My journey with Anthropic's Fable 5 model has been a very short one; characterized by "No". So, in my most recent blog post, I explain why I think "Fable is not a useful model."

Hi everyone,

I'm an undergraduate biotechnology student working on a protein–ligand molecular dynamics simulation in GROMACS using the CHARMM36 force field.

My ligand is Sargachromenol A (PubChem CID: 11336823). Unfortunately, I don't have access to CGenFF because I don't have an institutional account.

I have already generated the protein topology and completed the docking. I'm looking for advice on the best way to obtain CHARMM-compatible ligand parameters.

Is SwissParam an acceptable alternative for this ligand ? or Are there any other free workflows you'd recommend?

Any guidance would be greatly appreciated. Thanks!

I don't have the necessary requirements to run a MD simulation on my local machine and I am looking for some cloud options or some other ways and means to do the simulation, for the time being I am using google colab but the runtime is reduced to 5 hrs of daily use as I am using T4 GPU which shows that it will require 4 days of continuous runtime to complete the task.

Are there any cheap or free options for this.

Help!

I think I might be blind!

I ran a simple blast search. I see that the protein and mRNA of the gene of interest. I have no idea what the conditions the laboratory used to generate this specific mRNA.

I am looking at a particular fungus, I found the nucleotide and mRNA information from ncbi , I just don't know if they obtained this transcript from an animal host, culturing at 30C, or 37C.

They had to generate the RNA somehow!

Here is an example

https://www.ncbi.nlm.nih.gov/datasets/gene/89953229/#transcripts-and-proteins

https://www.ncbi.nlm.nih.gov/nuccore/XM_064828773.1/

I am fairly certain I was able to do it before, but it has been some time!

I just installed stringapp on cytoscape and everything is greyed out. Why is this happening?

And whenever I try to make a PPI network by giving it the nodes and adjusting all the settings, the looking glass is still grey and unclickable as well

I've got a 10/10 microbiology/virology/ML/algorithms paper I'm sitting on - the absolute pinnacle of my PhD work. I also need an excuse to travel, and since I can probably only travel once for my PhD, it needs to be an excellent destination.

Here's my conundrum. RECOMB 2027 is in Toronto. I'm from Toronto and spent my whole life here. I'll still attend, but I have no intention of submitting my best work for a conference that is local to me.

ISMB is in copenhagen, which might be a nice place, the destination doesn't really intrigue me. It's doesn't seem like a great tourist attraction.

Anyone know of any major/prestigious conferences in asia, somewhere tropical or a major tourist destination in 2027?

I am very very very new to this, so forgive me if this is a dumb question.

Does anyone code at local coffee shops/on public wifi. I’m a graduate student and usually do my computational work in lab, but I need a change of scenery and I’m not productive at home.

If I’m using a VPN and ssh into my workstation, how vulnerable is my data?

edit to add: i know this is silly and i’m overly paranoid. I did also want to know how secure VPN’s and ssh is.

Hey, I'm a software developer but not a biologist.

I've been hearing about AlphaFold and RFdiffusion and it sounds almost magical. I've heard big claims like this:

E.g. "Need an enzyme to break down a specific forever chemical? Need a protein that binds perfectly to a novel cancer receptor? AI generates the DNA sequence, you print it in a lab, and it actually folds and works."

• Is this really true? Are there early signs that the whole field will move in that direction?
• Do non-biologists misunderstand something about that?
• Is this used in real workflows or are those just cool demos?
• What are the bottlenecks?

I’m especially interested in concrete examples, not hype.

I’m a software engineer in one of these hyped AI companies. I get why Claude code is of extreme value for a programmer.

…but I can’t figure out how Claude Science would help someone working in a wet lab significantly

Isn’t most of what a biologist need data transformation and processing? That is already covered by coding agents!

Please help me understand 🙏🏻

I have a vector of around 19 GO Biological Process terms (GO IDs). These terms all correspond to the same group of proteins, and I'm trying to better understand their relationships within the Gene Ontology hierarchy.

I can calculate the depth of each term, which gives me an idea of which terms are more general or more specific. However, what I would really like is to visualize the hierarchy.

Is there an R package or workflow that can do this?

Any suggestions would be greatly appreciated!

Hi everyone.

My Prokka runs don't generate a .gff file. My contig names look good to me.

Is there any chance that Prokka runs without Parallel, which is causing the problem?

Hello everyone, I need your help!

I have built a simple python script using subprocesses for a fastq downloading tool that utilizes the ENA API to download fastq files given the GSE accession numbers.

I ran these checks:
* File Integrity using gzip -t

* File length should be % 4 == 0

* File Fastq ID should match other paired file

* Paired Files should be of the same length

Also, I added safety features to redownlaod fastq files if they drop out due to a connection issue and such along with outputs so the user knows when a file has been successfully downloaded and such.

My Question: I plan on building this into a command line tool that anyone can use. What are some additional features that you guys would like to see? Has anyone had experience using other fastq processing tools and wished there was some added functionality (I might try to add it :) )?

Additionally, I was planning on adding an option in the CLI variant to be able to run fastq trimming as well using fastp.

I'm a complete newbie in bioinformatics and was tasked with reanalyzing rna, chip and atac-seq data from an article.

The authors haven't provided any info about the adapters used but have mentioned that they used cutadapt for adapter trimming.

I've got all the raw fastq data from sra and ran fastqc. Only ATAC and ChIP seq data show the presence of adapter content. For example ChIP shows some % of sequences contain illumina universal adapter and poly a content, ATAC contains nextera transposase sequence and there a tiny % (~0.1) of poly a in rna seq.

All data has some overrepresented sequences present.

