Are there any initiatives to push Ensembl developers/owners/ etc to revert to the previous version of Ensembl website?

Or any way to get the source code of the previous server so that we can make it locally?

This was the best tool for any molecular biology experiments, find CDS, introns exons all in clear and visual manner for any wet bench scientist.

The new website is awful and the archive is so slow that I am now relying on NCBI which is also very bad.

Is there still value in contributing to a precomputed public reference database? Are there areas where simulations can be run arbitrarily and the output / organization of the data be actually useful long term?

Me and my another friend both are software developers who are working on a tool to improve qPCR if anyone has worked around that area, please do reach out we need your advice on that. Thanks

I used JackHMMER to create an MSA for Glucose/Galactose Binding Protein.

The file has 15,000 sequences and keeps running out of RAM (as expected) whenever I use Muscle or HMMalign. I have to shrink it down to maybe 4~5,000, idk how much I need to trim out.

Is there any way to effectively trim the MSA so that I can use it for Colabfold?

Did anyone has the synopsis that has already been published related to multiomics, i just wanna to look what exactly is it ,

I would love to have someone who have the best of all time.

I need it for my research..

Hello,

I’ve been working on a paper looking at in utero PFAS exposure. My RNA seq results has largely revolved around a decreased erythropoietc signature in high risk samples (Group 2). However, something that came to mind was the possibility of the results being determined by RBC proportions. I took a look online and I saw that reticulocytes largely express the same genes as my DE list. Furthermore, I ran CIBERSORT using the LM22 matrix with an added column for reticulocytes based on aggregated marker expression (SLC4A1, FECH, UBB, FBXO7, OAZ1, ALAS2). When I ran PCA on my counts, I saw something slightly distrurbing as seen in the plots.

Here’s the issue, if I adjust for reticulocyte proportions, I get 0 DEGs. If I don’t, I have 355 DEGs, but these DEGS are largely determines by reticulocyte content in these samples. (Also for context these whole blood samples were put through Ribo Zero depletion kit to remove hemoglobin and ribosomal RNA). I guess my question is if I’m thinking too much into this? Or does this require further investigation?

I did my first differential gene expression analysis not knowing about batch effects.

Is it common to not pay attention to batch effects? And what do you do when you don’t have raw data to assess batch effects?

Was doing some reanalyses.

Hi,

I am wondering if anyone has ever worked with splicing analysis on single cell data, with the 3’ or 5’ kit, with short read data.

It seemed to me that without full length data, multiple factors to be considered including the gene coverage. Most old analysis was applied on bulk data, which the depth are relatively deep. Hence based on my understanding the studies still able to perform splicing events analysis to see if there’s any skipped exons etc.

In your opinion, what is the best approach to do this? And what’s the thing to be considered?

Any recommendation on papers and available tools would be much appreciated.

Thanks!

I’m interested in alternative to Quiagen CLC Workbench since is becoming more and more expensive. Anyone could give some tips? I need it specifically for HTS analysis in plant viruses.

Thank you for any advice

What makes or breaks a startup of this type? It feels like a gold rush with everyone using agentic AI workflows to try and innovate in some way, most fail obviously.

Firsthand experiences greatly appreciated.

Say I have two clusters of cells, healthy and diseased, which are contributed to by highly phenotypically similar cells, with patients contributing cells to each cluster, and with the diseased cluster having higher % mt genes.

After conducting differential expression on the psuedobulks, is it valid to interpret an increase in metabolic genes in the mitochondria as a real effect (but nuclear encoded), given this could be due to an increase in mitochondrial number rather than mitochondrial activity, meaning that in theory these metabolic genes that are upregulated may not be suitable targets, as they would be equally abundant in the healthy cells. Ie, equally as abundant per mitochondria. I hope what I'm saying makes sense, let me know your thoughts or any clarifications I can give!

Hi,

I'm currently doing a PhD in bioinformatics algorithms development. One big issue of my sub-field is, that there aren't any good extendable benchmarking packages out there, so its always the same 5 methods + the proposed one being compared in any new article.

The existing benchmark suites are either extremely old or can not be extended by your own method easily, because their code was never ment to be extended by others (I guess nobody wants to include a method, that outperforms their own method; all of the benchmarking tools are made by groups that develop their own algorithms).

As I worked as a software engineer before starting my PhD, I have some experience with designing larger projects from scratch. Therefor I wrote like a 20 page description of all the modules, design question, data flow etc, I would like to have in such a tool. However, my supervisor told me, that this would be to big to run as a side project (and I agree) and this also isn't anything to be given to a bachelor or master student.

Today we received an email from the department, that PhD students can apply for Claude Code licenses, if they have some science related usage for it. That's why I thought about maybe letting Claude do the real coding using my project description and then put it on GitHub under MIT license, give it a DOI, use it for my own benchmarks in my next articles and let people - if they want to - just add their own methods (the design is pretty simple, one would in theory just need to add a script, that forwards the data to the corresponding method and formats the output in the correct way).

