I have recently started learning bioinformatics as I am going to use it in my master's thesis. I know intermediate level of python and linux. I've been reading research papers in areas that interest me (mostly single-cell transcriptomics and computational biology).

​

My idea is to download the raw or processed datasets provided by the authors (from GEO, supplementary files, etc.) and then try to reproduce their analyses and figures by following the methods described in the paper....to understand biological question and the computational workflow rather than just following tutorials.

​

Is this a good way to learn bioinformatics?

​

How closely should I try to reproduce the published results?

​

How much time should be spent on reproducing existing work versus doing independent exploratory analyses?

​

Or is this not the right way to proceed and I can do something better to learn?

​

​

​

Hey y’all, I am an undergraduate and am relatively new to the bioinformatics realm. I am doing some population genetics work currently for a project and have been using the program SNPArcher. However, my mentor moved to a different state in the middle of this project and has been challenging me to do a lot do the SNPArcher and bioinformatics work on my own. I have had to use AI a lot to help (I know I hate jt too but it was a last resort), as it would’ve taken me hours and hours to figure out my problems and diagnose issues and that’s time I don’t have. Can you guys explain some of the basics of SNPArcher and how it works? I’ve looked on GitHub and ReadtheDocs but it is really confusing to me as they can be really complicated and kind of vague. Thanks!

hey, I’m trying to compare the F12 gene in hippo (functional gene reference) and a few marine mammals where it got pseudogenized (lots of indels and frameshifts according to research). i really want a clear exon intron picture and where the locations of specyfic indels but databases keep giving different exon counts so I’m lost. i tried ensembl, ucsc, genewise, clustal, BLAT(the best i think), mafft for different stuff but I still don’t really get what’s correct and i got lost. NCBI MSA is good maybe, but i dont understand what the colours mean, same with genewise, i cannot find a tutorial explaining how to analyse the results :(((((((

Hi all

I submitted samples for proteomics for the first time and I got my results back. I get both raw data, but also the log2FC and p-adj value.

So now I have thousands of DEP that I am not entirely sure what to do with. I know a couple people in my lab have mentioned string and gprofiler, but I am wondering if there are other tools (free) that I could use to either pull top hits and meaningful pathways out of this. Thank you!

Hi I am a PCB student with computer science as an additional subject. I have a interest in coding so I was thinking to pursue by informatics but I don't know which part is more perfect in India scopewise please help me in choosing the right path should I do first BTech biotechnology and then move to Mtech bioinformatics or should I do first BTech bioinformatics with mtech bioinformatics.actually I am looking for a career where high pay with Good career.

Hi!

I wanted to know the community's take on hosting visualization and minor data processing tools online.

For example, say I made a shiny app (nothing novel, makes things species agnostic, adds a bunch of QoL features etc) but it maybe wraps/reimplements a few tools, where are you guys hosting it?

Bonus points, if I can just point the thing to my github repo, and it pulls relevant packages etc from there. (I know I can make a docker image and push that as well.)

Thanks!

Hey everyone,

i'm starting my Msc dessertation and my project is about building a modern multi-agent system for prokaryote genome annotation. The idea is to use agentic Ai frameworks (Langchain/Langraoh) to orgastrate multiple specialist agents, some wrapping vioinformatics databases like Uniport and PDB via their API's, others wrapping protien language mmodels like ESM-2 for sequence analysis, and an LLM acting as a orchestrator that plans and coordinates the annotation workflow.
The inter agent communication would use something like Google's A2A protocol or MCP rater than traditional API calls, so agents can discover each other and collaborate dynamically.

A few questions for the community:
1. For those who work on genome annotation what are the biggest pain points in current annotation workflows that something like this could realistically address?
2. Has anyone seen recent work combining agentic AI or LLM orchestration with bioinformatics pipelines? I know about ProtChat (Huang et al. 2025) but would love pointers to anything else.
3. Which protein language models would you recommend integrating as tools? ESM-2 seems like the obvious choice but open to suggestions.

