Journal of Open Source Software Paper

JOSS/paper.md

@@ -42,7 +42,7 @@ joss-doi: 10.21105/joss.xxxxx
 
 The analysis of microbiome data has become a major asset for investigating microbial diversity and dynamics, in diverse fields, like health [@LeChatelier2013], environmental studies [@Karimi2020], food-processing [@Chaillou2014], or environmental biotechnologies [@Poirier2016]. Due to sequencing advances, microbiome studies now require analysis and interpretation of large and high-dimensional datasets. Metabarcoding approaches, in particular, are based on a two-step process. First, a bioinformatics pipeline processes raw sequencing reads, generating counts and taxonomic affiliations for each Operational Taxonomic Unit (OTU) or Amplicon Sequence Variant (ASV). Second, these tables are enriched with sample metadata for biostatistical analyses to address relevant biological questions. The affordability of amplicon sequencing has led to its widespread use in microbial ecology. Therefore, there is a growing demand for user-friendly, well-calibrated, and interactive tools, enabling researchers to analyze their data independently, alleviating the dependence on bioinformaticians, biostatisticians or the need to acquire skills in R programming.
 
-Regarding the bioinformatic processing, many solutions are available to produce tables from reads, relying either on command-lines or on the Galaxy platform (ie: `QIIME` [@QIIME], `FROGS` [@FROGS-ITS]), or on R pipelines (ie: `DADA2` [@DADA2]). These solutions require various levels of investment from the users to master them. For the second step, several packages dedicated to the analysis and representation of microbiomes are available, such as `phyloseq` [@phyloseq], `microbiome` [@microbiome], `metacoder` [@metacoder], however their use can sometimes be complex. There are relatively few tools available for rapid, interactive analysis of microbiome data (`shiny-phyloseq` [@shiny-phyloseq] is no longer supported ; `animalcules` [@animalcules] requires local installation ; `shaman` [@shaman] is complex and requires specific skills).
+Regarding the bioinformatic processing, many solutions are available to produce tables from reads, relying either on command-lines or on the Galaxy platform (ie: `QIIME` [@QIIME], `FROGS` [@FROGS]), or on R pipelines (ie: `DADA2` [@DADA2]). For the second step, several packages dedicated to the analysis and representation of microbiomes are available, such as `phyloseq` [@phyloseq], `microbiome` [@microbiome], `metacoder` [@metacoder], however their use can sometimes be complex. There are relatively few tools available for rapid, interactive analysis of microbiome data (`shiny-phyloseq` [@shiny-phyloseq] is no longer supported ; `animalcules` [@animalcules] requires local installation ; `shaman` [@shaman] is complex and requires specific skills).
 
 # Statement of need
 
@@ -74,7 +74,7 @@ Users can export potentially preprocessed data for further analysis within R or
 
 Three major use cases have been identified for Easy16S:
 
--   Easy16S empowers beginner users to independently conduct their analyses.
+-   Easy16S empowers beginner users to independently conduct their analyses without any technical skills.
 -   More advanced users can utilize Easy16S to swiftly explore data, identify patterns, before adjusting their R code for a more in-depth analysis.
 -   During training sessions, Easy16S serves as a valuable tool, allowing users to concentrate on mastering biological concepts without being encumbered by minor programming challenges.