Reviewer has chosen not to be AnonymousOverall Impression:
UndecidedTechnical Quality of the paper:
Limited noveltyData availability:
All used and produced data (if any) are FAIR and openly available in established data repositoriesLength of the manuscript:
The length of this manuscript is about right
Summary of paper in a few sentences:
Tim Hulsen provides an updated, straightforward R and Python package to support drawing of area-proportional Venn diagrams of up to 3 overlapping lists. Biological lists can be mapped from Entrez/Affy to Enembl before calculating overlaps. The functions are parameterized so layout elements such as labels, font, colour, etc. can be adjusted. Additionally, the functions return the overlapping list permutations.
Reasons to accept:
When working directly within R or Python, BioVenn appears to provide a quick and easy option for creating presentation and manuscript-worthy images. This is especially true within the specific use case of working with and between supported EntrezGene, Affymetrix, and Ensembl IDs. In those use cases, it is easier than using a web-based option, and the area-proportional output can be helpful for rapid visual insight.
Reasons to reject:
The novelty of this tool is the biggest drawback. It appears to be an API change to an existing tool more than a truly novel approach. The "Bio" part is of limited use (or not properly explained), as a user with R, for example, could pre-process ID translations if desired, and a biological use-case is not strongly described in this paper. For general use, it is unclear if there are substantial differences between this and 'eulerr' for visual output in R. There are a few formatting parameters that appear to be supported in this tool (BioVenn), and of course it works natively in R and Python, but eulerr has a similar interface and allows for >3 sets. While the cross-platform support in R and Python may be helpful for some, a natural R improvement would be a tidy data/ggplot API that supported additional overlapping sets, as well as an easier layered graphical interface such that layout elements can easily be 'themed' instead of a lengthy and specific argument list. Additionally, while the area-proportional plots seem intuitive and visually attractive, the paper could benefit for an insight-driven/workflow use case that showed the utility of area-proportionality; for example, real-world cases where equal-sized circles could be misleading, or where proportionality instead improved cognition.
In summary, this is an easy and potentially useful tool for specific use cases, with limited additional support compared to alternatives (although the alternatives are nicely shown). A scientific paper, as opposed to a Vignette/application note, would benefit more strongly from a specific use-case example or literature examples showing, or at least suggesting, the insight gained by presenting proportional Venn diagrams, which would be more compelling material for a paper than a description of all of the functional arguments.