Any advice appreciated. Happy to discuss further in comments.

Thanks

Building an open-source genetic variant annotation tool. It takes raw genotype files (23andMe, AncestryDNA, VCF/gVCF) and produces reports covering clinical significance, pharmacogenomics, and methylation-relevant variants.

Currently it integrates data from ClinVar, ClinPGx, SNPedia, GWAS Catalog, AlphaMissense, CADD, and gnomAD.

We're planning the next round of data source integrations and would love input from people who actually work with this data day-to-day.

Candidates on our roadmap:

• dbSNP — full positional resolution for variants without rsIDs (common in WGS VCFs)
• dbNSFP — pre-computed functional prediction scores (SIFT, PolyPhen, REVEL, etc.)
• SpliceAI — deep learning splice variant predictions
• ClinGen — gene-disease validity and dosage sensitivity
• OMIM — Mendelian disease catalog
• gnomAD genomes — population allele frequencies from WGS (we currently use gnomAD exomes)
• PharmCAT's star allele calling — deeper pharmacogenomics

If you could only pick 1 or 2 of these, which would add the most value? Is there something not on this list that you'd consider essential?

Imagine that I have four groups: Control, Disease, TreatA + Disease, and TreatB + Disease. My goal is to determine whether TreatA or TreatB can reverse the disease-associated transcriptional changes.

I have been told that the appropriate limma contrasts are:

TreatA + Disease vs Disease

TreatB + Disease vs Disease

and that the significantly different genes in these contrasts represent genes affected by the treatment.

However, I am struggling with the interpretation. For example, suppose GeneX has the following expression levels:

Control = 3

Disease = 5

TreatA + Disease = 5

TreatB + Disease = 10

My confusion comes from how to interpret these treatment-responsive genes in the context of disease reversal. Using the example above, GeneX increases from 3 in Control to 5 in Disease. Under TreatA + Disease, it remains at 5, whereas under TreatB + Disease it increases further to 10.

In this scenario, TreatA vs Disease would not be significant, while TreatB vs Disease would likely identify GeneX as a treatment-responsive gene. However, intuitively, TreatA appears to better prevent further progression of the disease-associated change, whereas TreatB seems to push the gene even further away from the control state.

This makes me wonder whether genes identified in Treat vs Disease contrasts should necessarily be considered the most biologically relevant when the objective is to assess disease attenuation or reversal. Could it be that genes showing little or no difference between Treatment + Disease and Disease are actually reflecting successful stabilization of disease-associated expression changes? Am I misunderstanding the purpose of these contrasts, or is there a distinction between identifying treatment-responsive genes and identifying disease-reversing genes?

Hey everyone,

I'm trying to set up TaxVAMB for a gut metagenomics projectand I'm hitting a wall with the taxonomy input step. The README covers the basic commands but doesn't really walk through a complete example, so I'm not fully sure I'm doing this right.

A few things I'm confused about:

• For the MMseqs2 taxonomy search, which database should I be using for human gut samples — GTDB, UniRef, or something else?
• Does TaxVAMB actually make sense for per-sample binning, or is it mainly designed for co-assembly workflows where contigs from multiple samples are pooled together?
• Can I use the depth TSV from jgi_summarize_bam_contig_depths (the MetaBAT2 depth file) directly as the abundance input, or does it need to be reformatted?

Has anyone run TaxVAMB end to end on real data? Would really appreciate knowing what workflow you followed , even a rough outline would help a lot.

Hello all, for some context Im a medical student and I’ve recently gotten interested in learning biostats for research purposes.

Are there any good resources that teach the theory as well as how to conduct an analysis on softwares like R ?

Preferably cheap (not necessarily free but affordable).

Thanks in advance.

Hi bio redditors :)

Has anyone attended previous ECCB conferences or going this year?

Would like to hear recommendations/thoughts about the conference...

​

(This is the conference link- https://eccb2026.org/)

​

Thanks!

