bioc-issue-bot
commented
1 year ago Hi @mtiberti
Thanks for submitting your package. We are taking a quick look at it and you will hear back from us soon.
The DESCRIPTION file for this package is:
Package: Moonlight2R
Type: Package
Title: Identify oncogenes and tumor suppressor genes from omics data
Version: 0.99.0
Date:
Authors@R:
c(person("Astrid", "Saksager",
role="aut"),
person("Mona", "Nourbakhsh",
role="aut"),
person("Nikola", "Tom",
role="aut"),
person("Xi Steven", "Chen",
role="aut"),
person("Antonio", "Colaprico",
role="aut"),
person("Catharina", "Olsen",
role="aut"),
person("Matteo", "Tiberti",
role=c("cre", "aut"),
email="tiberti@cancer.dk"),
person("Elena", "Papaleo",
role="aut",
))
Depends: R (>= 4.2), doParallel, foreach
Imports: parmigene, randomForest, gplots,
circlize, RColorBrewer, HiveR, clusterProfiler, DOSE, Biobase,
grDevices, graphics, GEOquery, stats, purrr,
RISmed, grid, utils, ComplexHeatmap, GenomicRanges, dplyr, fuzzyjoin,
rtracklayer, magrittr, qpdf, readr, seqminer, stringr,
tibble, tidyHeatmap, tidyr, AnnotationHub
Description: The understanding of cancer mechanism requires
the identification of genes playing a role in the development
of the pathology and the characterization of their role
(notably oncogenes and tumor suppressors). We present
an updated version of the R/bioconductor package called MoonlightR,
namely Moonlight2R, which returns a list of candidate driver genes
for specific cancer types on the basis of omics data integration.
The Moonlight framework contains a primary layer where gene expression
data and information about biological processes are integrated to
predict genes called oncogenic mediators, divided into putative tumor
suppressors and putative oncogenes. This is done through functional enrichment
analyses, gene regulatory networks and upstream regulator
analyses to score the importance of well-known biological
processes with respect to the studied cancer type. By evaluating the effect
of the oncogenic mediators on biological processes or through
random forests, the primary layer predicts two putative
roles for the oncogenic mediators: i) tumor suppressor genes
(TSGs) and ii) oncogenes (OCGs). As gene expression data alone is not
enough to explain the deregulation of the genes, a second layer of
evidence is needed. We have automated the integration of a
secondary mutational layer through new functionalities in Moonlight2R.
These functionalities analyze mutations in the cancer cohort and classifies
these into driver and passenger mutations using the driver mutation
prediction tool, CScape-somatic. Those oncogenic mediators with at
least one driver mutation are retained as the driver genes.
As a consequence, this methodology does not only identify genes playing a dual role
(e.g. TSG in one cancer type and OCG in another) but also helps
in elucidating the biological processes underlying their
specific roles. In particular, Moonlight2R can be used to
discover OCGs and TSGs in the same cancer type. This may for instance help
in answering the question whether some genes change role
between early stages (I, II) and late stages (III, IV). In the future,
this analysis could be useful to determine the causes of different
resistances to chemotherapeutic treatments.
License: GPL (>= 3)
biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation,
GeneExpression, MethylationArray, DifferentialExpression,
Pathways, Network, Survival, GeneSetEnrichment,
NetworkEnrichment
Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2,
png
SystemRequirements: CScapeSomatic
VignetteBuilder: knitr
URL: https://github.com/ELELAB/Moonlight2R
BugReports: https://github.com/ELELAB/Moonlight2R/issues
RoxygenNote: 7.2.3
LazyData: true
Encoding: UTF-8
ococrook
mtiberti
lshep
Hi, here's our submission for the
Moonlight2Rpackage. Thanks for reviewing it!Repository: https://github.com/ELELAB/Moonlight2R
[x] I understand that by submitting my package to Bioconductor, the package source and all review commentary are visible to the general public.
[x] I have read the Bioconductor [Package Submission][2] instructions. My package is consistent with the Bioconductor [Package Guidelines][1].
[x] I understand Bioconductor [Package Naming Policy][9] and acknowledge Bioconductor may retain use of package name.
[x] I understand that a minimum requirement for package acceptance is to pass R CMD check and R CMD BiocCheck with no ERROR or WARNINGS. Passing these checks does not result in automatic acceptance. The package will then undergo a formal review and recommendations for acceptance regarding other Bioconductor standards will be addressed.
[x] My package addresses statistical or bioinformatic issues related to the analysis and comprehension of high throughput genomic data.
[x] I am committed to the long-term maintenance of my package. This includes monitoring the [support site][3] for issues that users may have, subscribing to the [bioc-devel][4] mailing list to stay aware of developments in the Bioconductor community, responding promptly to requests for updates from the Core team in response to changes in R or underlying software.
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I am familiar with the essential aspects of Bioconductor software management, including: