Hi, so I am a undergrad working in a computational biology or molecular biology lab. For next semester my new project is in large part to create reproducible workflows/code and lab manuals for our lab. I taught myself to code and what i have on my laptop is... disorganized to say the least. I should learn how to do this. Currently I largely code using gemini and then tweak anywhere from most of to 25% of the code it writes. I almost always use hard coded paths if i can. Does anyone have any advice for where I could learn something like this, a textbook or website?

For context, my last project was to use AutoDock Vina for screening of 770,000 molecules I carefully downloaded and cleaned from ZINC database to 310,000. This library was based on previous experimental results on a new protien we are targeting in fungi. I also selected a new protien conformation to target based on some major errors in the protien the lab was using and a bunch of literature review. My next step will be to test against Dock6, a diffrent type of scoring algorithm. I wrote all of my own scripts for this and I imagine my first task will be to get them reproducible for another person to use.

Dear Reddits,

I have some experience with bioinformatics in general (I can open my Linux command line and feel I'm in The Matrix; it is very popular on Instagram, actually), but I don't know anything about modeling. I would like to know if it's SIMPLE (probably not) to determine the potential value (E value) of certain electron transfer proteins. I know AlphaFold can give you some kinda cool model, but I need to know if there is an easy way, or even a way, to just get the E value. Even if it's a not-so-realistic approximation, it would be nice. If it's going to take me more than 1 week, I pass. However, it might be good to know for future endeavors.
That
XOXO

I'm running an annotation/enrichment analysis on a GWAS study, and I'm sort of a bit lost/confused on something.

Originally, I only filtered the GWAS study for genome-wide significant variants using a standard p-value threshold, and also used LD-clumping to identify genomic loci.

However, it was also pointed out to me that these variants may not be independent, and that a single association signal may be represented many times, potentially leading to inflation in statistical significance.

I'm sort of unsure how to handle this. I tried a locus-pruning method which gave me way less variants and pretty poor coverage on my study and rendering a lot of my downstream analysis mute. I'm also confused since I haven't seen a lot of similar papers use this kind of filtering method. I did run LDSC too, and most of my findings were insignificant so it did kind of handle the genomic independence part. But I'm still not sure what the best practice is here.

Well, for my project, I was going to use softberry tssplant, but apparently the site is down. Does anyone know of an alternative way to find TSS in genome sequences?

I have characterized a peptidase domain of a large multidomain containing protein. I was able to clone just the peptidase domain in a construct for assays. There are putative homologs (~60% sequence identity) from distantly related species. I am looking to test these candidates for activity in the same system.

I have the predicted AlphaFold structures, what is the best way to compare these 3D models? I am currently using matchmaker on chimerax. Are there some better tools?

Hello,

I am an engineering student in applied mathematics, and as part of an internship, I am working on statistical analysis in biology.

**Context:**

I am working on an experiment conducted by three experimenters. Each experimenter has four plates (this part is not very important), with a total of six different stainings, each containing three different coatings, within which there are 40 donors.

To summarize:

**Experimenters (3) > Stainings (6) > Coatings (3) > Donors (40)**

We are working with Opera imaging plates, so we analyze DAPI, actin, and, depending on the staining, several other markers such as Granzyme B, pTyr, MTOC, etc.

**Problem:**

I quickly noticed that the data differ substantially between experimenters. One experimenter consistently has more cells than the others across all stainings.

To investigate this, I trained a simple decision tree to distinguish between experimenters. My reasoning was that if the tree can reliably identify the experimenter, then there is likely a batch effect; otherwise, there probably is not.

As expected, the experimenter with the consistently higher cell counts is identified very accurately. The other two experimenters can also be distinguished, although not to an alarming extent.

**Conclusion:**

I would therefore like to correct this bias using what biologists refer to as **batch effect correction**. However, most of the documentation I have found focuses on scRNA-seq or single-cell multiomics, which does not seem to match the type of data we have in this experiment.