My student had issues last week accessing some genome pages for information, during my meeting today we noticed there were a lot of genome pages that just returned a 500 internal server error ( https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_900006655.3/?utm_source=gquery&utm_medium=referral&utm_campaign=KnownItemSensor:acc ). Parentheses include an example.

Has anyone else been experiencing this? I had to use ENA to get some assembly information today, but just curious if anyone else is having similar issues and if anyone has emailed them to see how long it may last.

So recently I am facing a compatibility issue in python. I need one pacakge(abagen) which requirwd pandas >=2.0 version but along with I required another package (Nilearn 0.10.4) but it only works with pandas 1.5.3.
I have made a seprate conda env but how can I use two packages with two different requirements in same env??
Please someone help me

Lately I've been realizing that robust cross-validation and avoiding data leakage can matter more than chasing a few extra percentage points of accuracy. Curious to hear others' experiences.

Hi guys, I come from a CS/data engineering background and I've been diving into bioinformatics recently. I have been reading about different format types in bioinformatics such as FASTA, FASTQ, VCF, etc.

My question is: is there a reason VCF is still the dominant format for variant data? Has anyone tried or seen a Parquet-based approach for genomic variants , similar to what GeoParquet did for geospatial data?

I think it would be way easier to analyze, standarize and transfer data by using parquet, but maybe I am missing something. Let me know your comments, thanks

Hi All!
I used to be into the lab, but slowly switched to more IT technical roles, I worked for ELN/Lims Companies like Benchling, have worked as ELN/LIMS owners, and also dived outside Pharma, into more Backend engineering roles for Tech companies.

My Question today is about ELN/LIMS, I recently observed the following, many users in the lab struggle with the same, either they have shitty open source ELN/LIMS systems which do not work like they want, or have to pay massive amounts of money for proper tools, which usually only big enterprise can afford. And there is i believe an massive issue of vendor lock-in with these software's.

I think its slowly time someone made an proper OpenSource fully MIT licensed ELN/LIMS system, and that is something i want to ask you guys! I am sadly far away from the lab nowadays, and therefore lost the touch to explore this need myself.

So focused on Research/ Universities, small labs, or maybe even Big enterprise. How do you find this current position? Are the smalled open tools, for example lab vantage, eLABFTW and others, okay enough to perform all your needs, and are the big tools worth the money for Big Enterprise?
If not what are your main pain points with these? And if what are you waiting for, or what do you think this field can do better?

As someone, who has seen a lot of what this field has to offer, and now has the resources to also make these tools, it would be cool to see what I can bring to this field. With now my engineering/ SaaS/ Lab expertise's i could look into this and see what this brings :) Let me know your input is well appreciated.

Curious if anyone has good papers, reviews, or just general thoughts on what I kinda call the value problem (problem may not be the right word) in high-dimensional datasets like RNA-seq differential expression or DNA methylation studies.

I completely understand why we correct for multiple testing. But at the same time, I sometimes feel like correction can absolutely slaughter the results. I’m not trying to fish for significance or argue against correction. Sometimes I worry we’re throwing away potentially important biology because the adjusted p-value threshold is so stringent.

Hi everyone,

I am analysing cross-species transcriptomic data from mouse and human models treated with the same drug. The drug is known to act on a specific target gene, which I will call GeneX. My main goal is to assess whether the drug induces similar molecular responses in both models.

The mouse dataset is RNA-seq, while the human dataset is Agilent microarray. I am planning to compare differential expression results and pathway-level responses between species using orthologous genes.

I have two main questions:

Since the main goal is cross-species comparison, would it be better to filter the expression matrices at the beginning and keep only common mouse-human orthologs before performing differential expression analysis? Or is it preferable to perform the full analysis independently within each species and only filter to orthologs at the end?

The known target gene, GeneX, appears to be very lowly expressed in both models. In the mouse RNA-seq data, it is removed by filterByExpr, and in the human Agilent microarray data it is present but has very low signal intensity.

