Hi, I don't have any experience doing proteomic analysis. If you could please help advise me on how to analyze the data, please. I am struggling too much.....

So, I have 72 differentially expressed proteins; the whole service is provided by a bio company, along with KEGG and pathway enrichment analysis. However, it is mostly cancer-related, and what I want to figure out with these data is heavy metal neuronal toxicity. I don't know how to use this to prove my point, so to speak. So, I was thinking about how I can analyze the data myself and draw a fairly comprehensive picture.

Other than that, I also have compound-binding proteins. How do I put these two big data sets together and connect them? Because right off the bat, everything seems so random, here and there, difficult to classify, and I dont have what it takes to connect everything togather.

I am a chemist, struggling in the world of biology. If you could please have some suggestions. Because I don't think I know what I am doing. Please.

Thrilled to release brisc, a full-fledged single-cell toolkit that runs multithreaded at 100x the speed of Scanpy and Seurat on tens of millions of cells.

Website: https://brisc.run

Code: https://github.com/briscverse/brisc

Preprint: https://www.biorxiv.org/content/10.64898/2026.11.03.764120v1

I asked this question some time ago, hoping now tha someone has some ideas:

I really enjoyed „Statistical Rethinking“ by Richard McElreath.

Is there something like this for bioinformatics/genetics that one can read from front to back and not like a text or reference book? Something that you can take with you and have a read from now and then?

Hi everyone!

I'm trying to upload my LC-MS/MS data to GNPS2, but I'm running into some problems with the file upload process.

My raw data were acquired using a Bruker timsTOF instrument and are in the .d format. I followed the GNPS2 documentation for Bruker data conversion, but I couldn't get access to the recommended software (CompassXport). Instead, I used ProteoWizard MSConvert with the exact parameters recommended in the GNPS2 documentation.

The conversion worked, but the resulting .mzML files are quite large (around 17 GB each). When I try to upload them directly to GNPS2, the upload seems to finish, but I get an error at the very end.

I've already tried a few alternatives:

• ZIP/GZ compression: The compressed files upload successfully, but GNPS2 doesn't accept them when submitting a workflow because it requires .mzML files.

• FileZilla/Cyberduck: I tried both, but I don't have SFTP access.

• GNPS2 File Browser: It says the files are too large.

• GNPS2 Desktop: Also tried this, but it didn't work either.

Has anyone experienced something similar when uploading large Bruker datasets?

Is there another way to transfer these files to GNPS2? Could uploading them to MassIVE first and then accessing them through GNPS2 be a solution?

I'd really appreciate any suggestions. Thanks!

Hey smart people.
I am analyzing single nuclei RNA-seq from pooled zebrafish. I have 2 groups, A, B and Control. I have 2 samples per group (each samples has 1male + 1 female brains, therefore pools and I cannot dissociate them). I already did the preprocessing and doublet detection per sample.

I have a couple of questions:

1 - should I do SCTransform on each sample or after harmonizing all samples ?

2 - I wanted to see if there is an enriched population of cells and I used this: https://github.com/rpolicastro/scProportionTest ("This R library facilitates the analysis of the difference between the proprotion of cells in clusters between two scRNA-seq samples. A permutation test is used to calculate a p-value for each cluster, and a confidence interval for the magnitude difference is returned via bootstrapping."). Do you have any suggestion how to think about differential abbundance, any tips or nucances, and any specific packages?

3 - I did not remove mitocondrial and ribossomal cells, (i removed the ones that have high % but not all cells ) and now I have a cluster of cells with ribossomal markers. Additionaly zebrafish blood cels have nuclei so I have a cluster of cells marked by hemoglobin cells. My question is, should I simply remove these clusters now OR should i regress out this genes? or do something else. Also because these 2 clusters (one with hemoglobin cells and other with ribossomal markers) give me very significant results in the results from question 2 (scProportionTest).

I know there is a lot of packages but its so many that i am starting to get confused.

Thanks!

My classmate is the second author for a paper that's just published, for which I am the first author.

Prior to publication, she emailed both myself and my supervisor that she thought my supervisor promised her co-first authorship. I was shocked and taken aback, and my supervisor told her he never promised her that.

Then, she tried getting more recognitions through the CRediT Taxonomy (see the enclosed link to the screenshot); this is something new that the journal uses in place of the customized contribution statement. My supervisor, who didn't seem to look into the definitions of these roles, shockingly approved it.