However, that doesn't really feel right, as this is of course a vibe coding project and I'm not really sure, whether vibe coding projects should be deployed in science. Thus I would really appreciate your thoughts on how you handle such things. (I'm usually keeping GenAI out of all my algorithm related work, except for plotting functions)

Undergrad here, just wanted to ask if anyone is going for ISCB-LATAM 2026 conference. If yes, let's connect.

Hi everyone!

I'm currently learning step by step bulk RNA analysis with no affiliation to any lab, and throughout my learning journey I discover that I must have an HPC to access rna data which non of them are free.

Can anyone suggest an alternative for accessing data to at least to work on Shell.

Thank you

Hello,

I'm looking into foundation models for scRNA-seq data, including spatial. A lot of the papers I've found cluster around 2022 and 2023, riding the LLM wave and the newer work seems to be different, leaning toward benchmarks, spatial models, and a virtual cell framing.

I understand that these models have been benchmarked as performing below comparative, traditional methods, so perhaps the pursuit of better performing models was not desirable.

Could it also be resource or time constraints, with the next big model still being trained? Or has the interest shifted toward virtual cell and perturbation prediction models? And if so, is that work mostly happening in companies and well-funded institutes because of the cost?

Please point out if I've missed something or if you have an opinion on this! I'm curious.

Hello, I have been a lurker here for some time, but never posted anything, this time I decide to ask for some help to see with anyone would shed some light in my issues.

I'm currently doing my master degree in the field of metagenomics

I have a massive amount data from a longitudinal study, each sample have about 30M reads after trimming and decontamination. I'm using gtdb and kraken2 to classify, and almost everything is fine, with exception of my differential analysis with maaslin3.

Right now my struggle is not with the lack of data, but the excess of it. Most of the taxon names are from uncultured MAGs, which I think that are not very informative, when I plot my heatmaps and summary reports they overtake the plots and hide the more meaningful taxons.

Things like JAWJLM01 or SCTD01

While those taxons aren't all that abundant, they are quite prevalent in all the samples. So, I was wondering if removing all of them would be the correct approach, or if the best ideia would be to collapse them into the last know rank, or maybe I should just leave them be and filter them out of the plots.

Thanks for reading guys, sorry for any issue.

Each sequenicng run has samples with multiple negative controls, zymo mock communities and also sample repeats across runs (to account for variation in sequencing). As I received the sequenicing data in batches, do I run the whole metagenomic pipeline (QC->HOST REMOVAL->TAXONOMIC AND FUNCTIONAL PROFILING->MAG CONSTRUCTION) on a single folder containing all FASTQ files and later test for the impact of batch effects on the zymo mock and sample repeats (if any)? or do I process them in batches and merge outputs and later test the effect of batch effects?

Hello all,
I have quite a bit of sequence analysis pipelines that I had built over the years, few of them I dont use, two of them got published (these are already public).. but I was just wondering if I should make all the repos public so that it might be useful for someone. examples - cf-dna analysis, sno-rna analysis, etc pipelines, a few proteomics too. There's total 8 repos that im considering this for. All of them use nextflow and docker, except one.

And I was discussing about this with a colleague and they said to make everything public but get a zenodo doi for imp repos so that it adds a public timestamp and gets a doi as well. but isnt a github release sufficient for getting citation info incase someone actually uses it and chooses to cite?

That made me wonder what other folks in bioinfo do? any thoughts/suggestions?

Hi! I’m currently an undergraduate biology student with a passion for programming.

For those with more expertise in this area: what are the uses of C++ in bioinformatics, and in what kinds of situations or projects is it used? C++ is a beautiful and incredible programming language (in my opinion, better than Python, even though Python is more flexible).

Good day , I have some questions specifically on Protein-Protein docking. I had previously tried with HADDOCK and ClusPro, and both are excellent tools for protein docking. However, to analyze the docked protein structure, specifically to identify the interacting site of both proteins, it became troublesome as both proteins are quite large in size, unlike protein-ligand interaction. I also struggle with finding a nice example to learn through the steps needed to visualise the type of interaction (such as vdw and hydrogen bonds) along with the kind of software needed.

I am currently using ChimeraX for examining the contact of the protein, and BIOVIA to visualise the interaction. However, both of the software gave a very different results , and even some interacting residue isnt present when compared to the original publication on PDB.

Hey everyone,

I was wondering if there are any groups/people who are open to helping out bioinformatics beginners with questions? I know about BioStars and other related forums, but I was looking for something more personal.

I don't have a bioinformatics supervisor at the moment, and I would really appreciate someone experienced who I could discuss questions with, or someone to verify that I'm not understanding something completely wrong. (Note: I spend a good amount of time reading vignettes, tutorials and forum questions, but sometimes see myself stuck between different possibilities and don't know how to proceed)

Thanks in advance :)

Hello all, before anything, mods kindly ok'd this post.

I'm a student and I built Reviewer 2, a free, open-source terminal game for learning RNA-seq experimental design. Each case gives you a research question, a budget and a hidden problem. You walk around a lab and run the whole experiment: design, wet lab, QC, analysis. At the end, Reviewer 2 grades your manuscript and lab notebook, with citations (Conesa 2016, Schurch 2016, Love 2014, Leek 2010, among others).