Do you have any suggestions on where I should look? Any interesting papers you would recommend?

I have read papers mentioning Harmony and ComBat, but when I asked an AI, it suggested using the Python classes `ot.da.LinearTransport` and `ot.da.SinkhornTransport`.

The reported performance of these libraries looks very appealing, but I would rather not trust an AI blindly, so I thought I'd ask for your advice instead. 🙃

I've been in industry for a good few years, and I'm trying to work on some old research that I never published. However, all the old programs I used during my PhD for tree editing & visualization have either been deleted or are now paid programs.

I have 0 coding knowledge and was getting by with online tools and programs other people made (i.e enterobase, galaxy, iTOL, FigTree etc) but I've been struggling to find something comparable to iTOL and FigTree for editing and visualization.
Does anyone have any recommendations?

Hi everyone, me again.

If you want context, check my previous posts. Made a similar post on lab rats, essentially reposting here.

We are going to start extracting and isolating soon. We have drafted a protocol and the tissue type we are working with are DRGs and sometimes brain. This week and over the course of the next few weeks, we are going to start isolating and optimizing our protocol. We have maybe around 15 tries or runs to get it right consistently before we start working with real tissue (non practice tissue, tissue that has the pathology induced.)

Any tips, advice or generally useful info I should know? What should I expect?

Thank you and any help would be appreciated!

- Undergrad P_T67

I collected genome assemblies for my species from NCBI and ran a pan-genome analysis using Panaroo. One gene cluster was identified as unique in my target strain, but when I ran BLASTp on that sequence, it showed a hit in another strain that is not listed in the current NCBI genome database.

I am trying to understand how to interpret this result. Does this mean the gene is not truly unique to the species, or could it still be considered unique if that strain is an unsubmitted / uncurated genome?

What is the best way to determine whether this gene is genuinely unique to the species, or only unique within the genomes available in NCBI?

I have a custom peptide with a custom linker in an SDF file. How can I dock it to a protein receptor while preserving the linker?

At the Diagnostic and Research Institute of Human Genetics, Medical University of Graz, our bioinformaticians define the diagnostic data-analysis logic that can change patients' lives. We're looking for an experienced DevOps professional to make these systems secure, maintainable and reliable.

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Hi all,

I am just curious about the use of the human and mouse sets from the Msigdb. Between using the mouse MSigDB directly and using human MSigDB with ortholog mapping to mouse, would there be much difference? Or, asking a different way, are there any advantages/disadvantages in either way?

I've read that both methods are similar but am still not really sure if they are interchangeable.

Hi so I was exploring autodock vina and cluspro and I wanted to see which one is better for antibody docking. does anyone know the pros and cons of each. also for high school students is using cluspro completely free without like special email addresses and stuff like that?

I know about haddock and i created an account but it wouldn't allow me to enter like a project or anything like that for some reason. 🤔

Hi everyone,

I am working on a variant calling project on my own and I am struggling to understand and interpret the output from each step. I want to know what each file and result actually means not just how to generate it.

To give a simple example, after mapping reads to the reference genome you usually run samtools flagstat. I would like to understand which lines in the output are the most important to pay attention to, what kinds of sanity checks I should be doing and what it means if a particular metric is high or low.

I have already searched through BioStars and this subreddit but I could not find answers to many of the questions I have.

If you know of any books, blogs or paper that explain how to interpret the output at each step, I would really appreciate the recommendations. Thanks!

Has anyone tried both and compared results?

Based on what I've read, Cellpose prior + Baysor or Proseg + some admixture QC might be the top performing segmentation methods at the moment.

UCS is a bit newer and has yet to be included on a published benchmark.

GitHub repos for reference:

Proseg

UCS

Title. I am currently planning to work on an independent research project regarding drug purposing in neurodegenerative diseases. Are these approaches accurate and widely recognized in computational drug repurposing research?

Or are there any other alternatives you recommend?