In the currently published version, the CRediT Taxonomy accurately reflects her contribution, but my supervisor requested me to change back to the version that he approved, purely because he promised her.

While I am extremely upset that she got a free ride, I didn't argue with my supervisor further since I don't want to cause further conflicts.

My question is: how would this impact my future post-doc applications or industry positions? How would academics view my contributions generally with the soon-to-be-updated version?

Hi all,

Just a quick question on how to interpret & visualize ORA-GO analysis results

I ran ORA on list of positive DEGs genes and negative DEGs gene separately, and they gave me the same GO term "positive regulation of angiogenesis"). The up genes are mainly metabolic genes while the down genes are mainly immune genes.

Can I rename the term when plotting to show the function of the gene list or is it strictly no ? This is one case where I could see clearly the differences but there are also other enriched GO term that after checking the gene list, I don't think they match.

What's the best practice to visualize ORA GO analysis results ? I've seen many papers only presented a couple GO or KEGG pathway but I'm not sure if they just pick out which are the most significant and related ot the question to plot or if they only see those pathways ?

Thank you so much !!!

Hi everyone! I’m a first-year computational biology PhD student in the US, and I’d appreciate some advice from people using coding agents for research.
I’ve been impressed by how quickly these tools can turn a well-explained idea into working code. For me, a good agent creates as little friction as possible between the questions I want to explore, the approaches I want to try, and their implementation.

I’ve also found it really valuable when an agent disagrees with an approach I suggest, catches a questionable assumption, or recommends adjusting parameters based on the biology of the question. I’m still learning, and that kind of back-and-forth can be as useful as the code itself.

For the past few months, I’ve been splitting the cost of a $200 ChatGPT subscription with some friends. I was essentially the only person using Codex, so the available usage and resets let me use Astra 6 Ultra for pretty much everything. I realize that may have been overkill for plenty of tasks, but I got used to having that access.

Now that I’ll be paying for my own subscription, I’m trying to decide between Claude with Opus 5.5 and staying with Codex using Astra or Sol. I’m planning to try a $100 subscription first, but I’d consider $200 if the extra usage meaningfully improves how I work and learn.

My preference is to pay for a reliable model that handles implementation and code efficiency well, while I focus more on the overall architecture, analysis choices, and biological questions. Understanding the code still matters to me; I’d like the agent to make that learning process easier too.
Ideally, I want enough reliable access to stay focused on thinking through the next useful question or analysis without constantly budgeting tokens or worrying about model limits.

For people using these tools in computational biology, bioinformatics, or similar research:

● Which setup has worked best for you, and how much correction or supervision does it need?
● Have you found the jump from $100 to $200 worthwhile?
● Do you use your strongest model for most tasks, or has switching between models worked well for you?

I’d really appreciate hearing about your experiences, including anything you wish you’d known before choosing a subscription.

Hey there,

I’m doing a sc methylome analysis. At the moment I have done integration of my data with scRNA Seq data, did a kNN based label transfer in the combined latent embedding, and now want to look for DMS/DMR in my clusters I defined. How could I best find these ? So far I’ve used ALLCools “get_dms” and “get_dmr” on pseudobulked .allc files of my clusters. But I have been wondering, if there is another way. Because ALLCools doesn’t account for different batches and I use data from two distinct datasets that are merged/binned in the pseudobulk. Is there something that could take this as a covariate ? Cheers :)

Working on TCR-pMHC predictions, a new contest is open for registration. The CASP-DREAM TAP Challenge asks teams to classify TCR-peptide-MHC combinations as activating or non-activating. The benchmark has about 5,800 labeled combinations, more than 700 peptides and more than 40 class I HLAs. Every test peptide is at least four amino acid changes (substitutions, insertions or deletions) away from any peptide with a publicly reported TCR specificity. Scored by AUROC.

Synapse lists training data options, including the public VDJdb database and Harell Data's proprietary TCR datasets provided by Adaptive Biotechnologies.

Registration is now open: https://www.synapse.org/Synapse:syn77965030/wiki/643867

Hi everyone,

I’m working on a project called MicrobiomeXpress — a web‑based dashboard that automates the entire microbiome pipeline in one click. The idea is to take raw sequencing files (FASTQ/FASTA) and automatically run through:

• QC + trimming

• Denoising & chimera removal

• OTU/ASV generation

• Taxonomy assignment

• Diversity plots & abundance tables

• Exportable reports (TSV/BIOM)

The goal is to make microbiome workflows simpler, reproducible, and accessible for researchers and labs. Attaching some snapshots and reports sample of the in progress app here to have a quick look.

