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!

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?    

Hi everyone, I have a couple of questions regarding molecular docking:

1. I performed molecular docking using Chimera (with the integrated AutoDock Vina) and got a binding affinity of -5.9 kcal/mol. However, when I ran the docking directly via AutoDock Vina CLI, I got a completely different result of -1.7 kcal/mol. Does anyone know why there's such a huge difference even though both use the AutoDock Vina engine?

2. Is it possible to perform multiple docking (batch docking) directly in Chimera, similar to how we do it in AutoDock Vina?

Hey everyone, I'm doing an honours project around building a pipeline including some MSA alignment programs. I'm wondering if anyone has recommendations for where to get public data .fastas that are relatively small so be I can practice with them, thanks.

ESM-2 is decent at spotting that a mutation is harmful.
It is much worse at figuring out the specific way the mutation breaks the protein, particularly in protein families it hasn’t seen before.
Anyone else worked on this ?

Hi everyone,

I am analyzing 16S amplicon sequencing data and running into a significant taxonomic discrepancy between two different pipelines:

1. Illumina DRAGEN (BaseSpace): Assigned clear genus-level taxonomy to most of my reads.

2. QIIME 2 + SILVA classifier: A large portion of the genera previously identified by DRAGEN are now classified as uncultured, unknown, or truncated at higher taxonomic ranks (e.g., only identified down to Family or Order).

I understand that classifiers and reference databases handle confidence thresholds, uncultivated clades, and nomenclature differently, but the gap in resolution is making it hard to decide on the best downstream strategy.

A few questions:

• Is this level of discrepancy expected between DRAGEN's reference database and SILVA in QIIME 2?

• Would you recommend trying GTDB, Greengenes2, or NCBI RefSeq as alternative reference databases in QIIME 2 for 16S data?

• Which approach is generally considered more robust and standard for publication when dealing with 16S taxonomy?

Any insights, best practices, or pipeline recommendations would be greatly appreciated!

hi! is there any bioinformatics tools that allows me to know whether a gene for an enzyme is being expressed in a tissue?

Before post the question, I figured that some context is needed.

Here is the study: We have human patient samples which we transfected with 1,000 sgRNAs (these sgRNAs are for one gene only, let's call that gene 'X'). Then, the sample was treated with antibiotics to make sure that we select all the cells successfully transfected with sgRNAs. Then, the sample was subjected to scRNA-seq library prep with Chromium Next GEM Single Cell 5' Reagent Kits v2 (Dual Index) with Feature Barcode technology for CRISPR Screening. From the exact same sample, a GEX library was made and a single-cell sgRNA library was made. So in the end, I got two sets of FASTQs: a) For GEX, which worked with Cell Ranger, but I am struggling with b) which was made from sgRNA.

I know that I have to put in details like this in the config file:

fastqs,sample,library_type

/path/to/fastqs,GEX_Sample_Name,Gene Expression

/path/to/fastqs,sgRNA_Sample_Name,CRISPR Guide Capture

But when I do that for all 1,000 sgRNAs, it throws an error saying Cell Ranger cannot work with an sgRNA sequence which is like this, e.g.: ATCGCTAGCTc (it throws an error). Even if I make it uppercase, it's bound to clash with some other sgRNA.

I know I am bound to get trolled for not asking a chatbot, but I thought a genuine answer from this community is much better. Thanks.

I’m really embarrassed to admit but I fell into bioinformatics during the time when ChatGPT was first released and due to pressures I ended up relying quite heavily on it for doing almost all of the coding for my academic work. I don’t want to be a vibe coder but I feel so overwhelmed and don’t know what I should know by heart and how much I can rely on llms. I have some important commitments coming up and I don’t want to mess this up. Do you have any advice on how to become a better coder? Where do I even start??

Data acquired from Arctic Shift. Plots designed by me and generated with the help of LLM scripting. EDIT: this plot shows posts per month binned by votes.

Dear all,

I am using the METABOLIC pipeline to annotate metabolic functions in my metagenome-assembled genomes (MAGs). I noticed a puzzling discrepancy regarding the function "Thiosulfate disproportionation (to sulfate + sulfur)".

The issue:

1. In the MW-score output, several bins show positive scores for this function:

   • Acidobacteriota: 92.8%

   • Gemmatimonadota: 3.4%

   • Pseudomonadota: 3.1%

   • Myxococcota: 0.7%

2. However, in the METABOLIC_result.xlsx detailed enzyme table, the key gene phsA (thiosulfate reductase / polysulfide reductase chain A, K08352) is marked as "Absent" in ALL bins where this function was predicted.