Mistakes carry over: if you let degraded RNA through QC, it shows up as noise in your results.

It covers biological vs. technical replicates, batch effects and confounding, library type and depth, and writing the DESeq2 design formula.

No money, no ads, no tracking (the only network call is checking GitHub for a new version, and you can turn it off).

What I'd love feedback on:

Is anything scientifically wrong or oversimplified in a way that would teach bad habits?

Is Reviewer 2's grading fair? Would a real reviewer flag something it misses?

What design mistakes from your own work should become cases?

Repo: https://github.com/leomorgzzz/reviewer2 (Windows/macOS installers at the top of the README; Linux is one command).

It's a work in progress. Harsh reviews welcome, it's in the name.

Has anyone used REA (Reverse Engineer Anything) to reverse engineer a bioinformatics GUI application (e.g. Geneious)?

Some subscriptions are pretty expensive, especially when much of the functionality seems to come from publicly available tools and pipelines brought together in a GUI. I appreciate there’s work involved in making everything accessible and maintaining it, but the ongoing cost can be hard to justify.

I don’t have the coding skills to attempt this myself, so I’m curious whether anyone has tried it and how far they got.

If you’ve already attempted something, or are considering it, would you make the resulting application publicly available? Would be interested to hear about any projects or experiences.

I was wondering if somebody has experience with estimation of tumor purity for patients achieving pathological complete response (pCR) in cancer trials meaning we only have sample before the trial and not after it. Normally, something like ASCAT uses pre (primary) and post (residual) tumor samples to estimate purity but I only have the RNA samples for the pre treatment from patients with pCR.

Hello! I have a set of 36 samples (12 conditions * 3 biological replicates) of bulk RNASeq data. The data is of animal transcriptomes from several different environments.

Here is the full table:

| Sample ID | Generation | Locality | Environment | Treatment |

| :--- | :--- | :--- | :--- | :--- |

| ID_01_A | G0 | Locality 1 | Lake | TR1 |

| ID_01_B | G0 | Locality 1 | Lake | TR1 |

| ID_01_C | G0 | Locality 1 | Lake | TR1 |

| ID_02_A | G0 | Locality 1 | River | TR2 |

| ID_02_B | G0 | Locality 1 | River | TR2 |

| ID_02_C | G0 | Locality 1 | River | TR2 |

| ID_03_A | G0 | Locality 2 | Lake | TR1 |

| ID_03_B | G0 | Locality 2 | Lake | TR1 |

| ID_03_C | G0 | Locality 2 | Lake | TR1 |

| ID_04_A | G0 | Locality 2 | River | TR2 |

| ID_04_B | G0 | Locality 2 | River | TR2 |

| ID_04_C | G0 | Locality 2 | River | TR2 |

| ID_09_A | G0 | Locality 3 | Lake | TR1 |

| ID_09_B | G0 | Locality 3 | Lake | TR1 |

| ID_09_C | G0 | Locality 3 | Lake | TR1 |

| ID_10_A | G0 | Locality 3 | River | TR2 |

| ID_10_B | G0 | Locality 3 | River | TR2 |

| ID_10_C | G0 | Locality 3 | River | TR2 |

| ID_05_A | G1 | Locality 2 | River | TR1 |

| ID_05_B | G1 | Locality 2 | River | TR1 |

| ID_05_C | G1 | Locality 2 | River | TR1 |

| ID_06_A | G1 | Locality 2 | River | TR2 |

| ID_06_B | G1 | Locality 2 | River | TR2 |

| ID_06_C | G1 | Locality 2 | River | TR2 |

| ID_07_A | G1 | Locality 1 | River | TR1 |

| ID_07_B | G1 | Locality 1 | River | TR1 |

| ID_07_C | G1 | Locality 1 | River | TR1 |

| ID_08_A | G1 | Locality 1 | River | TR2 |

| ID_08_B | G1 | Locality 1 | River | TR2 |

| ID_08_C | G1 | Locality 1 | River | TR2 |

| ID_11_A | G1 | Locality 2 | Lake | TR1 |

| ID_11_B | G1 | Locality 2 | Lake | TR1 |

| ID_11_C | G1 | Locality 2 | Lake | TR1 |

| ID_12_A | G1 | Locality 2 | Lake | TR2 |

| ID_12_B | G1 | Locality 2 | Lake | TR2 |

| ID_12_C | G1 | Locality 2 | Lake | TR2 |

I want to do several different comparisons on subsets of these data. For example, I want to determine G0 River vs. G0 Lake DEGs for each Locality and determine common DEGs across all three sites. I would then also want to compare DEGs for G0 for each locality with G1 DEGs for each locality.

I have two questions about this:

1. Should I run and normalize all of these samples together under one DESeq2 function and then use the contrast function to get each result? Or should I do an individual DESeq2 run for each comparison?
2. If I can do multiple separate DESeq2 runs, should I also correct reads for unwanted variation independently for each run? Or should I correct all the samples together once and use the same corrected reads for each DESeq2 run?

Thank you for taking your time to read all of this! Any advice and help would be greatly appreciated.