From f06fc52fff05de9fa1c18b04e6a4063a5e632686 Mon Sep 17 00:00:00 2001 From: Gionmattia <98411936+Gionmattia@users.noreply.github.com> Date: Tue, 10 Sep 2024 14:49:27 +0100 Subject: [PATCH 1/3] Update analysing-RiboSeq.Rmd --- vignettes/analysing-RiboSeq.Rmd | 1 + 1 file changed, 1 insertion(+) diff --git a/vignettes/analysing-RiboSeq.Rmd b/vignettes/analysing-RiboSeq.Rmd index a845966..09f1086 100644 --- a/vignettes/analysing-RiboSeq.Rmd +++ b/vignettes/analysing-RiboSeq.Rmd @@ -19,6 +19,7 @@ knitr::opts_chunk$set( ## Introduction TODO write text here Gionmattia approx 4 sentences +TODO delete this line ## Installation From e16b883d893587369ab44c183d50d398e8301791 Mon Sep 17 00:00:00 2001 From: Gionmattia <98411936+Gionmattia@users.noreply.github.com> Date: Tue, 10 Sep 2024 16:04:56 +0100 Subject: [PATCH 2/3] Update analysing-RiboSeq.Rmd Improved Intro in the vignette --- vignettes/analysing-RiboSeq.Rmd | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/vignettes/analysing-RiboSeq.Rmd b/vignettes/analysing-RiboSeq.Rmd index 09f1086..05463ab 100644 --- a/vignettes/analysing-RiboSeq.Rmd +++ b/vignettes/analysing-RiboSeq.Rmd @@ -18,9 +18,12 @@ knitr::opts_chunk$set( ## Introduction -TODO write text here Gionmattia approx 4 sentences -TODO delete this line +This vignette illustratrs how to use the ReactomeGSA R Client with RNA-Seq and Ribo-Seq (or Polysome-Seq) count data, in order to perform a Gene Set Analysis (GSA) over the results of a Differential Translation Analysis (DTA). +A DTA allows to observe to which extent gene expression is driven by transcription or translation, when comparing samples between two conditions. +The result of such analysis is usually in the form of a table, where each gene is categorised into a Regulatory Mode based on its changes in RNA-Seq derived counts, Ribo-Seq derived counts, or Translational Efficiency (TE) (ie. if it's changes in expression between the two conditions are driven by transcription, translation or both). +The integration with ReactomeGSA allows to further contextualise this information on biological pathways, thus giving an overview on which gene sets are afffected by translation regulation and how. +For additional information regarding Differential Translation Analyses and TE, please refer to [Chotani S. et al. 2019] (https://pubmed.ncbi.nlm.nih.gov/31763789/) ## Installation From 4aadc65332e754f2eb903417733f17272f501305 Mon Sep 17 00:00:00 2001 From: Gionmattia <98411936+Gionmattia@users.noreply.github.com> Date: Tue, 10 Sep 2024 16:33:57 +0100 Subject: [PATCH 3/3] Update analysing-RiboSeq.Rmd Improved wording --- vignettes/analysing-RiboSeq.Rmd | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/vignettes/analysing-RiboSeq.Rmd b/vignettes/analysing-RiboSeq.Rmd index 05463ab..4a42762 100644 --- a/vignettes/analysing-RiboSeq.Rmd +++ b/vignettes/analysing-RiboSeq.Rmd @@ -18,10 +18,10 @@ knitr::opts_chunk$set( ## Introduction -This vignette illustratrs how to use the ReactomeGSA R Client with RNA-Seq and Ribo-Seq (or Polysome-Seq) count data, in order to perform a Gene Set Analysis (GSA) over the results of a Differential Translation Analysis (DTA). +This vignette illustratrs how to use the ReactomeGSA R Client with RNA-Seq and Ribo-Seq (or Polysome-Seq) count data, in order to perform a Gene Set Analysis (GSA) on top of a Differential Translation Analysis (DTA). A DTA allows to observe to which extent gene expression is driven by transcription or translation, when comparing samples between two conditions. The result of such analysis is usually in the form of a table, where each gene is categorised into a Regulatory Mode based on its changes in RNA-Seq derived counts, Ribo-Seq derived counts, or Translational Efficiency (TE) (ie. if it's changes in expression between the two conditions are driven by transcription, translation or both). -The integration with ReactomeGSA allows to further contextualise this information on biological pathways, thus giving an overview on which gene sets are afffected by translation regulation and how. +This integration with ReactomeGSA allows to first execute the DTA and then further contextualise the results on biological pathways, thus giving an overview on which gene sets are afffected by translation regulation and how. For additional information regarding Differential Translation Analyses and TE, please refer to [Chotani S. et al. 2019] (https://pubmed.ncbi.nlm.nih.gov/31763789/)