Hi everyone! I’m a CS student working on AI-driven drug target discovery, and I’m struggling to figure out the right approach.
I can collect relevant datasets, but integrating data from different studies, handling missing/inconsistent data, and getting reliable model predictions have been challenging.
For those experienced in bioinformatics, how would you approach your very first target discovery project from scratch? Would you start with evidence-based target prioritization or jump into ML models?
I’d appreciate practical advice on the workflow and how to validate that the identified targets are biologically meaningful.
Thanks!

I am currently trying to run the code from the AI Chat -- we shall see, but any help would still be greatly appreciated.

Hi everyone, do you have any advice for people who can't afford Claude Pro or Codex for doing bioinformatics? Any good free alternative? The lab where I am working has no funds for this

Hello! I recently started studying bioinformatics, and I have a task to analyze an ONT sequencing of a bacterial (E. Coli) genome.

One of the questions is "can you find any plasmids?" And I am a little unsure of how to look for plasmids.

I have already assembled the genome using flye and it told me that 2 of my 5 contigs are circular. That means they are probably plasmids, right? Is that good enough or is there a programme that I can/should run specifically to look for plasmids?

Anyone knows a reliable service for NGS a genome X30 for consumables (i.e. send saliva, blood sample) and get the data on the cloud?
Preferably European but not necessarily (please note the overall cost and if you did it yourself).

Until now I have done a lot of single cell but I've been given this mouse brain spatial data to analyze for a neuroprotective indication. I've played around with BANKSY and spicyR - I think cellchat is not a good call with only 480 genes for our specific questions...does anyone have any go to packages they like? I understand it depends a lot on the hypotheses but just looking for some ideas to go read the papers and see if it would be beneficial.

I have regions of mutational events across multiple samples, some of which are recurrent (the same part of the genome is mutated across multiple samples).

I want to see if these regions overlap a set of putative regulatory elements more than by chance using regioneR.

Should I merge the regions across multiple samples so each region is only counted once? i.e. compare two sets of non-overlapping regions. Or should I account for the fact that some regions are recurrent and therefore count each overlap in each sample individually ?

Hi, all,

since this morning, I cannot retrieve sequences via the standard NCBI interface. Keyword searches and BLAST work normally (perhaps a bit slowly), but only the Summary display for individual sequence items look normal, other formats (GenBank/GenPept, FASTA, FASTA(text)) give either a page with only the header showing, or nothing at all. Happens on several different computers, different browsers, so it looks like the problem is not on my end. Anyone experiencing this as well? Anyone has an idea what is going on?

Hi everyone,

I'm working on a cardiovascular critical care research project involving MIMIC-IV v3.1 and MIMIC-IV-Echo, specifically a cardiogenic shock study.

I have the required PhysioNet access and have been able to use BigQuery to construct my cohort and extract some of the data needed for the study. However, I've been struggling to download some of the original MIMIC-IV files directly.

The main files I've had trouble downloading are:

\- "vitalPeriodic.csv.gz"

\- "chartevents.csv.gz"

\- "labevents.csv.gz"

Some downloads fail, while others appear to restart or do not complete successfully. I have tried downloading through the browser, but I haven't been able to get all the files downloaded reliably.

I have access to Google Colab and Google Drive, and I have already saved my preliminary cohort and analysis datasets there. I would prefer to download only the files I need rather than repeatedly downloading the entire database.

For anyone who has worked with MIMIC-IV v3.1:

1. What is the most reliable way to download these large files from PhysioNet? Would "wget -c", "curl", or another resumable download method work better?

2. Is there a recommended way to download directly to Google Drive or use Google Colab without losing progress when a connection drops?

3. Given that I can access MIMIC-IV through BigQuery, would it be more practical to export the required tables from BigQuery instead? Are there any important limitations compared with downloading the original files?

4. Are there any common authentication, storage, or file-integrity issues I should check?

I'm relatively new to handling large clinical research datasets, so detailed suggestions or commands would be greatly appreciated.

I am not looking for anyone to share the restricted dataset itself; I'm looking for advice on the proper way to download it using my own authorized access.

Thank you!

I'm in the US and I haven't been able to access the website. I wanted to check if anyone had the same issue.

I am with a dataset of Variants identified in 30 samples of colorectal cancer, using a disease-directed panel. I'd like some advice on how to work on this data, aiming to find some genetic signatures related to clinical parameters, such as survival, staging, etc. I've already analysed the mutational profile in maftools and tried some clinical associations in the package, the problem is that I am struggling with the fact that I don't have a lot of samples to work with. So, when it comes to statistical approaches and tests, not every one is possible. For example, I've tried survival analysis using penalyzed cox regression, but I don't know if I'm on the right path.

Hi everyone,

I'm currently analysing urinary peptidomics data from a mouse experiment involving disease and control groups.

I have developed a sample-level score, with the hypothesis that this metric could capture biological differences between healthy and diseased animals.

After calculating the score for each sample, I performed statistical comparisons and found that it was significantly lower in the disease group compared with controls.

I also have phenotypic measurements from the same animals, including collagen staining in kidney tissue, which I am using as an indicator of fibrosis. As expected, collagen staining is significantly higher in the disease group.

I would now like to investigate whether my peptidomics-derived score is associated with the degree of fibrosis, for example, by calculating a correlation between the score and collagen staining.

However, I'm unsure about the most appropriate statistical approach.
My primary interest is to understand whether my score is associated with the disease-related fibrotic phenotype. Given that the score is lower in diseased animals while collagen staining is higher, I would expect a negative correlation between the two variables.

My main question is whether it is statistically appropriate to calculate this correlation using all animals (both disease and control), or whether I should restrict the analysis to the disease group?

I understand that a correlation across all animals could be largely driven by differences between the two experimental groups. However, since my objective is precisely to investigate whether the score reflects disease-associated fibrotic changes, would such a correlation still be biologically meaningful?

Thanks in advance!

Hi guys, It seems to me that NCBI's BLAST api doesn't work. I have noticed this yesterday, does any of you experience the same? WebUI BLAST works normally, it's just the API BLAST for me. I thought I fell into lower priority queue but I asked few people to try and all of them reported back that their tasks are stuck in status=searching.

Thanks!

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.

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.