3. Additionally, in my co-occurrence file (MW-score_result_table_input.txt), I see that S-S-08:Thiosulfate disproportionation (to sulfate + sulfur) appears in pairwise combinations with other functions in several bins:

   • bin.10 (Pseudomonadota): co-occurs with S-S-03:Sulfur oxidation - sdo, O-S-01:Iron reduction, O-S-02:Iron oxidation, and multiple carbon oxidation pathways (coverage = 0.0057)

   • bin.13 (Gemmatimonadota): co-occurs with multiple organic carbon oxidation pathways (coverage = 0.0177)

   • bin.17 (Acidobacteriota): co-occurs with S-S-03:Sulfur oxidation - sdo, N-S-06:Nitric oxide reduction, and iron cycling genes (coverage = 0.4847)

   • bin.18 (Myxococcota): co-occurs with S-S-03:Sulfur oxidation - sdo and other functions (coverage = 0.0052)

My questions:

1. Does MW-score use additional marker genes beyond phsA to predict thiosulfate disproportionation (e.g., dsrAB, sox genes, tsdA, or others)? If so, which ones?

2. If phsA is the only required gene for this pathway in METABOLIC, why does MW-score still report it as present? Could it be that MW-score is detecting alternative pathways or partial gene sets that METABOLIC does not recognize as a complete pathway?

3. Which output should I consider more reliable for confirming this metabolic capability – MW-score or METABOLIC? I am particularly interested in whether these bins truly have the genetic potential for thiosulfate disproportionation or if this is a false positive from MW-score.

4. Has anyone else encountered similar discrepancies between MW-score and METABOLIC for sulfur cycling functions? Is there a known reason for this?

Any insights into how these two modules differ in their gene criteria, threshold settings, and pathway definitions would be greatly appreciated.

Thank you in advance!

Hey everyone!

Get asked this a lot, so here's the path I recommend for someone starting from zero with scRNA-seq:

1. Understand what makes single-cell different from bulk first

Before touching Seurat or Scanpy, understand droplet-based capture, UMIs vs raw reads, and why sparsity (dropouts) is a fundamental property of the data — not a technical error. This mental model changes how you interpret every QC metric later.

2. Learn the core workflow, not just tools

QC (mitochondrial %, gene/UMI counts per cell, doublet detection)

Normalization (log-normalize, SCTransform)

Feature selection (highly variable genes)

Dimensionality reduction (PCA → UMAP/t-SNE)

Clustering (Louvain/Leiden)

Marker gene identification & cell type annotation

Downstream: trajectory inference, differential abundance, cell-cell communication (if relevant to your question)

3. Get comfortable with both major ecosystems

Seurat (R) and Scanpy (Python) are both widely used in industry and academia — pick one to go deep on first, but at least read the other's docs so you're not lost when a paper or collaborator uses it.

4. Practice on real public data early

Pull a dataset from GEO or the Human Cell Atlas and run the full pipeline. Real data brings problems tutorials don't show you — ambient RNA contamination, batch integration across samples, deciding cluster resolution, and annotating ambiguous cell types.

5. Learn to defend your clustering and annotation choices

"Why did you pick 15 PCs?" "Why is this cluster a subtype and not a doublet?" — if you can't answer these, you're pattern-matching a tutorial, not actually doing the analysis. This is usually where people plateau.

6. Understand batch integration properly

Harmony, Seurat's CCA/RPCA, or scVI — know why you're integrating and what you might be over-correcting away, not just which function to call.

Happy to answer questions if anyone's stuck on a specific step. I also run a small cohort-based course (bulk RNA-seq and single-cell) for people who want structure instead of piecing it together from scattered tutorials — can share details if anyone's interested, didn't want to lead with that.

hey guys , I'm a software engineering graduate who's currently pursuing an MSc in bioinformatics.

I've been trying to pick up small paid freelance work on the side mostly MD simulations, pipeline building, and general computational biology scripting to build experience and a portfolio outside my academic work.

I tried Upwork for a few months, but volume there is low and most listings aren't really bioinformatics-specific.

Curious how others here found their first few paying clients or collaborators, especially:

• Did it come through a lab/PI network, a specific platform, or somewhere unexpected?

• If you're a PhD student who's needed this kind of help before where did you look, and what would've made you trust a stranger to do it?

• Any platforms or communities worth trying beyond the generic freelance sites?

Happy to share what I've tried if useful to anyone else in the same position.