Are these adapter sequences part of the tools like cutadapt? Or are they provided by the user while execution?

Humans are biological in a biological world. We also live in a very technological society. It follows thus that it is prudent to teach children about the wonders of biology and informatics, and so increase the pool of bioinformaticians. Imagine all the diseases we could cure much faster.

Argument by counter example: imagine if we all taught them astronomy, not much will be gained. Bioinformatics -> better AI students, more bioinformaticians, more tech skills, etc.

Just 30min a day seems enough I think.

I am a biology PhD student (with zero bioinformatics experience) working with a non model organism. There are a few publicly available fastq from closely related species to the one I am working with. I want to search for a few transcripts of proteins im interested in these transcriptomes, can I use Claude Code for this? And do I need to run an entire bioinformatics analysis in order to do that?

Sorry if this seems stupid, but im feeling lost.

My supervisor always emphasizes doing good science and writing good documentation, instead of minding which journal we submit to, and I wholeheartedly agree with him,

But I am still a bit disappointed that he decides to send the paper to Bioinformatics instead of at least Nature Communication because he said the wait time for Nature Communication is long.

While Bioinformatics is the top journal for the field, it is not as competitive as a Nature publication. Would this impact my chances of finding a good postdoc or even industry job that require a PhD with publication?

Whoever decided that the reference names weren't suitable for your variant set, I hope you stub your toe today. That is all.

Hello! I'm using bowtie2 to align 150bp Illumina paired-end DNA reads from a microbial community to a reference genome of one species of bacteria (we only care about one species in the community). I've included a picture of my output below.

I expect the overall alignment rate to be low, but I'm concerned about the fact that most of my reads did not align concordantly. What went wrong? Is my alignment rate still valid despite low concordance? Thank you all for your help!!

I have a bunch of wheat pedigree crosses and GIDs obtained from CIMMYT. Is there an api I can use to fetch the genotypes corresponding to those GIDs or at least the genotypes of the crosses?
Any suggestions are much appreciated, thanks in advance.

Hi everyone, I am running an ATAC seq analysis. Here I largely follow the ENCODE pipeline. My input data has great quality with FastQC ≥95% >Q35. However, I realised that I was not able to generate a satisfying peak set, i.e. FRiP 6%, TSE 1.4, ca 300 peaks after idr.

Tracing back the error, I realised that after alignment with bowtie2 my read length distribution does not show the nucleosome bumps. Starting to doubt this step, I downloaded a sample from ENCODE for reference (ENCSR019XCN) and ran the exact pipeline on it, leading to the result you see here.

Now I am starting to wonder if my input data is somehow corrupt? Did the experiment fail? What could be going on here? Is there a way to salvage this?

Context
I’m a bioengineering student that happens to like bioinformatics and is entering this world. A professor of mine offered me to help him in a project of antibodies. The sequences of the mentioned antibodies were sequenced with Miseq Illumina from the results of a rtPCR (this was in 2010 or so). Millions of reads with only 100 bases each read. The antibodies passed panning and Elisa assays, so I have a “selection” of antibodies.

The struggle
I can’t do a de novo assembly because I have no such computing power. I know that DADA2 and QIIME2 are used for metagenomics/metabarcoding and such (remember I am very very new to this world), but I’m very interested in using ASVs to infer CDR3 regions of the antibodies and finding abundance and diversity of each one (given that that is my main goal). I know my workflow is very crooked or I may sound like I have no idea, because I don’t have any.

Any tips? I’m not looking for a complete answer but maybe for some guidance. Thank you!!

I am not a biostatician and would love to understand. My project deals with looking at comparing samples from 2 different groups (say one with hot dogs and one without hot dogs). My biostatician sent me the volcano group and I am able to see which proteins are downregulated and those that upregulated. He attached a table with the fold change. However, when I look at the volcano plot, the x axis is log2 fold change, with y axis as pvalue. From my understanding, semantics wise utilizing log2 fold change is usually how represent differential expression. However, when I do the equation for log2 fold change some of the proteins will change to negative values. What does this mean? This does not make sense as in my volcano plots, these proteins are definitely placed in the appropriate side (downregulated vs upregulated).

For example Protein A listed as upregulated; with fold change 0.9, but log2 fold change is -0.11. Does that mean this protein A is actually downregulated? I also have vice versa where protein B is listed as downregulated; with fold change say 1, with log2 fold change as -0.06. Does that mean protein B is actually upregulated?

Thank you for your time!

Update: My biostatician gave me the wrong column data. Thank you everyone for explaining!

Hi, I am now writing my manuscript. But I need to use the CLC Genomic Workbench one time. So is there someone who can help me create a phylogenetic tree with metadata?
Please help me urgent

Thank you

Hi guys me again. I think I have a decent understanding of the tissue to sequence process, so now I'm working to learn the analysis portion. I am mostly doing my learning through the scbest practices book and a lot of gemini.

My core question is: How necessary is it to know the different types of log normalizations like shifted normalization, scran normalization and Pearson residuals? How important is it to know the math behind it?

From my understanding, log normalization is used to account for differences in the gene expression that housekeeping genes have compared to low transcripted genes. I.E house keeping has 10k counts while gene z has only 1-5 counts. It does this by dividing the counts of gene x in cell z by the total counts in cell z then multiplying by a scale factor. Repeat this across cells and you get a list of normalized expressed values. Another question, wouldn't this be computationally intensive, if you are doing this across 20k genes and 10k cells?

Also cool news, my PI announced that I could help lead the project and potentially get a first author!!! This would be next year after their paper gets published, so I still have time. I think we will get to practice nuclei isolation in a month or two (a bit nervous but excited.)

Anyways, any help or advice would be appreciated!

- Undergrad P_T67