[Bioc-devel] [EXTERNAL] Re: Question on prospective Bioc package

[Flanigan, Greg (NIH/NCI) [C]]

Dear Wolfgang and Sutirtha,

Excellent, thank you for confirming this! There has been a fair amount of back and forth in the development team about whether to push for Bioconductor or not. We’ll proceed with putting it together to submit to Bioconductor.

Best wishes,
Greg

From: SUTIRTHA CHAKRABORTY <sutirtha_sutir using yahoo.co.in>
Date: Wednesday, February 22, 2023 at 4:55 AM
To: Flanigan, Greg (NIH/NCI) [C] <greg.flanigan using nih.gov>, Wolfgang Huber <wolfgang.huber using embl.org>
Cc: bioc-devel using r-project.org <bioc-devel using r-project.org>
Subject: [EXTERNAL] Re: [Bioc-devel] Question on prospective Bioc package
Seems like this would be a great contribution.


Best Regards

Sutirtha

On Wednesday, 22 February 2023, 03:02:52 PM IST, Wolfgang Huber <wolfgang.huber using embl.org> wrote:


Dear Greg

This sounds like a typical use case for a Bioconductor package.
Simplicity is a not a bad thing—robust, well-engineered building blocks that do one thing really well are IMHO often more useful to many than big integrated complex jack of all trades.


Thanks and best wishes
Wolfgang





> Il giorno 17.02.2023, alle ore 18:26, Flanigan, Greg (NIH/NCI) [C] via Bioc-devel <bioc-devel using r-project.org<mailto:bioc-devel using r-project.org>> ha scritto:
>
> Dear Bioconductor Developer team,
>
> I�m part of a team at the NCI that is looking at developing a Bioconductor package, but I want to be sure it is a good fit with the goals of the Bioconductor project.
>
> The core of the package is a custom data object that allows users to query predicted drug performance based on target gene, whether wild-type or mutant, or to query predicted gene target fitness by drug. The data object is precomputed based on a variety of sources, and we include methods to modify the object based on custom data provided by the user.
>
> If a more detailed dive would help, here is the preprint of the accompanying paper: Link<https://www.biorxiv.org/content/biorxiv/early/2022/10/19/2022.10.17.512424.full.pdf<https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.biorxiv.org%2Fcontent%2Fbiorxiv%2Fearly%2F2022%2F10%2F19%2F2022.10.17.512424.full.pdf&data=05%7C01%7Cgreg.flanigan%40nih.gov%7Cf538f5333b2a4735965f08db14baefee%7C14b77578977342d58507251ca2dc2b06%7C0%7C0%7C638126565468954182%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=i2AM%2B2Xxkthq0T%2BCWsOykhPJHXQjhYemhMR7Yn%2B%2B42U%3D&reserved=0>>
>
> I think this tool will offer good impact and utility for researchers. Its predictions are well-validated and it offers a novel method for examining drug performance on various cancers. However, given the simplicity of this tool, would it be a good fit for the Bioconductor project? I would appreciate your opinion. We will be developing it into a package hosted on GitHub either way. Thank you for your time!
>
> Sincerely,
> Greg
>
>
>
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