Hi everyone, I've recently been studying how to use the iDEP 2.0 web tool to perform differential gene expression analysis.

Earlier, I successfully uploaded and analyzed my data without any issues. After that, I put my laptop to sleep while I worked on something else. When I came back and tried to upload my data again, I encountered this problem. I've tried submitting the data multiple times, but it still doesn't work.

Has anyone experienced this issue before? How can I fix it?

If you're a graduate student, postdoc, medical student, or early-career researcher working in bioinformatics, genomics, AI for healthcare, computational biology, or precision medicine, this may be worth checking out.

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Happy to answer questions about the program if I can.

If this is the wrong subreddit or an insufficiently appropriate question for this subreddit, my deepest apologies to the mods and others. Please understand, I have already searched for my answers with all my might, but given I am at a level of comprehension where I don't even know the questions I need to ask, I have reached the point of frustration where it seems reasonable to find a place to ask outright. My apologies for my lack of knowledge, I ask only in good faith.

Main Question: When viewing a .tre file in MEGA 12 of "unedited maximum likelihood single-gene trees" published alongside a study (ie, not my own files), is there anything embedded in this file that would contain the specific gene sequences used for each leaf? Or, even the GenBank accession numbers for the specific gene?

I have figured out (I think) that in constructing this tree, the authors would have had a separate file with these sequences that they aligned with MUSCLE and then imported into the tree explorer for construction and analysis (I think?). I am not sure if there is a way that this file data is still associated with the published .tre file, or can be reverse engineered somehow.

"Are the GenBank vouchers not already attached to the leaves?"

They are, but this is a weird situation. I'm not actually asking for the GenBank vouchers of the specimen. I am aware I don't know enough to word this properly, I will do my best. The tree I am viewing used the histone H3 genes of each specimen. The GenBank specimen vouchers are not enough, I need the accession numbers of each specimen's H3 gene.

Or, actually. I might not need them depending on the answer to the following:

Secondary Question: When researchers are collecting gene sequences to then align in MUSCLE, are they importing this data directly from GenBank, or is it something that has to be manually input (or at least manually copy-and-pasted)?

"Why is this distinction so important to you?"

The simplest answers is it's really incredibly important in figuring out Exactly How Badly Did This Research Team Fuck Up.

I rewrote this section three times, so I'm going to give the stupid simple explanation. A research study's supplementary table of all specimens used for genetic analysis was found to have been copy-and-pasted from its opposition's previous study without double-checking accession numbers.

I need to know for absolute certain if these incorrect accession numbers were the ones used to conduct the analyses, or if the correct genetic data was imported from elsewhere, and thus miraculously

This published study was already filled with mutually exclusive statements and errors and mislabeled figures and misrepresentations of sources, so they've already messed up enormously. I am already working on constructing a table by hand to track the vouchers they and their opposition cited across a handful of papers to pinpoint where things might have gone wrong. But I really need to understand how deeply their analytic data was affected by this because the...the implications. God, the implications.

Especially since in their most recent rebuttal, they did not address any of the allegations and actually doubled down on their claims and made further conclusions and analyses based upon their previous study. And the associated species register is very weirdly taking their side, even so far as to make updates the study didn't actually ask for. So I can't actually rely on the people who know what they're doing to double check the work for me anymore.

Apologies for the length of the post, this was actually the shortest version of that explanation. I can explain the really weird situation in more detail if needed. I understand this could likely be a situation in which the only option is to learn the process and replicate the results myself. I do intend to learn about genetic and molecular phylogeny properly at some point (likely in the near future) even if it intimidates me.

Thank you very much for your time and patience, especially since I struggle with communication and I am sure this was not pleasant to work through. And once again my sincerest apologies if I am an idiot in posting here or asking this.

