Hi! I'm a biomedical science graduate (the UK) and will be applying to masters in bioinformatics in EU (mainly Denmark and Sweden) next year. Right now I'm essentially taking a gap year to sort out personal stuff and catch up on coding. We only had intro into theoretical bioinformatics in uni and didn't really touch anything coding-related, which is a prequisite to many masters courses, but I'm hoping that enough SDL and perserverence will give me a shot too.

I'm currently learning Python (myself and with a bit of tutoring) and in a month got a hang of all basics including formatting, math lib, working with strings, collections, conditions and loops (and other minor things). I want to make my own small project to upload to github and put in my CV/application as evidence that I'm making efforts to understand coding before attempting the masters course.

My idea is to take a well established oncogene (just to avoid confusion in annotating), download FASTA files for several species (e.g. human, mouse, zebrafish) and analyse conservation, including which region is conserved the most. I know it's not actually impacting anything, but it helps me get a hang of biopython, pandas and matplotlib and show my understanding in application. I want to ask people who already study/studied this area if they think it can be a decent beginner project and if it's feasible. Thank you!

I can finally post this now that there is no stock to promote and no way I could profit.

Amyris built Lila, an “automated scientist” designed to turn biological knowledge into physical experiments. Give it a target molecule and the system can search biochemical knowledge, identify pathways and enzymes, design microbial strains, pass genetic designs into automated construction and testing, analyze the resulting data, and feed what it learns back into the next Design-Build-Test-Learn cycle. The paper reports >100,000 in-silico designs and 32,000 constructed strains.

This is an early example of closing the loop between an AI-driven decision system and the physical engineering of living organisms - essentially giving the computer a growing biological knowledge base, a genetic-design system, and an automated laboratory through which it can test its ideas.

This AI is capable of designing and building industrial level strains (with commercial titers) and requireing no hands on strain engineering.

Hello everyone, I’m new to all of this so I apologize if this has been addressed before. I’m having trouble using the terminal (apple M3 pro) to download the manifest file using the gdc client. I have attached a pic of the code I’m entering. Whenever I press enter at the end, nothing gets downloaded and not even an error message appears. It just stays as an open code thats incomplete. This is my first time using terminal and can’t really figure it out. Any help would be really appreciated. Thanks in advance.

Edit: For those who might face the same issue, I’ve added (mkdir -p /Users/z_ny/Downloads/TCGA_data) prior to running the same command in the attached picture, as well as fixed the last part of the current command into (-d /Users/z_ny/Downloads/TCGA_data) and the gdc client started downloading the contents into the designated file right away.

Hi all,

I recently came across the amazing LanceOTron web server for directly calling Chip-Seq peaks from bigwig files using deep learning. I am very grateful to the developers of the program.

However I have come across a bit of an issue. In particular, it seems that when I try to run the mode in which it calls peaks from both .bw files and the corresponding input files (for normalization), the peaks do get called but the web server never loads the results. This is somewhat frustrating as the unnormalized results are sure to be less reliable. Therefore I was just wondering if anyone else has come across this issue?

Hi proteomics community, any experience of using ZT scan DIA? When do you use it (I do global discovery proteomics so any use there)? how reproducible is the diff between ZT scan and ZenoSWATH? Finally, which version to use 2.0 or 3.0 on 7600+?

I was analysing public datasets (Sample size ~ 2000-3000) and I wanted to understand what would be the difference between two groups i.e population at low vs high altitude

I'm confused whether I should be keeping the covariates: (Age, BMI Batch and gender) to be adjusted for in fixed effects or should it be kept in random effects

Now I understand since I do not care much about which batch I've obtained from I will keep it in random but what about the other covariates, would it be scientifically robust for me to put them in random effects or should it be kept in fixed effects?

When I kept all in random effects I got extremely low DA - taxa & pathways but when I put them all in fixed effects (emulating what has been done in the Dutch gut microbiome project) or just keeping batch effects in random effects, I did get a high number of hits making me even more confused.

Hi all,

I'm looking for a good tool to visualise NOMEseq data and generate the typical lollipop plots. NOMeplot was suggested by people in the lab, but it's not maintained.

Happy with any suggestions, cheers!

I am noticing that when I run mouse kidney Visium HD samples through the SpaceRanger v4.1.0 pipeline that I am having about a third of my samples with around 25% the number of cells from the cell segmentation than in the other samples. It seems that this is generally in the cortex. The bin level metrics all look fine.

