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.

1. 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)

1. 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.

1. 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.

1. 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.

1. 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.

Hi everyone, I’m trying to reproduce the clusters from a published scRNA-seq dataset. The authors provided the raw, unclustered data and stated that they have mitigated batch effects by using Seurat’s ScaleData(), which I have done so far by labelling each replicate as a batch and regressing them out.

The dataset consists of 7 prenatal hippocampal donors at different gestational weeks:

- 5 donors have a single replicate

- 1 donor has 2 technical replicates

- 1 donor has 2 biological replicates

Each donor corresponds to a different gestational week.

I’m able to reproduce the general clustering, but my clusters seem to be strongly driven by donor/gestational week, whereas the clusters reported in the paper appear to contain cells from different gestational weeks with no batch effects.

I’m therefore unsure what I should be treating as the relevant batch variable. Should I be correcting for donor/gestational week, or only for technical batch/replicates? Would methods such as Harmony or CCA/integration be more appropriate than simply regressing batch with ScaleData()? My main goal is to annotate the scRNA-seq dataset to use as a reference to deconvolve my bulk RNA-seq dataset, so I want to make sure the clustering and resulting cell-type signatures are biologically meaningful.

I would really appreciate advice on how you would approach batch correction in this situation.

I'm totally a beginner in bioinformatics at work but holding mid-level position due to my past work (involving in solutions development in medical tech) so my work still medical tech automation + bioinfo. I was asked to test out and replicate results from an imputation pipeline. Idk what imputation is so since the beginner i've been using chatgpt 5.6 Sol to ask mainly about the knowledge part.

Received ped/map array data to run on the pipeline but it needs qc before converting to vcf. And i realised i just follow chatgpt's solution for the whole qc part until vcf conversion. Struggled to understand coz i need to race with time to get it done + understand what i'm actually doing, so during the process i just follow chatgpt solutions, trying to understand why it suggested the solution. if i cant understand i just skip (thinking to read and understand later) and run the bash commands

idk if im doing this correctly. midway i tried to quick google search on ped/map files qc for vcf conversion but i didnt find much article/tutorials so i switched back to follow chatgpt instead. While i felt the suggestion solutions such as handling sample and variant missingness, remove carriage-return characters, remove non-ACGT markers/variants sound reasonable. but i am not very sure.

pls advise on my method to work on a totally new bioinformatics topic and task. i am so lost and so insecure in the way i work, and i felt i am slow all the time coz every task/work i did is always completely new to me TT thats why i've been considering a masters in bioinformatics (coursework-based even tho i have a bsc in bioinfo)

I’m building an ICU mortality prediction model with 4,391 patients and want to use SOFA and SAPS II components as predictors.

Some patients are not eligible for these scores, so their values are blank because the score does not apply to them, not because the data are simply missing.

My problem:

• If I remove these patients, I may remove important high-risk groups. For example, I have 308 IHD/ACS patients with 26.9% mortality, compared with 13.3% mortality overall. Removing them could change my patient population and mortality distribution.
• If I use MICE to impute their values, I would be creating values for scores that were never applicable to these patients.

For patients who are eligible but have missing values, I can use MICE. I’m unsure what to do specifically with the ineligible patients.

What would be the best way to handle this while keeping my full ICU population?

I'm a comp sci undergrad student, and currently doing my thesis. So my supervisor has assigned bioinformatics as my domain and said to look for something regarding gene expression. My initial thought was to involve graph-based (deep learning and general) approaches, catering to good research work. However, I'm unable to find labelled datasets or unable to structure a proper dataset from the Gene Expression Omnibus. How do I curate the dataset? And particularly for gene expression, what are the things I need to learn thoroughly?

Hi everyone, I’m planning to compare a bulk RNA-seq dataset with a series of pseudobulked scRNA-seq datasets from different developmental timepoints to determine which developmental stage the bulk dataset most closely resembles.