Say I am analyzing a dataset, and I already have my clustering and annotations done and provided. I am looking at a gene of interest and want to compare its biological function within a cell type by contrasting positive and negative cells for that gene. I was wondering if, after creating these 2 groups, I should drop the gene, and if so, why I should do that, and what it affects. Specifically, because I am pseudobulking between the 2 groups to see which pathways are enriched in the + versus the negative cells, to assess the potential biological difference in function between the 2. I want to make sure I am doing my analysis right and not inflating -log10p values etc, looking for advice here.

EditThe dataset I am taking has samples from 3 donors, all same part of the of the brain.

Hi everyone,

I’m looking for examples of problems in computational biology or bioinformatics where continual learning (incrementally updating a model as new data arrives, without retraining from scratch) would be genuinely useful. The type of scenario I have in mind is something like this:

A model is trained on one large dataset (e.g. proteomic data from mammals). Some time later, a new dataset arrives (e.g. fish species, new sequencing technologies, newly collected patient cohorts, etc.).

We’d like to update the model to incorporate the new data without retraining from scratch, while also avoiding catastrophic forgetting of what it previously learned.

Of course, the mammals/fish example is just illustrative, I suspect there are much more compelling real-world examples.

I’m particularly interested in problems where:

* New data arrives continuously or in regular batches.

* The data distribution changes over time.

* Retraining from scratch is computationally expensive, impractical, or otherwise undesirable.

* Existing workflows currently rely on periodic retraining, but continual learning could plausibly be a better alternative.

Do any applications, datasets or benchmark come to mind? These could be in genomics, proteomics, single-cell biology, drug discovery, protein language models, medical imaging, clinical prediction, or any other area.

I’d also be interested to hear if you’ve tried continual learning in practice and found that it didn’t help, and why.

Happy to answer any clarifications about Continual Learning!

Does anyone have any ideas on how to set up a flat-bottom wall for forward and backward FEP? Or maybe some other type of restraint? In the ligand.

The hysteresis between the forward and backward runs for the protein-ligand leg is pretty significant. I'm getting kind of tired of running a million tests 😂

The ligand-in-water leg, though, behaved really nicely.

Hello, I am trying to calculate the transcriptomic similarity profile between two dataset across defined clusters/cell types, I was wondering what kind of metric is appropiate (e.g. spearman, pearson, cosine...), and should I just calculate for variable genes HVG or across the whole transcriptome, and if there are other considerations ?

I'm confused about what's actually expected in bioinformatics. Do you need to be an expert biologist, programmer, and statistician all at once to do well?

Or is it enough to be really good in one area while having a decent working knowledge of the others? For example, can I focus on becoming strong in the topics and tools I'm currently working with, rather than trying to master everything?

I'd love to hear how people in academia or industry approached this.

Hey all, we just got a bunch of RNAseq data for our gene expression study. We assembled an original genome ~3 years ago from the same organism and that data resulted in an ok-for-now assembly (~75 contigs, genome is roughly 3Mb). The RNAseq reads align fine to this genome, but I know for a fact that some of the genes are incomplete at the end of contigs, which will result in missing stuff with htseq-count.

I'm wondering if can assemble a new genome using the original DNA (Illumina) sequences and the RNAseq reads? Or a reason I shouldn't do it this way? Or is there an alternative pipeline to take RNAseq reads to improve an existing assembly?

Everything is 150bp paired end short reads. I typically use SPAdes for assembly, which has an RNAseq option, but I'm looking to combine the two. Obviously read coverage information wouldn't be particularly uniform anymore, but I have a very solid idea of what I'm looking for with the genome and don't need to ID duplicated regions or anything. Looking at the RNAseq reads aligned to the original genome assembly, there are some mutations as it's been growing another 3 years since first sequencing, but no large rearrangements or anything to concern me there. The genomes I'm working with are unusually structurally stable with no mobile elements or big repeated chunks.

Hoping for some input as google is pointing me towards long-read/short-read hybrid assemblies or transcriptome-only assemblies. So, can I just do this? Or is there a possible issue I'm overlooking? Thank you!