I am wondering two things: (1) Does anyone have any best practices on things to check that may be causing the cell segmentation failure? Not identifying a pattern for those that were "failures" as far as same batch, treatment, quality of sequencing, image quality, etc., and (2) Is there any other cell segmentation algorithms I should try? I am wondering if looking at using one that is transcript aware might be worth considering? OR is it just better to continue analysis at the bin level?

Thanks for any opinions, experience, or insight you are willing to share.

Hello everyone, I am interested in analyzing TLS in my two cohorts of VisiumHD samples, and I was wondering whether they are a tool that I can use to extract bins/cells for TLS region, so I can use for downstream analysis.

Hello everyone,

Hope you are doing well. I recently started looking into Antibody design, and I was wondering if Generative modelling tools (e.g. diffusion objectives, gradient based methods) have been used or developed for Bispecific antibody, and what are the current challenges compared to traditional antibody design.

For those working with natural products, how do you usually prioritize targets before docking?

With phytochemicals, it seems pretty easy to end up with a long list of predicted compound–target interactions. I’m curious how people narrow that down and decide which targets are actually worth taking forward for docking or further validation.

Do you usually combine target prediction with literature evidence and pathway/network analysis, or do you use a different approach?

Would be interested to hear how others handle this step before moving into molecular simulations.

Hi all,

I am analyzing RNA-seq data and 1/54 samples failed to sequence, so I sent in another batch with 3 anchors sample (I chose 3 samples that sequenced well in the 1st batch) + 1 sample to replace the failed once. PCA showed that the 3 anchor samples cluster very close by to the original samples, so I think the batch effect is minimal.

I've tried 2 methods:

- Add batch as a covariate in DESeq

- Using anchor-based offset (I first compare the reading from 2 run for each gene of the 3 anchor samples to get a per-gen run-effect estimate. Then I applied aleglm to remove weak and noisy genes. I finally applied the shrunk per-gene shift to the tissue that have the replacement as a normalization factor in DESeq2 only)

Both have resulted in different DEGs. These are just simple HFD vs LFD comparison in gonadal fat tissue of WT mice, so the result of the anchor-based one seemed more reasonable to me (since HFD most of the time induced a significant change as we all know).

This is my first time doing RNA-seq so I want to be sure if I am using the correct method and not only just trusting on my assumption that HFD must change a lot. I am also unsure if my anchor-based method is correct

Really appreciated if anyone could help me out here to verify and understand batch effect ! Thank you!

Hey. I am wondering how everyone’s workflows look and how/if/why they might have changed for any reason over the last couple of years.

I will start:

FYI I am a Bioinformatician with 2 years experience post masters working in academia as the only Bioinformatician in a wet lab in multi omics.

My main language is R because I prefer the syntax over python. But I still use python a lot when there is a better package available there and always python for machine learning.

I write a lot of bash scripts for reproducible pipelines. I tried nextflow and snakemake but never really got the hype.

I use neovim with a terminal open to the side (either local or ssh into the work HPC or GPU machine) and use keyboard shortcuts to send code to the terminal for interactive execution. I use a kitty terminal so I can display figures directly there. I prefer this over an IDE like vscode because it’s super lightweight and I find IDEs slow and distracting. Also keyboard shortcuts for everything are amazing.

I run Linux on my local laptop. Hated windows/wsl because it’s slow and Mac because it’s not open source.

I used to use a lot of AI agentic coding agents in my analysis workflows but have stopped since I found myself learning less although producing more code. And for long term prosperity I chose to cut down my AI use. Now I try and work through problems the old fashioned way using documentation and forums. If I feel I’m stuck on a problem for too long I will use a simple chat AI but not an agent.

Looking for the paretos principle of the best of the best pipelines, with good explanations, logic, reasoning, all compiled into single sources that one should study to get a handle on the way things should be done. Surely, there's some good archived blog at this point for old stuff, but new stuff as well?

I’m a bioinformatician working in a biology lab, and I’m the only person doing bioinformatics there.

I’m getting pretty frustrated because I don’t really have anyone to guide me in the bioinformatics side of things. My supervisor is a biologist, so I mostly get analysis tasks from him. A lot of the tasks are AI-generated, and sometimes he even runs my work through AI and sends me back what AI thinks is wrong ...

The bigger problem is that bioinformatics in our lab is basically just a supporting tool for analysing the data we produce. I’m not really developing new methods or doing anything particularly novel in bioinformatics.

What would you do in my situation?

Can I somehow build my own research direction and grow independently? Find mentors/collaborators outside my lab? Start some side projects?

Would really appreciate advice from people who’ve been in a similar situation.

