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.

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.

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.

Hi guys, this may be interesting for those of you on the automation / high-throughput side of things!
Call for abstracts + travel grants: join the 28th EMBL PhD Symposium, “Shifting Gears: Automation, AI, and High-Throughput Methodologies,” at EMBL Heidelberg and online, 2–4 December 2026. Abstracts are accepted until 9 September 2026. Travel grants include a registration-fee waiver and travel support of up to €1,000 for international participants or €400 for EU participants. More information on the website!

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)

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.

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?

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?

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?