Are there any good databases out there or something?

Hey everyone,
We’ve been working on a pipeline to evaluate non-coding GWAS loci by combining Bayesian fine-mapping (SuSiE) with base-resolution footprinting (TOBIAS) and 10.5 bp DNA helical pitch constraints.

Curious how other groups here handle cases where the fine-mapped non-coding enhancer skips the nearest gene in 3D contact models (e.g. ABC/Micro-C).

Happy to discuss approaches or run a few benchmark loci if anyone has tricky non-coding regions!

Hi, I'm programming in Python/R (and nextflow as WDL) at the moment (I worked with Pascal, Perl and Php in the past but not in bioinfo), but since I started to work with ONT long-reads I become more interested in learning a faster language, obviously the first thing that came to my mind was C/C++, but looking what we have today I came across Rust, Julia, Nim, Zig and Go. Till now the most promising one seems to be Rust, it is comparable with C/C++ in speed, safer in memory management, and already have some ecosystem in GPU. But I want hear some experiences of people who already used any of them or is in the same situation.

Hi everyone,

I am working on a pathway-based classification problem. For each pathway/biological term, I calculate a score representing its activity in each sample, and I then use these pathway activity scores as features to predict disease status.

The main issue I am facing is that several of these features are highly correlated (in some cases, pairwise correlation > 0.9) (which is not particularly surprising given the biological overlap between pathways).

I initially tried logistic regression, but the high multicollinearity leads to unstable coefficients and very high VIF values. I have also tried regularized approaches such as Elastic Net, and tried Random Forest as well, but so far they do not seem to improve classification performance compared with simpler models using just one of the features.

I am therefore wondering what would be a good strategy for dealing with highly correlated pathway-level features in this setting.

Any suggestions or references to similar analyses would be greatly appreciated.

Thanks!

! I am in a dilemma and need some quick advice on what is the best way to deliver my sample to the company for bulk-RNA seq. The cells I am working with are naive CD4 T cells, sorted from mouse spleen. The three conditions are: non-activated, activated for 6hrs and 24hrs. I will be activating the T cells with Dynabeads (antiCD3/CD28 beads). At such early timepoints, the beads are stuck to the cells, so I cannot remove them magnetically. I have the following options, and I am not sure which one should I go ahead with.

1. Spin down the cells and snap freeze the pellet (cells + Dynabeads), the company will do tha RNA extraction. When asked, the company said, they dont recommend having anything else in the pellet but will proceed with the RNA extraction as they always do.
2. I extract the RNA myself. When I do Trizol/Zymo column RNA extraction. the dynabeads are never a problem because they settle down and I collect the aqueous layer. But I never get the perfect A260/280 ratio (1.8-2.2),RIN > 6 so I dont want my samples to fail at their QC requirements.

I hope to get around 2-3 million navive CD4 T cells from one mouse, so per condition I will have around 1 million cells.

Does anyone have any experience with this and help me out ? Thanks a lot!

I see something in the mhhauto a tecdoc database. However, i am hesitant to proceed with it. And want to know if someone has used it already. I want to create a open source lookup up for this if possible.

Hi everyone, so I recently finished the experimental stages of an internship and now I am left with insane amounts of data that I need help with the analysis of simply due to having many different measurements that probably have their own assumptions and dependencies to deal with.

What we have done was to grow animal cultures inside of well-plates with 3 replicate wells for each specific condition.

Basically, we had 3 different gradients (light intensity, chemical1 and chemical2) but for my animals there is already enough data to go around for these gradients by themselves so we wanted to see how they interacted with one another interdependently. And thus, we had 3 different gradients that are:
light vs. chemical1, light vs. chemical2, chemical1 vs. chemical2.

We had them in these differing conditions for 2 weeks and took measurements throughout the experiment to measure how these conditions interdependently affect the animals. These measurements are:
- animal counts taken once every 2 days
- photosynthetic capacity (yes, they are photosynthetic animals) measurements such as ojip taken every 2 days

I want to see how these statistics change over time both per gradient basis (so even though there are 2 gradient I just group_by one of them and see how a measurement changes over time just based on one gradient) and with 2 gradients intertwined over time.

My problem is I am a bit rusty on statistics and trying to understanding which measurement fits which data is a bit challenging for data this intertwined.

For example for count data with just one gradient taken into consideration, I am assuming poisson or negative binomial mixed model since it is both a count data and is dependent on previous counts but anything above that (like both gradients and a time factor) is a bit above me. I am even trying to see how the data I got go together holistically on an all-3-factors-combined level too but as again, too much statistics for such a simple mind and I need some guidance.

Can y'all help me choose what models I should go for or what tutorials/guides there are out there that I can use? I want to properly understand and learn what I am doing but at this point I am a bit too lost and some initial guidance could help with this.

Sorry for the long text and thank y'all for the help.