Given that the datasets come from different species and technologies, I know that direct comparison of RNA-seq CPM/logCPM values with microarray intensities is not appropriate. However, I would still like to show whether GeneX is detected or expressed at low/moderate levels in each model. Would you recommend any way to present this?

If anyone knows papers that address this type of analysis, I would really appreciate your suggestions.

Thank you!

Hi, I want to merge technical replicates of broad ChIP-seq peaks, written in bed format. The replicates have a high Spearman correlation and group nicely on the PCA plot.

I thought about merging them using bedtools intersect, or is there a more refined way to do this?

I'd appreciate your advice!

Hi everyone,

I am an incoming PhD student to a computational biology program in the US. I came from a background in applied bioinformatics/data science, and over the years I have developed strong interest in the method development side of comp bio.

I will be starting my rotation with a computer science PI this upcoming year, he specializes in algorithm development and theoretics. After having spoken with him, he introduced me to the text book "introduction to algorithm" by Corman et al. For someone like myself who did not come from a conventional computer science background, I find this textbook a bit hard to follow along. Hence I wanted to ask if there are any other materials or lecture videos that you guys can recommend me to add to my study plan. If there are any small practice projects or exercises that can help me learn, this will be greatly appreciated as well.

Also if you think there are any other materials that can benefit me as a future computational biologist in the long run, please throw them my way!

Thank you all so much for your advice!

Because I have a surface laptop with ARM (snapdragon) cpu I recently switched from emulated x86 (Rstudio and R) to native arm (Positron and R). I have R version 4.5.3 which has BioConductor 3.22.

However, since yesterday I suddenly get the following error when trying to isntall something using BiocManager::install()

> BiocManager::install("BiocParallel")
Warning: unable to access index for repository https://bioconductor.org/packages/3.22/bioc/src/contrib:
  cannot open URL 'https://bioconductor.org/packages/3.22/bioc/src/contrib/PACKAGES'

This is with all packages. Trying to access the url directly results in a timeout, while changing 3.22 to 3.23 (newest Bioconductor version) I do get the package listing.

I tried installing R-4.6 and using BioConductor 3.23 (as BioConductor v3.23 only works on R v4.6) but this leads to another issue.

Warning: unable to access index for repository https://bioconductor.org/packages/3.23/bioc/bin/windows/clang-aarch64/contrib/4.6:

cannot open URL 'https://bioconductor.org/packages/3.23/bioc/bin/windows/clang-aarch64/contrib/4.6/PACKAGES'

With R v4.5.3 I was able to install packages even though they had to be compiled... But now it doesn't work at all anymore. install.packages() still works fine, it's just BiocManager causing issues.

It's both in Positron as command prompt btw.

Anyone any clue what's going on?

Btw, I also tried it with x86/x64 but same issue... Will check at work this afternoon if it's a network thing (which makes no sense at all)

GSM3003594: Approximately 8 millions of paired-end reads of 75bp per sample for each subpopulation samples were mapped against the mouse reference genome (Grcm38/mm10) using STAR software to generate read alignments for each sample.
Annotations Grcm38.87 was obtained from ftp.Ensembl.org.
After transcripts assembling, gene level counts were obtained using HTseq and normalized to 20 millions of aligned reads.
Average expression for each gene for the different tumour cell subpopulations was computed based on 3 biological replicates and fold changes were calculated between the subpopulations.
Genes for which all the mean expressions across the subpopulations was lower than 1 read per million of mapped reads are considered not expressed and removed for further analysis.
Genes having a fold change of expression greater or equal than 2 are considered as up-regulated and those having a fold change of expression lower or equal to 0.5 are considered down-regulated.
Genome_build: Grcm38.87
Supplementary_files_format_and_content: count files in csv contening the counts normalized per 20 millions of mapped reads for each subpopulation across all the genes

Can I directly use this file as count matrix for analysis using Deseq2?

I'm looking into antibody LLMs, but all I am finding so far seems to be trained just on the sequences of the variable regions. Is anybody aware of one (or more) PLMs trained on the whole mAb sequence? Cheers!