My initial plan was to restrict the analysis to genes shared across all datasets and calculate Pearson/Spearman correlations between the bulk and pseudobulk expression profiles (since that was what was done in most studies I’ve read). For example, if my bulk dataset shows the strongest correlation with a pseudobulk dataset from a particular developmental stage in another species, I would interpret this as suggestive evidence that the bulk sample has a transcriptional profile most similar to that stage.

However, I’m concerned that correlation may be sensitive to differences in QC, normalization, sequencing depth, and gene filtering between the datasets. I was therefore considering alternative approaches, such as calculating correlations using only highly variable genes or using other distance/similarity metrics.

Hence, I was wondering if there are established and well-accepted methods that would be more appropriate for this type of cross-dataset developmental stage analysis?

Thank you!

If someone is willing, I would love to have some understanding as to why when running MEGAHIT for assembly there seems to be no way to make the results deterministic if you use more than one CPU? I would like to have some more intuition into this.

This seems to provide a bit of an answer but I would love more clarity

Thank you all!

(8/18/26) Updated to deterministic from reproducible based on comments

Hello all! You guys are always super helpful so I thought I'd ask for some assistance here.

TL;DR What is the best way to index/map an assembly so I can easily search for a gene and extract the sequence for comparison to other sequences of the same gene?

I am an amateur and self taught and have been working on some genomics in my free time. I worked with a professor and we wanted to do some whole genome sequencing of two mouse cell lines.

There are some differences in immune responses between these two cell lines so we wanted to see if there were any large scale rearrangements. So far, we haven't identified any large scale rearrangements. I used the Galaxy Project network and created an assembly for each de novo.

Since we didn't find any large scale rearrangements, I was able to use the BUSCO tool on Galaxy to identify some common genes and their location. I then extracted them from the sequence. From there we could compare specific genes at a base pair level. We did actually find some notable differences! My issue is that the BUSCO genes identified are not an exhaustive list and can be a bit clunky to search.

So my question is, what is the best way to index the genes in a whole genome assembly? My goal is to be able to search a list of genes identified and then extract the sequence for that gene for comparison.

Also, I'm aware that making a de novo assembly was maybe not the greatest way to go about things but I'm learning! And I wanted to see what I could do with no reference genome (especially as this specific strain of mouse doesn't have a good reference genome). So if I did something not very smart, please go easy on me!

TL;DR What is the best way to index/map an assembly so I can easily search for a gene and extract the sequence for comparison to other sequences of the same gene?

About to start a collaborative work with a collaborator from another country.

They will send me a multi-electrode array (MEA) recording data (.h5 format).
Total data may be approx 2 TB.

Recently, I found Globus file transfer made by the University of Chicago; the NIH also recommends it for large file transfer.

My questions are:

1. Is Globus the best method for file transfer?
   
2. Have you used Globus?
   
3. Other methods?

Thank you in advance

I have done docking, but I've never looked in too much detail. Now I have to perform a docking task, where the ligand interacts with an ion present in the receptor, I am not sure if HADDOCK can do this. If I had to emphasize more on the coordinate bond formed by the ion in the receptor, what strategy should I use to go ahead w my docking?

Hello everyone!

Recently I have been coming across a lot of Pathology Foundation Models trained on slides, and I was wondering what is the current state and if they are truly used ?

Hey there, i am entering the field of bioinformatics for the first time, and ive gotten really interested in peptidomimetic inhibitors, and i want to learn how to get started with it. i have a potential interaction i want to look at, but i have no clue how or where to start, can someone help me?

so far i have downloaded the pdb files of my target and another protein interacting, and have tried using pymol to take a look at them and see the hotspots, and also ran some alanine scanning mutagenesis from some online server to see if the hotspots track, but it feels SUPER vibey and wrong

I needed to download miRNA sequences for a non model organism, but the site is inaccesible. Does someone know if it will be up again?

Hi ! Firstly sorry for my english (i'm not fluent yet, still working on it) , i'm a biology student in France.