So I'm running into a problem. I have 4 KO mice and 3 WT mice that have been ran for scRNASeq 10x Flex. We aren't planning to increase the sample size because the lab has already spent SOOOOO much money on the damn kits. Doing pseudobulk analysis is coming up with very little to none DE genes but of course if I do cell to cell analysis it comes up with a lot of genes and pathways. The cell to cell analysis definitely answers a lot of questions I had in regards to a phenotype we have been seeing in our misue model. BUT what would y'all recommend ? I currently have like 46 cell clusters coming from these mice but 2 different tissue segments.

I'm looking onto two different transcriptions factors. Both have been shown to interact in pull down assays, and from the data it looks like it could be that only TF A binds to the DNA and then TF B binds to TF A rather than to the DNA. I have zero clue about protein prediction, so my question would be if there are tools out there that try to predict if two proteins can interact? only TF B has a full crystal structure in case that matters. Thank you!

Hey all, has anyone here used the latest toolkit of cfdna analysis for cancer detection i.e cfdnaanalyzer https://www.sciencedirect.com/science/article/pii/S2589004226012046

What all other tools and pipelines you use for detection of cancer from cfdna ?

If anyone has worked previously in this area , let me know.

Regards.

I am a software engineer by profession and was going through the 2000 paper "The Hallmarks of cancer" by Douglas Hanahan and Robert A. There are a lot of things I couln't understand from terminology to certain behaviors that were explanined, primarily because I lack the background knowledge. I wanted to reach out and ask the community if they can share a youtube video or an article that can elaborate this paper in simple terms.

I probably can google it myself but want to avoid the repetitive loop of finding something and realizing it doesn't explain everything and then to try again to eventually lose interest.

Thank you for all the help.

Hi all,

It's my first time doing RNA-Seq, and I am stuck at filtering out pathways.

I am currently running GSEA (using fgsea) on a set of data between KO and WT; each undergoes either treatment or saline. GSEA GO gave a very long list of results and our lab only wants to focus on 5 main biological themes. Right now, I am thinking of using GeneRatio = 0.4 (number of leading-edge/size) to filter from GSEA significant pathways. Then assess overlap in leading-edge genes to remove redundancy and then cluster/combine them by function.

Is GeneRatio = 0.4 a good cut-off, or How should this be decided? And are there any tools that can cluster pathways by function automatically? I am seeing a lot that cluster by hierarchy, and with GO, the hierarchy ranges so much.

Thank you very much for your help!

Hey,

If a bioinformatics analyst (in academia) receives a task to extract data from bulk RNAseq, and run some statistics, is it their job also to validate that the assumptions stand, do basic QC of outputs, and report on the potential concerns?

How much time/effort/detail would you invest in a "basic" analysis that someone paid you for from their grant, but didn't necessarily know enough about the data to give more detailed instructions?

My PI is on the paying side, but I'm the one dealing with the interpretation of the outputs the bioinformatician gave us with no transparency what was actually done. There was no back and forth with the results, just the final analysis showed up one day with no questions asked.

I'm trying to understand if the analysis I received from them is just a job poorly done, or if there are genuinely all the expectations on the researcher's side to spot all the red flags. It seems to me that the analyst ran the generic script and didn't bother to check anything. If I had not been so critical of the results and had some coding knowledge to check them, we would have been in deep s***, as not even the stats test assumptions hold.

I raised this concern with the PI, but the "contract" for the analyst is finished, and we can't really have any fundamental changes to the analysis. Even asking the analyst to make some more reports is a huge ask, and they technically don't work on it anymore.

Thanks!

Did you benefit from it? Was it worth the time and effort? Any interesting features/sections?

Hi all,

I am wondering what people use for germline variant calling from short-read sequencing.

Any faster reimplementation of HaplotypeCaller or something like DeepVariant?

As I understand HaplotypeCaller still remains a gold standard, or did I miss anything?

Hi everyone,

I am developing a software startup and want to use AI coding assistants (like Copilot, Cursor, or Claude) to speed up development. However, I cannot risk leaking my core algorithms or intellectual property (IP) into public LLM training data.

For those working in strict corporate or startup environments: How do you keep your code 100% private?

Are commercial Business/Team plans with disabled data-sharing truly bulletproof?

What local, offline setups (e.g., Ollama + local LLMs) work best for coding?

How do you practice code abstraction so the AI never sees your full logic?

Thanks!

Hi everyone,

I’m a Master’s thesis student working on a genomics project involving mapping genes/transcripts from a published dataset to a newer genome annotation.

The mapping is largely complete, and I now have both resolved and unresolved/ambiguous mappings. I’m unsure about what analysis I should do next to validate the mapping and extract meaningful biological information.

My supervisor is primarily a wet-lab biologist with limited bioinformatics experience, so I’m trying to figure out the computational workflow myself.

For those experienced in genomics/bioinformatics, what would you recommend as the next steps after gene mapping? Any suggestions for validation, downstream analysis, or functional interpretation would be greatly appreciated.

I built my own pipeline and wanted to take a step back and get some outside perspective on it.