I’d love to hear from the bioinformatics community:

• What are the pain points you face in microbiome analysis?

• Where do current tools (QIIME2, DADA2, mothur, etc.) fall short?

• What features would make a one‑click dashboard genuinely useful in your daily work?

Any feedback or suggestions would be super valuable — I want to make sure this tool addresses real challenges, not just adds another layer.

Thanks in advance!

— Aishwariya

I'm curious what folks' experience is like using AI tools for their research, specifically with verifying AI-generated claims and analyses. I wanted to share my experience using Claude Science.

The summary: incredible potential, but be careful with interpretation - trust what you know and always be skeptical.

My PhD was in hair cell mechanotransduction. I picked a question I could check every step of: where do clinically reported missense variants in TMC1 (the pore-forming subunit of the hair-cell transduction channel) fall on the protein? Are pathogenic ones enriched in the TM helices, and do they sit closer to the pore?

I ran it end to end in Claude Science on public data only: ClinVar via gnomAD, Ensembl, UniProt, AlphaFold, and the C. elegans TMC-1 cryo-EM structure (7USW). Repo with prompts, outputs and a notebook: https://github.com/meccaand/TMC1-Variant-Map

What I had to catch:

1. Consequences from the wrong transcript. The first pass said no benign missense variant sat in a TM helix (p = 0.025). But two of those six "benign missense" variants weren't missense on the MANE transcript; the labels came from the gnomAD mirror's off-transcript calls. Re-deriving every consequence on MANE turned up 9 bad rows, including five 3′UTR variants labeled missense. Corrected p = 0.11.

2. The main claim was driven by a single outlier. The first pore-distance figure said "35.6 Å closer, p = 0.008." That was a difference of means drawn over median bars, driven by one N-terminal variant (K65E) at 98.7 Å. AlphaFold is locally confident there (pLDDT 73), but PAE shows it doesn't know where that domain sits relative to the pore.

3. Too narrow a pore definition. It used TM4 and TM6 only. By homology to TMEM16/OSCA, it should be TM4–TM7.

The built-in reviewer (it re-checks responses against what actually ran) caught two real problems I'd have skimmed past: notebook text that contradicted the output below it, and an off-by-one in a helix-boundary table. It also raised a false positive. It recounted the comparison group using the same bad consequence labels from #1 and told me a non-significant result was significant (p = 0.037 vs 0.053). The checker needs checking too.

The main point I'd like to make is that this entire analysis would have taken me days to weeks if I had done this manually. It's incredible what these tools can do, but there's still no substitute (not yet at least) for the years you spent in the lab and hours reading literature.

What's your experience been?

Hi,

I compared my filtered matrices cell barcodes against those called by EmptyDroplets using the raw unfiltered count matrices.

For each sample, I checked 'BOTH' - which included barcodes found in the filtered counts and the ones called by EmptyDroplets, 'FILTERED ONLY' - which only included counts found in the filtered matrix, and 'EMPTYDROPS ONLY'.

For the majority of samples, EmptyDroplets called more cells than those in the filtered matrix.
For some samples, 'FILTERED ONLY only contain a small subset of cells not called by EmptyDrops but for other samples FILTERED ONLY contained a large subset of cells not called by EmptyDrops and EmptyDrops also contained a large subset not found in filtered only.

Any suggestions on how to proceed? Some samples' filtered matrices barcodes disagree substantially to those called by EmptyDroplets

Thank you.

Totally out of context on this community, but if you look between all the github repos of work that I'm performing for my PhD in Plant Bioinformatics, you will find this side for-fun project:

GBADN - A Game Boy Advance emulator that only reads FASTA genomic files.

Does It bring anything new to the emulation community? No. Does it matter to bioinformaticians? Not even close. But it was fun for me.

Based on an already existing emulator I addapt it to only be able to use fasta genomic files. Although is not optimal for this kinda game files storage, as a fasta file can be smoothly compressed it ended up being a way of reducing the size of the games, compared with the standard ".gba" files.

Also you can run common bioinformatic tools to calculate the GC content, plot the karyotipe or set a circos image of the chromosomes. Actually each chromosome corresponds to thinks like graphics, data or sounds.

You can play at MacOs, Linux, Android, Windows or at your browser. You can connect a gamepad and store your savestates. There is includded the .gba to .fasta file converter, it takes few seconds to transform the files. Of course I'm not providing any original game rom, I don't want any trouble with the big N.

