Posting with mod approval!

Hey everyone, I’m a 3rd Bachelor's student in University of Debrecen, Hungary and over the end of my 2nd-year summer (2026) basically during my internship , i was more into building an automated Nextflow DSL2 pipeline for our yeast genomics lab because we had to do alot of manual work like creating samplesheets for almost every processes ( like curl, joint genotyping , etc)

Since ,we work a lot with Saccharomyces genus and my main project is the phylogenomics of historic lager yeasts, which are messy interspecific hybrids of cerevisiae and eubayanus.
Before this, our lab was mostly relying on manual bash scripts, and when scaling up to 100+ genomes I kept running into the same headaches that I think a lot of other labs working with hybrids or non-model microbes deal with:

If you map hybrid reads to a single reference, you get forced mismapping. If you map competitively to a concatenated pan-reference, it fixes the alignment, but then GATK lumps the divergent subgenomes together during joint genotyping and ruins the variant filtering stats. On top of that, writing hundreds of intermediate SAM/BAMs was kinda choking the storage.
Also, static ploidy 2 calling was messing up haploid mitochondrial/plasmid DNA, and reading massive VCFs for distance matrices kept running out of RAM.

Since I got super passionate about workflow engineering over the summer, I ended up building an 8-module workflow saccharomyces-genomics-pipeline to automate the whole thing from raw SRA/FASTQ to trees.

1. For the hybrids, after competitive alignment to the pan-reference, the pipeline reads chromosome prefixes (`species_map`) during `GenomicsDBImport` / `GenotypeGVCFs` and splits each subgenome into its own isolated master VCF. It also uses a `ploidy_map` so nuclear chromosomes run as diploid (2) and organelles/plasmids run as haploid (1). -
2. To stop consuming too much cluster storage, alignment streams directly in memory (`bwa mem | samtools view -Sb | samtools sort`) with zero intermediate disk writes, and `GenomicsDB` updates incrementally when we add new strains. -
3. For the trees, the last module uses `awk` to strip the VCFs into allele tables for an OpenMP C++ / R engine that computes pairwise IBS distance matrices fast without the Java memory crashes.
4. - To stop consuming too much cluster storage, alignment streams directly in memory bwa mem | samtools view -Sb | samtools sort with zero intermediate disk writes, and GenomicsDB updates incrementally when we add new strains.

One thing I wanted to ask about (negative branch lengths in Newick trees):

When I was building the phylogenomics module( i havent finalized it yet) to output Newick trees from the pairwise IBS distance matrices, I sometimes ran into negative branch lengths when using standard Neighbor-Joining (NJ).

From what I read, this happens when the distance matrix isn't strictly additive (which makes sense with hybrid/introgressed yeast strains). The solution I looked into was calculating networks using Neighbor-Net alongside NJ, but I'm curious how people with more experience handle this in automated pipelines. Is there a clean way to avoid negative branch lengths in Newick trees here , or do you just leave them as-is and let the user handle it?

Because since i already developed till distance matrices, it would be nice to have newick trees in the results ... but we just decided to stop at distance matrices- so that we can wind up other stuff especially with my last year and other application stuff for my masters and yk stufff..

Since I'm still in my learning phase and learning new stuff every day, I'm open to any suggestions, or ideas on the Nextflow code, the C++ IBS logic, or the phylogenetics side.
Would love to hear what you guys think and people working in relative field !!!!
like genuinely , wdy all think about it ??

i dont wanted to brag about what i built but its always nice to share stuff with experts !! especially im new in this field and ig i`ll stick to it for my career....

here is the repo and the citation .... lmk about it please!!
we're also including this in a yeast phylogenomics paper our lab is wrapping up to submit by the end of the month hopefully......

GitHub: https://github.com/hanzala-14/saccharomyces-genomics-pipeline

hanzala-14. (2026). hanzala-14/saccharomyces-genomics-pipeline: Saccharomyces Genomics Pipeline v2.1.8 (Version v2.1.8) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.23289452

Hi , I am working on an ML project to predict which genes are associated with specific phenotypes in Arabidopsis thaliana. I'm particularly interested in understanding how environmental conditions influence the relationship between genotype and phenotype.

