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).

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.

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.

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.

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.

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.

Hi,

A paper I'm coauthoring (but I'm not the main writer) has been approved and I was doing some final checks before publication. Then I realized that, somehow, we had a weird reference to R:

Wilson A, Norden N. The R Project for Statistical Computing The R Project for Statistical Computing. URL Http//Www r-Project Org/254 2015;3:1–9.

Clearly, this is not R's preferred citation (and the one I added to the word document)

R Core Team (2026). _R: A Language and Environment for Statistical Computing_.
R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

Checking back previous versions of the document, this reference change happened somewhere between November 2025 and January 2026. I don't know how or why my coauthor's Mendeley or Copilot sharted this reference.

However, digging deeper into this, it seems to be a pretty common issue, with plenty of papers using that citation, going back at least to 2022:

https://www.google.com/search?q=%22Wilson+A%22+%22Norden+N%22

Yet, what's more, there are no Wilson nor Norden among R's contributors: https://www.r-project.org/contributors.html

What the hell is this reference, and why does it show up in reference managers?

The story is that because policies of a scholarship I won, I ended up in a lab that was not in my preferences for Msc. The lab is good and has a really good professor, is just that it was not what I wanted. It focuses on enzyme expression, characterization, etc. with either applied perspectives or to unveil their role in certain physiological process.

My background is biochemistry and I've worked with this lab's workflow before. But also, I have been learning bioinformatics from around 2 years by myself, doing a 6-month course and joining bioinfo projects on my old lab. I did things like general genomics (genome assembly, genome mapping, dada2, identification of new species from enviromental samples) and a bit of structural.

My professor is very open about including my bioinformatic "side skills" into my thesis, so I'm thinking of experiments that would fit a wet-lab and dry-lab approach.

The project will be about expression-characterization of an enzyme with a suspected role on oxidative stress resistance in a bacteria with not much genomic work done before. I've thought of pangenome, ALE, and also characterize the genomic context around that protein. Any ideas? Thanks!

Hello everyone, I’ve been trying to find an answer to this question online but to no avail, as most examples online as shown for FASTq files. I currently have the bam files for my RNA-seq samples, along with the index (bam.bai) files for them. Do I submit the bam.bai files as raw files along with the BAM files, or as supplementary files instead?

Thank you very much in advance for the help.

Hi all,

I am analyzing scATAC-seq data to see if there is any difference in chromatin accessibility in the promoters of certain genes of interest in controls vs disease patients. My hypothesis is that a certain transcription factor acts as a repressor under homeostatic conditions, and there is de-repression of this TF in active disease.

I do have a bit of a conceptual confusion I was hoping to clear up - I understand that even if this hypothesis is true there may not be any significant difference in chromatin accessibility at the relevant loci; however, if there were to be a difference, in which direction would it be expected (more accessible in cases or more in controls)? I can see arguments either way I think, as you would expect the chromatin to be open in controls in order for the repressor to be able to bind there, if that makes sense? Or perhaps I am not thinking about this correctly?

I am using google colabfold for predicting my 936 amino acid long protein, but whenever I run it, its showing indexerror: list out of range. I have even tried to reduce the max_msa to 16:32 but its still showing the same error. Anybody has any idea on how to fix it?

Hi everyone, I am submitting RNA-seq data to GEO for the first time. Based on the understanding, processed data only includes FASTQ files and gene count matrices, and not BAM files. Hence, do I submit BAM files as supplementary data in my submission?

Thank you.

I’m new to bioinformatics and am doing some background readings on approaches for single cell analysis. From what I’ve looked at some packages offered in Python purport to do a better job of certain tasks than the ones offered in R. Is it advisable to switch between R and Python for different steps in an analysis pipeline? Thanks for any advice!

Hi everyone, I am currently looking for a RNA-seq data repository that provides an accession number instantly after file submission, as the journal (Cell Stem Cell) that my team submitted a paper to is asking us to provide the accession number for our RNA-seq data in 7 days (we currently only have 5 days left) and GEO only gives an accession number within 5 days of submission.