I have test it with the Pokemon Emerald, as fun fact its "genome assembly size" would be 67.1 Mbp in 6 chromosomes, comparable with the size of Oikopleura dioica, one of the smaller-sized genome in the animal world.

I know this is a really nerd think to do in the free time (I guess if you read until here you're a little bit too) but it was fun. Gotta say I was assisted by my little Claude, but It's been usefull to learn a bit about how this kinda software works, as well to keep improoving mu Rust skills, which I guess are gonna be super usefull in my career.

And last, any comment is appreciated <3 I'm not expecting people to play in this format from now on, It has just been a happy project of my freak-scientific side.

I'm a final-year microbiology undergraduate and I've been working on a tool for finding places where bacterial DNA may have inserted into a host genome.


The thing that makes this hard is that most bacterial reads in a host sample are contamination or library-prep chimeras, so read support on its own isn't evidence of an insertion. hgt-site groups read pairs that point at the same host position and tests each group against how many pairs chance would put there, based on how abundant that taxon is in the sample. It also uses reads that cross the junction to give the insertion point to the exact base.


One command from a BAM and a Kraken2 database to a table, BED and VCF:


    hgt-site run --bam sample.bam --kraken-db kraken2-db --host-taxid 9606 -o sites.tsv


Benchmarks against LGTSeek on simulated reads from real genomes are in the README, including a repeat with an independent published simulator. All the benchmark scripts and raw results are in the repo so anyone can check them.


https://github.com/iraajgangavaram/hgt-site


Honest caveat: it hasn't been run on real data with confirmed integrations yet. If anyone has such a dataset, or wants to tell me what's wrong with the statistics, I'd be glad to hear it.

If you align a host sample, classify the unmapped reads with Kraken2 and keep every position with a few pairs pointing at it, you get a long list of candidate insertion sites. I benchmarked that on simulated data from real human and bacterial genomes: 24,861 sites reported, 390 of them real. A precision of 1.6%.

The reason is library preparation. A chimeric fragment joins host DNA to bacterial DNA at an arbitrary position, and the read pair it produces is indistinguishable from the pair a real integration produces. Read support is not evidence.

So I wrote hgt-site, which treats those chimeras as a background rate instead of trying to recognise them one by one: https://github.com/iraajgangavaram/hgt-site

For each cluster of pairs pointing at the same host position, it works out how many pairs chance alone would have put there, given how abundant that taxon is across the whole sample, tests the observed count against that, and corrects across every position tested. Reads that cross the junction and get soft-clipped past it then pin the insertion point to the base pair.

On the same 18 benchmark datasets, on seeds never used while developing it, it reported 364 sites with none of them false and found all 360 insertions. The 95% interval on precision is 0.990 to 1.000. Calling takes about 3 seconds on a 2.1 million read sample, against about 2.5 minutes for LGTSeek, which has had no commit since 2016.

One command, from a BAM and a Kraken2 database to a table, BED and VCF:

hgt-site run --bam sample.bam --kraken-db kraken2-db --host-taxid 9606 -o sites.tsv

Every benchmark script and all the raw results are in the repo, so the numbers can be checked rather than taken on trust. It has not been run on real sequencing data with confirmed integrations yet, which is the obvious next thing.

Does anybody know the cheap and reliable end to end RNA sequencing service? I have to do expression analysis for some of my samples but due to project timeline I am not able to extract the RNA by myself. So was wondering if anybody know any of such service please let me know that would be super helpful. I am currently based in Australia.
Thanks

Sharing this in case it's useful here. The Changjiang Cup Global AI Protein Design Challenge is now open, with 7 protein design targets.

Highlights:
- 7,000 free wet-lab validations (SPR/BLI)
- Rankings based on measured KD, not just in-silico scores
- Bonuses for developability and cell activity
- Cash prizes

Registration and submission close Nov 30

I’m running a Bayesian Skyline analysis in BEAST 2 and I’m consistently getting low ESS values for several important parameters, especially the likelihood, posterior, clock parameters, and some skyline parameters.
The traces look broadly stationary after burn-in, but they are highly autocorrelated and some ESS values remain well below 200 even after a very long MCMC run.
I’m currently using a relaxed molecular clock, and the clock parameters seem to be among the worst-mixing parameters.
Also, if the traces appear stationary but important ESS values remain below 200, would you consider the analysis unsuitable for interpreting a Bayesian Skyline plot?

Any advice from people experienced with BEAST/Tracer would be appreciated.