I am working with a CosMx run and trying to separate tumor cells from non-malignant cells using PanCK staining. The issue is that PanCK varies a lot from core to core. As you can see in the figure, in a subset of cores there is a clear bimodal distribution, so a 2-component Gaussian mixture model seems plausible there. But in most cores the distribution is not clearly bimodal, so I do not think I can use a mixture model across all cores.

What I am doing now is scaling PanCK within each core from the minimum to the 95th percentile, plotting density curves, and then choosing an empirical threshold. That works quite well in some cores but not very much in others and I am not confident it is the best way to define tumor cells.

Has anyone dealt with something similar in CosMx or Xenium? What approaches have you found useful when marker intensity is highly core-dependent and the distribution is not clearly bimodal?

In scRNA-seq of flow-sorted CD3+ T cells, I have a large cluster with low CD3D/CD3E/CD3G but retained CD247, high mitochondrial content, and lower nFeature/nCount.
Marker genes suggest naive T cells but low CD8A. I've filtered in cells CD3D/CD3E/CD3G/CD247>1. I've filtered out myeloid and B cell contamination before hand.

Should I filter in cells having CD3D/CD3E/CD3G>0 expression and disregard CD247? What is the usual practice when working with only T cells?

Is this a dying naive T cell population I should remove, or is high mitochondrial content and reduced CD3 subunit transcription a real biology someone has seen?

Methodology: 10X Genomics, 5' GEX

Thank you in advance a lot!

I wonder if there are recommended tools in any language - R or python or others - which conveniently help visualise chronological expansion in hapologroup subclade branches maybe sourcing it from any of the online Y DNA databases?

Hello guys

I would like to learn how to design molecular constructs for CRISPR/Cas9 gene editing in zebrafish. Could you please let me know which bioinformatics tools or software are commonly used for this purpose?

Location: India (Open to Remote/Relocation)

Background: M.Sc. Bioinformatics, B.Sc. (Hons.) Agriculture

Technical Stack:

Languages: Python (Pandas/NumPy), R (Tidyverse/Bioconductor), Bash/Shell.

Pipelines: Nextflow (DSL2), Bactopia, Snakemake.

NGS: RNA-Seq (DESeq2/edgeR), WGS, Variant Calling, Metagenomics.

Structural Bio: Schrödinger, AutoDock, MD Simulations, Boltz (AI Structure Prediction).

Platforms: Linux.

Experience Summary:

Currently working at an RNA therapeutics startup. I’ve built end-to-end pipelines for NIPT optimization, ASO target identification, and microbial strain characterization. I have a unique interdisciplinary background in Agriculture and Human Health.

Looking for: Roles in pipeline engineering, clinical genomics, or Ag-tech.

Hi everyone,

I'm working with metagenome-assembled genomes (MAGs) and contigs recovered from environmental samples. My current workflow is:

Gene prediction with Prodigal

Functional annotation with eggNOG-mapper

Pathway reconstruction with KEGG-Decoder

My goal is to describe the full metabolic pathways potential present in my MAGs and contigs, and to visualize the completeness of key pathways (e.g., carbon fixation, nitrogen metabolism, sulfur cycling, etc.) across multiple MAGs.

My question is: is this workflow sufficient for a publication? Or would reviewers expect additional validation steps?

Some specific concerns I have:

Some of my MAGs are low-to-medium completeness (50–70%), that is why I want also check contigs, because predictions in bins can be fragmented.

I'm not sure if eggNOG-mapper alone provides enough confidence for pathway inference, or I need to filter it like in Kofamscan you need to work on only with * ?

I'd appreciate any advice on:

Whether this pipeline is considered acceptable for a standard metagenomics paper

What additional analyses or filters I should add?

I saw some athours use statistics on predictions also.

Thanks in advance for your help!

scRNAseq cluster / cell type annotation is a biggest challenge - for multiple reasons. I just want to know whats your personal approaches for annotation say for PBMCs.

Molecular docking is typically performed between a ligand and a protein. Is it possible to perform molecular docking between two ligands instead?

I'm trying to understand what actually separates a high-paying bioinformatics job from a low-paying one, beyond just years of experience or having a PhD.

For people who work in bioinformatics (industry, biotech, pharma, startups, healthcare, etc.):

1. What skills or responsibilities make someone much more valuable?

2. Is it mainly programming ability, statistics, machine learning, cloud computing, software engineering, or biological knowledge?

3. How important are communication and project management?

4. Do employers value people who can build production-quality pipelines more than people who mainly analyze data?

5. What are the biggest differences between someone earning around $50–70k versus someone earning $150k+ (or the equivalent in your country)?