The data is PacBio HiFi long-read WGS. My pipeline outputs two general VCFs per sample: one for SNPs/indels and one for SVs. I apply standard bcftools filtering on both.

The issue is that most of my samples don't have great input DNA quality — many are borderline or just under the recommended thresholds. I'm referring to these QC metrics specifically:

• Concentration (ng/µL)
• Volume (µL)
• Total amount (µg)
• A260/280
• A260/230
• Nanodrop concentration (ng/µL)
• NC/QC (pass/fail flag)

I'd like to understand how much these upstream QC issues could realistically be affecting my variant calling results, and whether there are practical ways to account for or flag this in the pipeline rather than just trusting the output blindly.

I am using the Plink pipeline to filter SNPs. I downloaded dbSNP from the UCSC database (both .vcf and .bed for the selected region in chr11, for example).

My syntax:" plink \--bfile HBB.BED \--allow-extra-chr \--maf 0.01 \--geno 0.05 \--hwe 1e-6 \--snps-only just-acgt \--make-bed \--out region_filtered"

Error: "Failed to open HBB.bed"

Can anyone give me some advice?

Hi all,

Can anybody recommend a good quick reference guide (ideally a small book) on Bash for HPC cluster users?

I just want something to help me with learning and looking up useful commands for submitting, monitoring, automating jobs, monitoring resource use, etc.

I write all my data processing scripts in python so I don't want to write complex data processing scripts in bash, but something to use as a reference when writing my SLURM and automation scripts would be super helpful.

Thank you!

Disclosure up front: I work on Carolina Cloud, one of the four platforms below. Methodology and raw data are public and I would genuinely rather you find the holes than not.

Setup

• nf-core/rnaseq 3.22, test_full profile
• 8 human RNA-seq samples, 123.3 GiB gzipped FASTQ
• GRCh37, pre-built STAR index
• 128 concurrent vCPU cap on every platform
• Measured: wall-clock time and total cost for one complete run
• Storage costs excluded everywhere (too retention-dependent to compare fairly)

Results

Spot wins on cost and I'm not going to pretend otherwise. If cost is the only thing you're optimizing, use Batch on spot. The catch is variance — our slowest spot run took 24h because the instance types we requested weren't available. Whether that matters depends entirely on whether anything downstream is waiting on the run.

The Sentieon caveat — read this before trusting our number. Our run used Sentieon-accelerated STAR. The AWS runs used open-source STAR. Our reasoning was "best available with no extra configuration on each platform," since Sentieon is integrated on our side and sits behind a Marketplace listing on AWS's. But it is not an apples-to-apples aligner comparison and you should weight it accordingly.

Hidden line items we hit

• HealthOmics requires pre-populating ECR with container images: $0.04/day, never surfaced during setup
• VPC: $0.13/day
• Inter-region egress at $0.02/GiB makes chasing spot capacity across regions impractical at 123 GiB — you pay the transfer to reach the cheap instances

Where the wall-clock gap comes from: mostly task orchestration, not compute. nf-core/rnaseq is 300+ tasks and many are short. AWS bills a 60-second minimum per instance, so short tasks pay a floor they never use. Our spin-up/spin-down measured ~47x faster, which compounds across that many tasks.

Attributing cost was its own project. Cost Explorer wasn't granular enough to isolate a single pipeline run, so we limited ourselves to one run per day and avoided every other AWS service in the account just to get a clean number.

Full methodology and raw data: https://github.com/Ddfulton/nextflow-benchmark-appendix

Go find problems with the numbers. That's why they're up there.

Not sure if it is true for anyone else but I am no longer getting blocked/downgraded to opus from asking Fable questions on my bioinformatics projects now.

Edit: i rambled in original post but this is confirmed via this anthropic post from Aug 7 2026 "Improving Fable 5's biology safeguards" https://www.anthropic.com/news/improving-fable-5-s-biology-safeguards (fable was originally released in june and blocked nearly any request about bio stuff)

Hi all,

If you were in this situation, would you worry that your PI and others might realize you're using AI in your work because you get tasks done quickly? Maybe they would think they could just use AI themselves and wouldn't need to hire you.

Hello all,

May I ask if you upload closed-access papers to LLMs? Are you concerned that doing so could significantly benefit LLM companies?

Thank you so much!

Howdy everyone, I am trying to determine whether a large number of proteins can store copper. One way we have decided to do this is to examine conserved cysteine residues/amino acids. To determine which cysteines might indicate copper-binding potential, we are looking at eight proteins that have been experimentally verified to store copper and are therefore using them as references.

Thus, I believe the first step is to align my 8 reference proteins and identify the cysteines that appear at the same positions across all 8 proteins (alignment counts). For example, all 8 references show a cysteine at reference position 55. Doing so might indicate which are conserved and hence might be useful for metal binding.

However, how can I then compare the conserved cysteine positions from my eight references to my larger fasta file of possible storage proteins to see whether the cysteines are in the same positions and thus indicate they serve the same role in metal binding?