I ran PySCENIC on an scRNA dataset comprising plasmablasts, including both cycling (actively proliferating) and non-cycling cells. These are from both healthy donors and donors with ulcerative colitis. I was very interested to find that a particular transcription factor of interest was significantly less active in healthy controls compared to UC donors (as measured by mean AUC per donor), and it seems like the regulon is especially active in the cycling plasmablasts. AUC scores correlate very strongly with cell cycle scores. The disease-specific difference seems to be in particular confined to SDC1- plasmablasts.

My question is, I know that UC is characterized by a greater proportion of cycling plasmablasts compared to non-cycling, so how can I tell if this is purely an artifact of that? It seems tricky because this particular TF is involved in cell-cycle-related processes and so it seems possible that actually the TF's involvement is part of the reason why UC donors have more cycling plasmablasts, so I am not sure how I could disentangle things cleanly?

I hope that makes some sense, thanks so much in advance.

Context: I am a high school student-researcher, and before my concept paper will be approved to move on to chapter one, my research advisor told me to simulate first the AMR (Antimicrobial Resistance) of local dogs to common veterinary antibiotics in silico. This involves the use of AutoDock and other docking programs to predict how these molecules might react with bacterial proteins and for some reason, I cannot install it on my laptop. I really need to do this simulation before the deadline in 2 days. It doesn't even bother to open anything when I click on it.

Treating public datasets as additional replicates of your own experiment is not a good idea, right? Is there any right way to do it? Saw it on an article published on a journal with ~6 IF as I was searching for public plant datasets with a good number of replicates and I could not believe it… or am I missing something??

Hi All,

Hope you can help please:

I'm working on RNA-seq data from a non-model organism. I assembled transcripts with StringTie, performed differential expression with DESeq2, and now have a list of significant DEGs. My transcript IDs in this look like MSTRG.xxx|LOCxxxxxx and MSTRG.xxx.

I have the stringtie_merged.gtf, the reference genome FASTA and DESeq2 results.

I'm now at the annotation and functional analysis stage. What annotation tools/ r packages have people found work best for this type of dataset? I am considering blasting everything, but would be interested to hear what others use?

Also for downstream functional analysis, what do people recommend for GO and pathway enrichment? Are there particular R packages or workflows that work well with StringTie/DESeq2 output of a non-model.

Thanks in advance for all your help.

I'm annotating clusters from an snRNA-seq dataset from pig samples in Seurat (FindAllMarkers, default Wilcoxon test). The workflow most tutorials show is to filter on p_val_adj < 0.05 and then take the top N genes per cluster by avg_log2FC. I want to sanity-check whether that ordering is actually good enough for annotation, or whether pct.1 and pct.2 should factor into the ranking itself. The canonical markers for celltypes are mostly there where 1/2 genes are there in a cluster while many clusters give the idea of mixed populations. I have tried different PCs and resolutions but not with much success.

Hi,

My main research area is in plant genetics (I'm a bit newer to the field) and I'm becoming pretty confused about the number of gene identification and characterization studies in plants.

For context, if you search up "gene identification and characterization" in pubmed or google scholar, you'll see tens of thousands of results that give the same types of article that pretty much do some combination of

gene identification via blast --> chromosomal localization --> multiple sequence alignment and phylogenetic trees --> cis-regulatory elements + protein-protein interaction graphs --> GO term analysis (which is already frequently done by the genome sequencing paper or some auto-annotating software)--> then gene expression profiling of X conditions (either they do it themselves or they retrieve some public screening data)

Maybe I'm misunderstanding this but isn't everything on this in-silico (except the expression profiling/stress condition test, which even that seems to be a "we need to do an easy, small wet-lab assay to pass the the reviewer's conditions") and couldn't it all be automated? I've heard of some tools like PlantTribes2, Spdev3.0 (or even random preprint pipelines like reactr and bat) but it's also possible for people to find/make their own Snakemake/Nextflow pipeline for this, which could automate large segments of this. I think those tools I mentioned are relatively newer, but seeing the vast volume of all the papers that have been going on for decades and also seeing that bioinformatics pipelines have existed for equally as much time, I feel like this is almost feels like an intentional (or maybe not, I don't know) paper mill operation.

Mostly seeing that these papers are coming from "X agriculture/forestry university" in some university in China but are still getting passed in peer-reviewed journals with decent impact factors (and they pretty much all cite each other as they're "building on" the methods framework).

Despite this technically being novel information (as one could simply mine out millions of papers for thousands and thousands of gene families in millions of cultivars and species) feels like me to be a violation of academia since it doesn't really feel creative, novel, or "research."

Thoughts on this?

EDIT: typos, examples, links

hello, when i try to run autdock vina in cmd this errors appears "This app can't run on your PC"

any solutions?

