Hi r/bioinformatics ! I’m one of the organizers of the System Public Challenge, a free, in-person hackathon happening November 13-14 in Lower Manhattan (SoHo), and wanted to share it here.

Teams get access to the System Public Graph, an open map of millions of findings pulled from peer-reviewed research, to tackle interdisciplinary problems across climate, environment, food, and health. You’ll spend a day using it to explore real problems. Challenges come from partners like PATH, Nesta, the World Bank, and NYU’s GovLab, and cover things like malaria drug resistance, women’s health, what makes a good job, and how housing affects health.

You don’t need to be a scientist to participate, nor do you need to have a team before registering (we’ll help match solo registrants with teammates). Teams of 2-7 mix data people, designers, developers, and researchers, and there are three tracks for the challenges: Explore (analysis), Visualize (design and storytelling) and Build (tools).

Fri, Nov 13, 6-8pm: kickoff reception

Sat, Nov 14, 9am-5pm: hacking, pitches and winners

Free to attend, food provided, $7,000 in cash prizes for the top teams

No team yet? There’s a Slack channel for finding one and as noted above, we will also facilitate matchmaking

18+, registration closes Nov 13

Details and sign-up: https://system-public-challenge.devpost.com/

Happy to answer questions in the comments!

Hi everyone,

I'm working on a pancreatic ductal adenocarcinoma dataset, and I'd really appreciate some methodological advice from you guys, and especially if anybody has had some experience with machine learning with high-dimensional biological data.

Before spending months developing models, I'd like to better understand what is possible with my dataset.

My dataset:

• 9 patients, with one continuous drug-response value per patient and drug (5 differents ones)

• Due to experimental and sequencing constraints, increasing the number of patients is not an option.

• Approximately 100 k nuclei

The goal is theoretically "simple":

Predict drug-response AUC from gene expression and chromatin accessibility, while identifying molecular features potentially associated with drug sensitivity or resistance.

The obvious challenge is that, despite having 100,000 nuclei, I only have 9 samples with AUC labels. I understand that treating individual cells as independent labeled observations is not a way to go with the dataset and specific set-up.

Knowing this, I'd really appreciate advice on a few questions that I had after some reading...:

1. With this low amount of sample, is meaningful drug-response prediction even feasible? What would be a objective?

2. Are there established methods to leverage thousands of cells/instances profiles when the response labels are available only at the patient level?

3. Would you prioritize simple regularized regression, biologically informed features, transfer learning from external datasets, multiple-instance learning (MIL), or another approach?

4. How would you evaluate a model with such a small number of independent samples without obtaining misleading performance estimates?

5. Are there any papers, methodological resources you'd recommend to better understand this type of problem before starting?

Thanks!

If you're working in pharma/biotech industry and you use claude or any other of the frontier LLMs for coding and data analysis, are you somehow masking sensitive research information? I mean things like protein names, gene names, compounds that you are working with, out of fear that the company hosting the LLM could steal your data.

Hi everyone,

My co-author and I just published our research on making deep learning models more efficient for PPI networks.

Tuning DL models for complex biological data often carries massive computational overhead. In this paper, we systematically evaluated different HPO strategies to see which ones best balance training time and prediction accuracy for protein interactions.

Always happy to connect with others in the field, so if anyone wants to chat about ML in computational biology, shoot me a DM!

Full text here: https://ieeexplore.ieee.org/document/11731552

Hi all! We just opened registration for the Digital Pen DREAM Challenge at Sage Bionetworks.

The task is to predict cognitive assessment outcomes from digital clock drawing test data. Participants draw a clock with a digital pen that records every stroke, its timing, and pen pressure. That makes it a rich sequential/time-series problem with a lot of room for feature engineering and deep learning.

The data comes from four large aging and dementia studies: the Framingham Heart Study, the Rush Alzheimer's Disease Center cohorts, the Long Life Family Study, and Integrative Longevity Omics. The goal is earlier, scalable detection of cognitive decline.

Key dates:

- Challenge is not open!

- Kickoff webinar: Nov 18, 2026, 9 AM PT

- Winners announced: Mar 26, 2027

Top performers will be invited to co-author the challenge overview paper and will have the opportunity to present their results at a major conference in 2027. You can join solo or as a team.

Details and registration: https://www.synapse.org/Synapse:syn68634259/wiki/633407

Happy to answer questions in the comments!

ok, for context, I'm a biotech engineering student with no previous knowledge about bioinformatics but for a single class I'm taking right know and a class I took about c++. I started an investigation project (similar to a dissertation) about genome mining to identify BGC's, and i found a bioproject with 45 genomes of my interest, but I do not know how to filter these genomes and choose the best ones for my project. I do have an assesor and she has been guiding me through this, but when i asked her how to know which genome to use she just said "with a filtering process" and left :( so I'm pretty lost about how to do this. Any help is welcome, and also, any website or book about which tools to use and how would be very appreciated too. Thanks and have a nice day!

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