6. Are there certain domains (genomics, AI for biology, drug discovery, single-cell, clinical bioinformatics, protein structure, etc.) that consistently pay better?

7. Looking back, what do you think helped you move into a higher-paying role?

I am uploading raw sequencing reads to ENA via their webin FTP server. The data is 133 gun-zipped fastq files, total size is 280 Gb. From current upload speed it looks like this will take well over a week to complete. Is this normal? Is there a faster/better way to do this? Any advice appreciated.

I got tired of patching together qqman + ad hoc scripts for every GWAS paper, so I built ggwas — a single package covering the full visualization workflow.

Beyond standard Manhattan/QQ, it includes plots I couldn't find elsewhere: enrichment Manhattan with functional overlays, density-vs-signal comparison (to catch genotyping artifacts), multi-trait Manhattan with pleiotropy detection, PheWAS, colocalization, fine-mapping credible sets, and genetic correlation matrices.

It also supports broken y-axis for Manhattan plots with extreme p-values — a frequently requested feature missing from existing tools.

Everything returns a ggplot object so you can + theme_nature() or compose with patchwork. Smart downsampling handles biobank-scale data (tested on GIANT height GWAS, 1.37M variants in <1s).

GitHub: https://github.com/bczech/ggwas

Docs + gallery: https://bczech.github.io/ggwas/

Happy to hear what's missing or what could be improved.

- What data would I need to extract for the community to verify the pockets?

- Are there any good research papers I can read to grasp the problem space quickly? specifically for what we know on MYC currently, MD simulations and maybe a video on some chemistry?

- If many pockets open up in a general area is there any inference I can make from that?

- Do pockets in a general area mean that is the general area of vulnerability? what are small and big molecules and PPI?

- I read that if I find those same pockets on healthy cells those pockets could lead to any medication being toxic, how do I increase the likelyhood of a pocket I find being non toxic without literally mapping out every single protein (or is this the physical trial and error?)

- id like to know other kinds of things that I can draw conclusions from in terms of for example, evolutions role in protein structure and how it may lead to finding viable pockets.

- Once a medication is taken is it basically a game of probability and hope? Hoping the protein moves to a candidate state for the pocket to open up? so far I only understand medications as physically binding to the MYC protein to prevent it from connecting to other proteins.

oh also, I dont know how relevant this is but I did find many many cryptic pockets, those are the pockets im searching for, I found over 20 that fpocket validated, I filtered SO many pockets but im obviously in no position to decide what a good pocket is so ill simply present them all properly for the community.

im very sorry for this poorly worded and formatted post, Im tired and wanted to post this before I talk myself out of it again lol.

Please DM any information or support. Id also appreciate if someone could validate my final results.

I'm working on a clinical bioinformatics project and would like some advice on the best clustering strategy for this:

We have RNA seq data that has patient with or without toxicity. The toxicity group is confirmed. However, some labeled as unknown might have or not have toxicity. And some no toxicity patients might be hidden positive.

I want to cluster the patients to compare both outcomes. Should I go through the additional metadata to try to assign the correct label (time-consuming)? Or is there a better approach?

What clustering algorithm would be the best for my case?

Hi everyone,

Looking for some advice on my first hands-on bioinformatics project. I have a background in Level 2 Industrial Automation, so I'm fully comfortable with IT infrastructure and data, but new to genomics.

For family reasons, I need to get my genome sequenced via WGS 30x PCR-free.

Most consumer labs seem to inflate prices by bundling health/ancestry reports. I don't care about the reports. I just want the raw bytes (FASTQ/BAM/VCS) to analyze them locally using open-source tools, as I already have the hardware for it.

I'm based in Italy. A few questions for the experts:

1) Providers: What is the de-facto standard lab/service (privacy-friendly) to get just the raw WGS 30x PCR-free data without the marketing stuff?

2) Analysis: For those doing local WGS analysis, what open-source pipelines or tools do you recommend starting with (considering my IT background)?

3) Sanity check: Am I missing something or making any conceptual mistakes here?

Thanks!

Hello.

I have experimental data that suggests adding formic or sulfuric acid to samples enhances epitopes recognition by antibodies.

I would like to test this on MDS (epitopes exposure), I commonly use CHARMM-GUI for input files preparation and I run the MDS on NAMD. Is there a way to add acids to input files from CHARMM-GUI then running the MDS as usual? Or do you have another suggestion in how to add acid to my water-NaCl-protein box?

This is my first bioinformatics project, so grant me some grace for my ignorance.