I’m using 1001 Genomes, AraPheno, and Kawakatsu, but I’m struggling to find data linking genotype accessions to phenotypes measured under specific environmental conditions (e.g., temperature or light).

Does anyone know of a dataset or repository that provides this information, even for a single environmental variable? Multiple linkable datasets would also work.

Any pointers or suggestions would be greatly appreciated. Thank you!

Hello!

As the title says, I'm currently analysing maternal transcriptomics and need to see what is upregulated and downregulated in case group. This will be done longitudinally.

I have no background in bioinformatics, coding, statistics etc. My background is medical. Im doing this to built my research skills. My supervisor is amazing and has patiently taught me everything. I am now analysing the data for a possible publisable outcome.

I have been working on this for 2 weeks. I have understood the terms in my .csv file including NES, leading edge, log2fold, etc. My biggest hurdle is

I have literally tons of core enrichment genes, how do I know which to extract and look up. I did a excel count today and one gene stood out amongst the reactome GSEA Pathway which was AAAS. In the filtered unregulated data, it was present 32 times. Okay. But what to do with it? It's not in the top of the upregulated DEG data.

Additionally, i have browsed reactome.org and understood the heirarchy but I don't get how to organize my pathways in not "manual labor" way. I manually copy pasted the name and noticed that top 20 had singnal transduction, cell cycle and dna repair pathways.

I havent even touched KEGG yet and I'm lost with what to do with all this.

Any explanation or yotube video would be really helpful. Honestly, I feel im wasting my time by going in circles. Currently, i have a code running matching my 1426 reactome pathways to reactome.org API.

But i would appreciate professional help. It feels im lost in the woods. Whatever I do, whatever I read, i dont have the navigational tools just random information in my brain that is serving no purpose.

Thank you.

I ask because when I used it for my dataset from C. elegans, the homologene gene symbols and descriptions are obviously wrong. A lot of C. elegans genes do not have names, and instead use sequence names based of the BAC and YAC positions they were cloned onto. Examples are C30F2.4 and R08F11.4. When I ran such genes through analysis, the homolog instead showed completely different genes: C30F2.4 - C30F2.6, and R08F11.4 - fpn-1.3 (which has the sequence name R08F11.6). What's more, some genes with proper names that are well-annotated, like zipt-2.3, wasn't recognized at all. zipt-2.3 is a member of ZIP family, a family of zinc transporters, but the line for that gene is blank. This resulted in problems in enrichment analyses downstream.

I initially used WormEnrichr for my dataset, but the libraries were outdated (GO term 2018) and redundant GO terms weren't grouped. This made it very hard to make figures. Other tools like WormCat didn't have my zinc transporters annotated, either. Enrichment tool from Wormbase also trimmed the zinc transport GO terms. Under "zinc transport" on Wormbase, there are a list of genes associated with the term. If you paste this list to the Wormbase enrichment tool, you won't get the same GO term back in the results.

Right now I'm completely baffled. What am I supposed to use for my enrichment analyses, and what am I supposed to do with the results I get back from Metascape?

For years, across multiple locations, operating systems, I always come across the NCBI nucleotide (traffic shaping?) issue;

I think it might be the server trying to share data usage equitably and reduce server load?

What happens is that you do a search on nucleotide, find the list of matching sequences, then select ‘Send To’ ‘File’. The download commences, you get 60-80 megabytes in a few seconds, then you either wait a few minutes for the web browser to give up (which it invariably does), or you just cancel and try again;

With fast internet with no dripped packets, it usually takes a few dozen attempts before you can get the data (as an example, the data I am trying to get is around 100mb, but it takes dozens of 60-80 megabyte attempts before you finally get the file, so the issue is probably increasing downloaded data by at least an order of magnitude.

It is possible to sort by length and download piecemeal, of course, but I am wondering if there is a better way (given that the issue has been there for years, across different ISPs, locations, etc)

Probably a simple question, but can’t find anything using search engines!

Anybody that used the tool colbuilder to build collagen models for martini3, what locations of cross link did you use for bovine collagen fibril and why?

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?

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.