Is there an alternative repository that I could use for now? Based on google searches and the journal’s requirements, it seems that I can use ArrayExpress.

Thank you.

When I got into spatial transcriptomics, it was hard to tell which tool fits which step, so I put together the list I wish I'd had:

https://github.com/wrab12/awesome-spatial-transcriptomics

There are already good lists (awesome-single-cell, awesome_spatial_omics and others, linked at the bottom of mine). This one tries a few different things:

• Ordered like an analysis: technologies, segmentation, spatially variable genes, domains, deconvolution, alignment and 3D, cell–cell communication, super-resolution, spatiotemporal modeling, visualization.
• Single-cell tools included, since spatial pipelines lean on them for QC, integration, annotation and trajectories.
• Recent work: tools for Visium HD and Xenium, single-cell and spatial foundation models, LLM agents, and benchmark papers.
• Every tool row has the language, a paper link and a live GitHub star badge. Tables are sorted by stars, but stars measure popularity, not quality, so the benchmarks are linked up front.

How I checked it: all 278 GitHub repos were verified through the GitHub API (exists, not archived, canonical name), every DOI was matched to its paper title via Crossref or DataCite, and the remaining links were checked too.

Disclosure: it's my repo (CC0), and one entry, GenOT, is my own paper. It's labelled as such in the list.

I'd really like feedback on:

1. Tools you actually use that are missing, or ones that should go
2. Benchmarks you trust (or don't)
3. Whether the section order matches how you work

Issues and PRs are welcome. Thanks!

Hi everyone,
I am looking for the original human piRNABank reference files for a legacy small-RNA analysis.

I tried to download the file using this link: http://pirnabank.ibab.ac.in/downloads/all/human_all.zip

But I have been receiving this error form the server site: 503 Service Unavailable It looks like the server is no longer in service and the domain has gone offline.

I am specifically looking for the original human piRNABank sequence/reference file, ideally the original human_all.zip, human_pir.txt.

Does anyone know of: an archived copy of the original piRNABank human database, a reliable mirror or repository containing human_all.zip or human_pir.txt

Can u guys tell me how to get going with spatial transcriptomics and single cell transcriptomics from raw data and by raw data i mean from publicly available sra/fastq files for self learning.

I would appreciate it if u can suggest something related to the gut microbiome and ageing.

I’m a cs undergrad currently working with Evo 2 and trying to establish if theres a meaningful research gap around biological validity of generated sequences.
But I’m struggling to find a benchmark that will prove this. I initially thought comparing the generated sequences to real ones will be enough. Turns out it isn’t , since alot of the sequences dont have to look like real ones to be biologically viable.
Is this a gap worth pursuing ? I have limited resources and cannot do wet lab testing so is there anyway to prove that this is a real gap?

Curious to hear what the community thinks of this article, which primarily discusses Anthropic's recent "discovery" of a supposedly CRISPR-like enzyme system that I'm sure we've all heard about. Personally this strikes me as a pretty balanced, rational take towards AI as a tool rather than an apocalyptic job destroyer. However, it's still unclear to me why we needed a thousand Claude agents and millions of dollars of compute for what ultimately strikes me as a regex style pattern search through genomic data. I'm also surprised that I haven't heard anyone discuss the fact that setting thousands of agents loose on terabytes of sequencing data with the instructions to "find some interesting patterns" is essentially a massive multiple comparisons problem that is bound to turn up some spurious patterns with no biological significance.

Hello everyone,

I recently received visiumHD samples with low quality, and I had to use a tool called SuperSpot to aggregate multiple bins and then annotate with a reference dataset using RCTD, since when aggregating multiple bins, the result meta spot will usually contains different cells, I was wondering if they are any tools that could take advantage of the format and include possible group comparison.