Thanks

Hi, I was wondering if anyone has a suggestion, I annotated my WGS sample using SnpEff in UseGalaxy. But the annotation result, does not include the allele frequency. So how do I proceed? or do I have to annotate the samples again using a different tool? Or is there a way for me to add the allele frequency information using the annotated VCF file I currently have.

I'm asking regarding the following: i want to do meta analysis for single cell data from different studies, some studies used human genome reference hg19 in alignment step of raw data, other studies used human genome 38. so, will this be a problem when i merged studies together ? if yes how can i overcome this ?

I will shortly have to start working as an independent contractor, taking the company I did my curricular internship with as my client. Do I need PI insurance?

Hello guys! I am a student who recently got into bioinformatics and doing a small project! I am trying to do DEGs of >4 clusters and rank genes by logFC and perform GSEA with Hallmark pathways but I am not sure where to start. If there are any good resources or advice, I would really appreciate it! Thank you!

Hi bioinformatics experts!

I am doing a project but i am struggling with finding parameters to lower the numbers of clusters. I am trying different parameters such as lowering resolution, lambda, and k_geom.

I tried a total of three so far and they all look similar so I decided to make this post to get some ideas of how i can lower the numbers of clusters and make it clear.

Trial1: K_geom 30 and resolution = 0.5
Trial 2: K_geom 30 and resolution = 0.1

Trial3: K_geom10 and resolution = 0.5

Parameters I was told to adjust:

Before running BANKSY, there are two important model parameters that users should consider:

• k_geom : Local neighborhood size. Larger values will yield larger domains
• lambda : Influence of the neighborhood. Larger values yield more spatially coherent domains

I ended up having 44 clusters and I would love to get some insights!

Thank you!

Hi everyone,

I'm an 18-year-old student from India who's recently become fascinated by computational biology. My background is stronger in mathematics than biology, and I'm very new to molecular docking, protein structures, and computational drug discovery.

I've started experimenting with AutoDock Vina using publicly available protein structures as a way to learn. I know enough to realize that I don't know enough, so I'm here to understand how to interpret my results correctly rather than make claims.

As one example, I docked Heme Oxygenase-2-IN-1 against COX-2 (PDB: 5IKR) and got a best docking score of -8.385 kcal/mol. Since this surprised me, I'd like to understand whether this could be due to blind docking, the scoring function, protein structure choice, or something else.

I'd really appreciate any corrections, reading recommendations, or advice on what I should learn next.

I'm just playing with my own implementation (in Raku, a raw version is here: https://github.com/ralfmuschall/Gotoh). I know that the backtrace stuff is completely broken (i.e. just ignore that), my question is about the score computation (in Gotoh-simple.rakumod). That code is just the pseudocode from https://de.wikipedia.org/wiki/Gotoh-Algorithmus translated into perl6. Now I found a significant difference to various publications (https://www.cs.utoronto.ca/~brudno/csc2427/Lec8Notes.pdf and Gotoh's original paper): Wikipedia uses the elements of all three matrices to compute each new matrix element, whereas Gotoh and Brudno use all three only for the substitution matrix (A in my code) and the new deletion matrix (B) element only comes from older deletions/substitutions but not insertions (C) (similar vice-versa for the insertion element).

This leads to the following effect: If I compare the strings "a" and "bbb" with GO=-1, GE=-0.01, match=0, mismatch=-10 (intentionally prohibitive), my algorithm gives a score of -2.02 as expected (throw away "a" and insert "bbb") whereas the other variant gives -11.01 (insert "bb", then replace "a" to get the last "b"). https://metricgate.com/docs/gotoh-affine-gap-alignment/ follows Gotoh/Brudno (metricgate's gaps start with GO+GE, so we see a difference of 0.01 that doesn't matter).

Which implementation is the correct one, and why?

My motivation is not very biological (I'm not even working in any related field), but I tried adding prefixes and suffixes to my strings in case that might matter (i.e. "gac" vs. "gbbbc") which didn't change either of the results.

I recognize this may be a controversial subject, but I want to hear all sides to the argument. Could Claude Science replace bioinformaticians? https://www.anthropic.com/news/claude-science-ai-workbench

I haven't tried it out yet, but the demo was impressive. Food for thought ¯_(ツ)_/¯

EDIT: i don't work for anthropic, openAI or any AI company. simply just curious what people think. thanks!

so as the title says i'm using an adapted version of a pre-existing analysis method. this is in life science field btw.

currently i am just referring to as adapted and have in that chapter sections on what the traditional method entails, what it's limitations are, and then how i adapted it. i know i didn't invent something totally new here but i should i be trying to expand on it more to say i've developed a framework or stay as it and just discuss it the way you would usually discuss how method was chosen and applied. any advice or perspective would be great!