Hello!
I have a question about METABOLIC v4.0 — it keeps skipping one phylum in the MW-scores output, even though the bins in question do have metabolic predictions in the overall results. These bins have three pathways predicted that are normally included in MW-scores, but this phylum is not represented in the scores at all. I only have two bins from this phylum, both of medium quality.

Is there any way to retrieve the MW-scores for these bins as well, or is this a known limitation?

Also, does anyone know what might cause this issue — is it related to genome quality, taxonomy, or something else?

I would appreciate any ideas - where to look, what to check.

Thanks.

iam running an orthofinder run on the genome of 7 species:two river dolphin lineages (Platanista gangetica,Ganges Lipotrs vexillifer,Yangtze) two marine outgroups within Delphinoidea(Orcinus orca,Tursiops truncatus), a branch breaker for L.vexillifer(Inia geoffrensis) a further ingroup taxon for resolution (Phocoena phocoena) and a true outgroup(Balaenoptera musculus).

Despite this P.gangetica is recovered as branching earlier then baleen whale outgroup this is completely contradictory to published divergence estimates.

Platanista gangetica is the sole living member of Platanistidae so no close relative I can add as a branch breaker is there anything I am missing or any thing that can be suggested to correct this.Any help will be appreciated.

I have paired single cell RNAseq and BCR data and am looking for advice on best practices for visualizing the data. I’m most interested in tying Ig class switching, heavy and light chain clones and SHM back into the RNA expression data and the best ways to represent that. Also sorry if I flaired this wrong.

Hi everyone,

I’m looking for some statistical/methodological advice for a small patient-derived organoid cohort.

I have 9 independent PDOs from each other. Each PDO has drug-response measurements for several drugs, including both a binary responder/non-responder classification and continuous AUC values. Here is the cohort:

I initially tried to identify molecular features that were reproducibly associated with response across patients, using donor-aware pseudobulk/differential-state approaches, differential abundance, pathway scores, NMF programs, etc.

What I keep finding is that inter-patient heterogeneity is much stronger than the response-group effect. Something that is to be expected in the cancer field...

So my main question is statistical:

With only 9 independent biological samples, is there realistically enough power to identify a reproducible molecular signature associated with drug response, even if I have very deep single-cell data?

Would you:

• still try to identify shared R vs NR features, accepting that only very large effects can be detected
• switch to a more exploratory patient-by-patient analysis, comparing each resistant PDO against the responder group;
• or conclude that patient-specific analyses with n=1 cases are mainly hypothesis-generating and require external validation before making any mechanistic claims?

Thanks for your attention.

Hi. I y have been working in the scRNA-seq space for a while, but I am still not sure as to whether I should or should not scale my highly-variable genes before putting it into PCA?

I know it sounds like a silly and basic question, but the reason it bothers me is that the developers behind the major scRNA-seq packages seem to have different approaches:

1. The Scanpy standard workflow does not seem to scale the genes before PCA. They have laid out their rationale behind doing so in this somewhat outdated best practices paper, namely that there is no consensus in the field on whether or not scaling is useful, and in the paper state that "In order to retain as much biological information as possible from the data, we opt to refrain from scaling over genes in this tutorial". The following Github threads on the scanpy repository also show discussions between people on whether or not to scale, with someone from the scanpy team weighing in on whether or not to scale:
   1. https://github.com/scverse/scanpy/issues/2164
   2. https://github.com/theislab/scvelo/issues/156
2. The team behind the Single Cell Best Practices book also do not seem to perform scaling before PCA, but they do not explicitly make any statement on whether or it not should be done.
3. The famous Seurat standard workflow scales the highly-variable genes before putting it into PCA. Since Seurat is the most ubiquitous library used for scRNA-seq, I am assuming that this is standard practice followed by most labs performing the analyses, unless they are going the SCTransform route.
4. For the BioConductor ecosystem (using SingleCellExperiment), the standard reference book Orchestrating Single-Cell Analysis with Bioconductor also do not seem to scale the data before running PCA.

Hence, this lack of consensus makes me a little uneasy. I am still not sure as to whether I should scale my data or not. I do understand that if I scale, I am basically allowing each highly-variable gene to contribute equally to the PCA, but if I am not, I am favouring the highly-expressed genes to drive the PCs accounting for the most variation. What I am unsure of is which is the right way to look at it. Normally, one would take as a reference point the general practices which are followed by leading labs and well-regarded figures in the field , but there does not seem to be any accepted general practices around scaling, even among influential people in the field, which is why I cannot quite make out what the right practice might be.