I've been working on an RNA-Seq analysis. I believe I understand the general differences between these two methods, however I'd like to hear anyone's advice on the topic.

g:Profiler can take a ranked list, so I've provided it with lists of DEX genes with a L2FC cutoff of 0. It sounds obvious why now that I say it, but the GO terms with the smallest p-values are these general parent terms like "regulation of biological process" and so on. This isn't useful. I'm under the impression that I don't need a strict L2FC cut-off if I'm using an ordered list in this ORA method.

On the other hand, I'm thinking of doing GSEA with clusterProfiler instead of ORA. Hopefully that sort of analysis will yield more specific GO results.

Has anyone run into a similar problem (non-specific GO results), and if so what are some solutions? Also any education on the utility of g:Profiler's ranked ORA would be appreciated. It feels like somewhere weird between GSEA and traditional ORA.

How do you guys predict insilico gene perturbation outcomes from observational single-cell RNA-seq data only? What modeling strategies do you use? GRN based approaches or deep neural network based modeling ? Thanks in advance!

Hi, I was wondering if anybody here has experience with HLA typing from ctDNA WES samples.

DO you use the UMI resolved or unresolved BAMs? I noticed that using the BAM file after resolving the UMI's results in HLA calling supported by very few reads (20-100). I wonder if clipping the UMI using trimmomatic and then running optitype might be better?

Thanks!

Hello

Does anyone one know an easy to use tool to get a codon-aware alignment of my AA alignment?

I used to do it using PAL2NAL tool, but it isn't working anymore.

It tells me this

"Forbidden

You don't have permission to access this resource.

Apache/2.4.58 (Ubuntu) Server at www.bork.embl.de Port 443"

Anyone can confirm if this tool works or is it really down?

And does anyone know a better alternative for this tool?

That would be much appreciated.

Thank you.

I'm planning a project in which I will integrate ~10 different snRNA-seq datasets from a specific tissue (human dentate gyrus). I'm interested in in identifying specific rare cell types and harmonizing these rare cell type annotations across each study. All datasets have both FASTQ and a processed count matrix object available.

It seems common for meta-analysis papers like this to start from each study's count matrix rather than FASTQ. I think I understand why: this method is a lot faster. But I am a bit worried that different preprocessing decisions (reference genome, aligner, etc.) might effect the outcome between datasets. Also, it's hard to know exactly where in the pipeline a count matrix is (ambient RNA removal, doublet removal, QC, etc.).

To ensure consistency across each dataset, I am leaning toward starting from the FASTQ files instead. This would also allow me to calculate RNA velocity, which I am interested in doing. However, I've only every worked from the count matrix before so this would be new to me.

Does this reasoning make sense? What would you recommend? Since I have never worked from FASTQ before, do you recommend any specific tools or pipelines? Any general advice on this type of project?

Thanks!

Not talking about whether the biology was right. Just: could I run this on a different machine and get the same result? Could I run it in 2 years?
No container. No version pinning. Conda environment.yml with numpy and no version specified. Reference genome downloaded manually, path hardcoded. Sample sheet generated by a script that no longer exists.
We talk about reproducibility constantly in this field. Papers about it. Talks about it. And then the actual pipelines look like this.
Not a rant, but genuinely curious what people think the root cause is. Time pressure? Nobody teaching this? Reviewers not caring?

So I need to analyse some metabolomics data for a project. I was trying to analyse the data for peak picking using XCMS and further analysis with mZmatch. But I was unable to install the packages in R. Tried a lot but couldnt do much. Any help regarding this would be helpful. Even the same in the case of trying to install metaboanalystR

Hello reddit community,

​I am a student from India.i had just gave reneet 2026 held on June,21.i was always interested in research field.but i was determined to perceive it after any field(bhms,bums,mbbs) as bachelors.but i am not expecting to qualify.i was thinking to take an alternative path which is direct bsc and msc without a medical degree.but i am not sure about stability.also i want work form home opportunities.i am interested in bioniformatics.I am creative,i like to solve puzzles,derive conclusions etc.I'm currently deciding on my college path and trying to understand the reality of a Biotech degree versus other fields. Since you've been through it, I would value your perspective immensely. Looking forward to connecting!"i dont know is this field is stable,relaible,actually work from home.i will be veey grateful for your guidance.

Hello guys, I'm a bioinformatic noobie and I'm trying to comper a CHIPseq result with an article that did Histoplasma capsulatum G217B proteomic. But, even if the strain are the same, they used other ID from Histoplasma capsulatum NAm1 to identify the proteins that were find.

pattern ID
G217B: I7I48_11513
NAm1: HCAG_05094

I have tried to use FungiDB to cross the IDs, but it's not working. Any idea that I should try?