hoping some scientist academics out there can provide insight into a question I've been ruminating on for a while.
I spent multiple years working on a large computational data set, and it was published in a journal this year. There were millions genes and many interesting patterns in this project. I am now working on another large data set, collected with different organisms, and going back to my original code to optimize and try out new methods for data exploration. In doing this, I discovered better ways of carrying out my original analysis. I wanted to understand how these modifications changed the biological outcomes in my already published study. In doing so, I discovered a pretty interesting gene that I missed the first time around, that was overall not very abundant in the scheme of things, but definitely responsive. It's a part of a module that I discussed during a section of this paper. It doesn't change major conclusions, but if I could, I would rewrite a part of the paper to take into account this missing gene. The concern is that I will lead people astray who have a very specific interest in this gene process.
My question is, how should one handle this? Do I correct the paper? If so, do I merely mention this gene I missed or re-do the entire analysis using an updated (and better) pipeline? I have not seen this done in practice, and the corrections/errata I see are due to technical and specific errors, not to add in extra information. It seems like a Pandora's box because I now know so much more than I did the first time I did the analysis, and could probably keep updating every time I find another new important gene that was previously missed or a better way of carrying out the analysis. Another option is to write a new paper expanding on this subsection.
I can't tell at what point I'm crossing the line from being a diligent scientist to obsessing over every detail. I know things like this must happen to other people, but I don't hear of them being discussed. Is anyone else concerned about how scientific methods (especially computational ones) and our own data analysis skills improve over time, and how this influences previous results? How do others deal with this in academia?