained. Finally, for +each motif and gene-set, RcisTarget predicts the candidate target genes (i.e. +genes in the gene-set that are ranked above the leading edge).") + (license license:gpl3))) + (define-public r-cicero (package (name "r-cicero") |