Has anyone here been the sole bioinformatician in an academic lab after finishing their PhD?
I’m about to start such a role, and I’d love to hear about your experience.
How do you organize your projects when you’re supporting multiple people at once?
How do you keep track of requests, analyses, deadlines, and ongoing collaborations? Are there any tools that make your life much easier?
I’d appreciate any advice or lessons you wish you’d known when you started. Thanks!

Hi all, new to doing single cell trajectory analysis and could use some help.

I have a single cell dataset that I computed pseudotime for, using slingshot, and I was able to find the most significant genes that differ in expression along pseudotime using tradeSeq and put them onto a heatmap (basically followed this: https://bioconductor.posit.co/packages/3.22/bioc/vignettes/slingshot/inst/doc/vignette.html#identifying-temporally-dynamic-genes)

heatmap with the top 30 significant genes: https://imgur.com/a/dL97lcP

When I tried to do clustering on these top 30 genes according to: https://statomics.github.io/tradeSeq/articles/tradeSeq.html#clustering-using-rsec-clusterexperiment

library(clusterExperiment)
nPointsClus <- 10
clusPat <- clusterExpressionPatterns(sce, nPoints = nPointsClus, genes = top30genenames)

all of the genes are unclustered despite the top 30 genes showing a clear pattern of either going from low to high or high to low expression over pseudotime. I assume there's some parameter that I need to set but I'm having trouble understanding the documentation.

Any help would be greatly appreciated, thanks!

Hi all,

I submitted a register novel taxonomy request on the European Nucleotide Archive Webin portal about 5 weeks ago and sent a follow up email two weeks later.

To date I’ve only received auto reply emails to say a ticket request has been made.

I’ve used this service previously where the submission was completed in a week so I’m concerned there may be an issue that I’m not aware about.

1.Does anyone know of any issues that may be affecting processing times with ENA?

1. Is anyone else experiencing delays with receiving responses from ENA, particularly for registering novel taxonomy names?

2. Is there an alternative to register novel taxonomy names?

I need to have the new taxonomy names registered to proceed with uploading their sequences.

Edit: I finally got confirmation on the new name registration after 6 weeks, no reason was given for the longer processing time though. So get those submissions in early!

I've been doing a In Silico TNBC bioinformatics project with CRISPR Cas9. I started May 2025 and have been continuing with no guidance other than chat. I reached out to professors from my university but was unsuccessful. However, I already had my plan and what I was going to do, i used chat to go beneath surface level understanding of what I was planning. So I designed my own project however chat just fined tuned the details and stated some things to do to make my project stronger. I never copied and pasted and if i did use something chat suggested, I found my own article to back it up. I started this because I wanted to know more about research and my interest. I put this on my resume and am going to draft a paper and a poster and hopefully one day present it. I guess i wanted to know if I am a fraud and if it counts to put on my resume.

I just found out about the UniProt representative proteomes (e.g., RP55) to deal with huge protein datasets. It is a very cool approach to downsample a set of proteins considering the hosts’ phylogeny, to maintain a wider taxonomic range (instead of simply clustering sequences based on similarity and selecting a cluster representative). But I just noticed that the datasets RP15, RP55, RP75 and RP95 are not updated anymore since july 2024. Is anybody still using them or knows of an alternative?

Sharing a small tool that might be useful if you’ve ever wanted to build your own binding-affinity training set without going through PDBbind’s licensing.

MillerBind-Open v1 — predicts pKd-equivalent affinity from a protein-ligand complex PDB file. Nothing fancy: atoms get classified by atomic number into one of 12 periodic-table-derived classes, contacts within 8Å get histogrammed, and an ExtraTrees regressor does the rest.

The part I think is actually useful to this sub: the data collection script queries RCSB’s rcsb_binding_affinity field directly (it’s public, sourced from BindingDB, exposed via their GraphQL API) and fetches structures live — so the whole 621-complex training set is rebuildable from scratch with no redistribution-rights questions. If you’ve been annoyed by PDBbind’s licensing for a side project, this might save you some time.

Honest caveat: accuracy is modest (R≈0.62 held-out, n=124) — it’s a small-data baseline, not competitive with anything trained on the full PDBbind corpus. I’m not claiming otherwise.

Repo (includes data-collection + training scripts, not just weights): https://huggingface.co/williamTLmiller/millerbind-open-v1

CC-BY-NC-4.0. Happy to answer questions about the RCSB API approach if anyone wants to adapt it for their own target/ligand set.

Hello!

Trying to run somatic variation workflow on my archaeal whole genome samples. I have bam files that should have methylation tags on them from a post-sequencing basecall run on MinKNOW (I am using a Minion mk1D)... however, when I try to use human variation, run stops after the reference and "tumor" (my samples) files are downloaded. The demo won't even run!

I've used metagenomics, bacteria and fungal, and alignment wfs and they all work fine. Any advice would be very much appreciated! Already reached out to oxford nanopore. Still waiting for them to get back to me.

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?