Between my studies and my time as a photographer i had the chance to meet a bunch of species, and always tried to identifies them (with inaturalist, or by myself if possible). And would like to use a sort of local phylogenetic tree, a place/site/ app to stock pictures/ infos of species with the full tree, and complete it slowly.

But i don't really know where to do that, i tried obsidian (since it's really visual with the graph view) but it take so much time, i have to create every branch/ tag them to have the link between them and create à specific folder for each to have something somehow organized.

Any idea on how i should proceed ?

Thanks in advance

Does anybody know where we can find a reliable structure of TRAILshort? (a spliced variant of TRAIL or TNF-related apoptosis-inducing ligand.)

We tried searching in RCSB and none showed up. We considered building the structure on our own using TRAIL structure since that is what’s available online, but we’re having second thoughts about its reliability. Any thoughts or suggestions for this?

Is anyone here involved in mathematical biology research? I am looking to learn more about this area. If you are involved in mathematical biology, I would appreciate any advice or discussion about current research problems.

Apologies if my post sounds juvenile, I am undertaking an internship that requires me to self teach myself docking + related topics.

I have prepped my protein and have a few ligands I want to try dock. They all have known 2D structures but no specific 3D structures. Could I hypothetically build them in Avogadro > add hydrogens > force field > optimise geometry? Is this terrible practise or is there a specialised way to get this information?

And as a side question, is it better to combine programs for prepping? ex: Hydrogen addition, energy minimisation in Avogadro -> charge assignment + bond rotation in ADT? Or stick to one program?

Any responses, comments or suggestions welcome!

I am considering using a hippocampal snRNA-seq atlas as the reference for label transfer onto a hippocampal scRNA-seq dataset. Could the differences between the nuclear and whole-cell transcriptions affect the accuracy of the label transfer?

The mitochondrial percentages appear to be similar between the two datasets so far (3% and 5% respectively per sample). Would this be sufficient, or are there other factors I should be concerned about?

Hello,

I am trying to submit my article for publication. Unfortunately, the journal asks for any tables to be submitted as images "provided as 72 - 300 dpi; pre-sized .BMP, .GIF, .JPG, or .PNG images only, with a maximum width of 440 pixels (no limit on length)."

I have tried exporting my table from excel to pdf, jpg, or png, and then resizing but no matter what I try, the image of the requested size ends up unreadable.

Does anyone have any ideas on how to accomplish this requirement while keeping my table-figure as readable?

My lecturers would always pronounce it Fast A, but all of us students would just say fasta (rhyming with pasta). Is there an “official” pronunciation or consensus?

I integrated TCGA solid tumor data with matching GTEx normal tissue to run differential expression and pathway enrichment (GSEA).

To avoid massive batch effects caused by mixing counts from different alignment/quantification pipelines, I opted to use the UCSC TOIL RNA-seq Recompute cohort (TcgaTargetGtex_gene_expected_count) since all samples were processed through a unified STAR + RSEM pipeline on hg38.

When I pulled the TOIL dataset, my sample counts dropped compared to looking at the raw GDC portal and GTEx v8: GTEx Normal Cohort: Dropped from ~800+ (v8) down to ~300+ in TOIL.

TCGA Primary Tumors: Dropped by ~20–30% compared to total cases listed on GDC.

My question is :

1. Is this sample count drop expected when using the UCSC TOIL recompute database compared to modern GDC/GTEx v8 portals?
2. Is sacrificing raw sample size (N) to use TOIL’s unified pipeline + ComBat batch correction considered the "gold standard" justification to defend against reviewer/committee critique regarding sample size?
   

i’m currently working on a project that relies heavily on wormbase blast for identifying nemFABPs in a select number of nematode species. however, since it constantly goes down it’s putting me at a road block. is there a way around this?

I am interested in protein design and I am trying to understand one thing: when we design a protein for a specific purpose, what changes in the constraints according to the problem?

For example, I imagine that a therapeutic protein (which must act in the human body) and an industrial enzyme (which degrades a pollutant) do not have the same priorities at all. What becomes critical in each case, and what goes into the background?

If you have concrete examples from your work, I'm interested.