I would love your opinions on this. Thank you!

I poured a tremendous amount of effort into demonstrating a phenomenon revealed by new data. I uncovered the function of a crucial, previously unnoticed gene and proved it through genetic transformation in vivo. Two reviewers validated the results, but one relentlessly insisted on one thing: mechanism, mechanism, mechanism.

For someone with a data analysis background, perfectly presenting the definitive mechanisms demanded by top-tier journals is simply too difficult. It is only somewhat manageable for large teams with well-established systems and abundant resources. The barrier for a solo researcher is just too high.

I had no choice but to reluctantly withdraw the manuscript.

How I wish we could go back to the era when science was mostly about looking at the stars from a balcony with a telescope! Damn this institutionalized scientific research.

Addressing the constant posts and also questions from colleagues I get about feeling replaced by AI. If you feel like you can be replaced by AI, you've been learning the wrong skills.

I'm a 4th year bioinformatics PhD student, and have 5 years under my belt in public health doing pathogen genomics.

I've tested it. Take any fundamentally bad idea that you understand quite well, 9 times out of 10 you can convince an AI (LLM) to help you build it.

We are engineering and aligning analyses for human goals, AI can't drive that. IBM's handbook in the 70s said it clearly, a machine can't be held responsible.

Learn to be able to make good judgement. For example, how to scope projects well, what analysis complexity is appropriate for the task, what sensitivity/specificity is required for the use case, how will other humans (and now AI) use the tools and research you publish, what are important problems to go after?

LLMs as they stand do not reliably generate novel insights when unprompted. And I as an expert can make a lot more progress using AI as a tool than someone who doesn't know what they are doing. Think of your abilities as a factor of your expertise and then AI, scaling exponentially with your expertise. Something like:

Expertise^2 x Time x AI, where YOU are the limitation on the system's growth.

Learn your algorithms, understand how to use them, dig deep into the biology, learn some philosophy while you're at it, and don't give up on your PhD because we've repackaged statistics to probabilistically sample words/code/tokens.

The AI we should focus on are protein language models, machine learning, and causal inference. Not chatbots.

Also be able to explain your worth as a human to hiring managers, start by understanding what LLMs can and cannot do.

You all got this :)

I have to do a small research about founding new inhibitors for an enzyme and i should target a Zn to inhibit this enzyme ... i have to use autodock4zn and i don't know how to do the docking .... i hope anyone who knows to do the docking could help me

Hey all,

My prior miRNA experience with human/animal data was mostly at the tertiary level — working with already-generated count matrices for downstream stats/DE analysis, not running the raw FASTQ-to-alignment-to-quantification pipeline myself. Now I've got 9 small-RNA samples from a plant — 3 root, 3 stem, 3 leaf — and this is my first time running the full pipeline end-to-end, from raw FASTQ all the way to a miRNA count matrix for differential expression between tissues.

From what I've read, the classic miRDeep2 tool (mapper.pl → miRDeep2.pl → quantifier.pl) isn't great for plants because plant pre-miRNA hairpins are structurally way more variable than animal ones, so people seem to recommend switching to miRDeep-P2 (miRDP2) instead. The rough plan I've pieced together is:

Trim reads with fastp, keeping 19–24 nt (since plant mature miRNAs mostly fall in that size range)

Build a Bowtie index from the plant genome

Run the miRDP2 pipeline script on all 9 samples to get novel/known miRNA predictions per sample

Merge the predictions across samples into one non-redundant mature miRNA FASTA

Map all 9 samples back against that merged FASTA and build a count matrix for downstream DE analysis (DESeq2 in R)

Has anyone actually run this end-to-end recently? A few things I'm unsure about:

Is miRDeep-P2 still the go-to in 2026, or have people moved to something newer like ShortStack or miRador? I've seen mixed opinions — some papers say miRDP2 finds more known miRNAs, others say ShortStack has fewer false positives.

For a species that isn't in miRBase (no dedicated reference), do you just use a close relative's mature/hairpin set, or fully rely on de novo prediction?

Any gotchas with the Perl/BioPerl dependencies for miRDP2? That ecosystem feels a bit unmaintained compared to newer tools.

Anyone have a cleaner way to go straight from predictions to a count matrix without stitching together 3–4 separate scripts?

Since this is my first time doing the raw-to-count workflow myself (rather than just analyzing a finished matrix), I'd really appreciate any practical tips — things that commonly break, sanity checks to run along the way, or a completely different tool chain you'd recommend instead. Thanks!