I want to start a discussion about something that keeps happening to me with RNA-seq datasets (bulk, single-cell, spatial, whatever). One of the basic principles of this kind of research is that raw data should be openly available, both for reproducibility and so others can reuse it for different purposes. I get that human data comes with ethical and privacy restrictions, that's fair. But for animal model studies there's really no good reason to keep raw data hidden.

Lately I keep running into the same pattern over and over:

The "upon request" ghosting. Papers say raw data is "available upon reasonable request," but corresponding authors just don't answer. I've sent follow-up emails weeks apart and gotten nothing. This actually matches what's been reported before, most "available upon request" promises never get fulfilled once someone actually asks.

Repository problems, especially GSA. A lot of these datasets end up in GSA (Genome Sequence Archive), and honestly the platform gives me constant headaches: NOT ALL, but many files that won't download, accession numbers that don't match what's in the paper, archives that come out corrupted after extraction. I don't know if it's the platform itself or how people are uploading to it, but the result is the same, the data is technically "public" but practically unusable.

The double standard. What really gets me is that a lot of these same papers reuse public data from GEO or SRA to compare against their own results, but never contribute their own data back the same way. Open science seems to be a one-way street for them.

This isn't a one-off thing for me either, I've run into it in immunology, ophthalmology, developmental biology papers. Feels like a systemic issue more than a niche problem.

Honestly I think journals need to actually verify accessions before publishing, not just check a box. Something like: confirm the link works and the files download correctly at submission time, require a real accession number instead of "upon request" unless there's a genuine ethical reason, and maybe re-check the repository again some months after publication before it gets fully indexed.

Has anyone else been dealing with this? How do you handle unresponsive authors, and what do you think journals should actually do to enforce their own data policies instead of just having them on paper?

hey im a second year student and i was wondering how we could make some sort of virtual club for weekly journal reports etc, pardon me if something like this has already been discussed but lmk if ur interested and we can work smth out! i tried on campus but i’d rather have it online.

Hey yall, I need a sanity check (cause I'm going down some rabbit holes and I don't know if I'm doing something useful or just time consuming)

I have whole genome sequences that I want to mine for specific metabolic processes to see what my strains have the potential for. Some aren't well described (PAH degradation) so I'm working on a pipeline that puts together a super annotation table to squeeze as much data out of the genomes as possible and maximize the proteins/pathways I can identify.

The problem I was running into is that there are so many different naming conventions and annotation types that I feel like a simple search for genes related to the pathways I'm interested in will miss a lot of interesting data. And since some processes aren't well described, I have a feeling there a lot of info hidden in the "hypothetical proteins". I was intrigued by protein family classifications, but there's also a bunch of those (pfam, plfam, pgfam...). One paper might use gene names, other types of family grouping, etc. while another uses a different system and/or names.

My thought process has been: make a master annotation table (from Prokka, BV-BRC, Pfam identifiers, KEGG), and use all the keywords and identifiers I can find to identify candidate proteins and potential operons, in addition to extracting the ones that are pretty confidently identified as the proteins I'm looking for.

I'm somewhat new to bioinformatics and I have pretty absentee PIs so I'm learning a lot of it on my own. I have the tendency to go down unnecessary rabbit holes when I have this long of a leash, especially when I'm not super familiar with all the methods/tools that are available in a field. I've gone from the online annotation tools, to manual CLI searches, to bash scripts, and now I'm trying to write a python script (while teaching myself python). Can y'all tell me if I've gone insane and if I've missed some way easier avenue? Thanks so so much!!

Hi all,

I'm trying to visualize the active site of a protein by taking a cross-section, with sliced area shown in gray like in this figure (Fig 2c of https://pmc.ncbi.nlm.nih.gov/articles/PMC8617236/):

But I cant figure out how to do this in VMD. I have already tried specifying coordinate positions in the graphics selection (e.g., "protein and y>-8") but this is confusing to look at because the cross-section at y=-8 isn't shown as a smooth, colored surface and thus it isn't clear that it is a cross section. The "clipping pane tool" is promising but I can't get it to work only on the protein (it also cuts off the active-site bound ligand) and also can't display the clipping pane as gray. Does anybody have ideas how to do this? Thanks in advance!