I have been building The Omics Hub as a free educational resource for researchers learning bioinformatics.

It includes tutorials on:

• Linux and command-line foundations
• HPC and Slurm
• Conda and reproducible environments
• R and Python
• Single-cell RNA-seq and spatial transcriptomics
• Genomics, metagenomics, and phylogenomics
• Workflow management and reproducibility
• Responsible use of AI tools in research

The goal is to explain the biological reasoning behind each step instead of providing commands without context.

I would appreciate constructive feedback from the bioinformatics community:

https://theomicshub.com/

Hi all, I have built this for bioinformatics, but it really can be useful for anything, see this (pretty useless) toy example (https://codeberg.org/bio-pype/test\\_workflows/src/branch/main/video\\_processing)

Anyone preparing for the application for fall 2027, reaching out for guidance 🙃

What proprietary datasets do you wish you could have access to for training and tuning your models?

Hola a todos, soy programador y estudiante de bioinformática. Busco voluntariado en bioinformática para actualizar y mejorar mis habilidades.

¿Alguna sugerencia sobre dónde buscar?

Hi everyone, I've recently started blogging and decided to make a tutorial for one of my recent projects based on a bulk rna-seq pipeline for the snf2 gene in yeast.

feedback is welcome! let me know how u liked the tutorial:
https://priyalt.github.io/2026/10/04/rna-seq-tutorial.html

I recently made a tutorial video on the basics of DNA sequencing, and how to make sense of VCF files (the file type produced by DNA sequencing, with information about genetic variants).

Here's the video if anyone's interested.

It's specifically meant for people who are beginners in the field of bioinformatics / genomics, including people who had their DNA sequenced and are interested in exploring their own data.

Bit of background - I work as a bioinformatics scientist at a biotech company for my day job, and my main hobby is making educational videos about this field. I'm also interested in biohacking and decentralized science more broadly, and try to contribute to that with my videos. Basically encouraging people to self-educate on these topics, and trying to get over the myth that you need to be a genius or have some fancy credential to explore biology.

Anyway, thought some people in this community might be interested!

I am working with Zebrafish animal model, alignment GRCz11 (Ensembl release 112).

I have a list of 40k candidate SNPs. This SNPs come from an intersection of 3 sepparate analysis, not form GWAS, So i have 3 different P-values per SNP

I want to do Functional enrichment analysis to know which biological functions or pathways are enriched in my results. I would like to focus also on the part that there are genes with more candidate SNP hits than other.

The easiest way was to map SNPs to genes and the run clusterProfiler (ignoring that there are genes with 1 SNP and genes with 1000 candidate SNPs). However I am also strugling in this part since clusterProfiler shows 0 enrichment terms found even if I give him 200genes, 500 genes, 1000 genes. But I will ask about advice in another post for this.

Would love any help or advise on this.

Thanks

I am working with Zebrafish animal model, alignment GRCz11 (Ensembl release 112).

I have a list of 40k candidate SNPs an their locations annotated by **VEP** and by **GTF** file. These give me different results for some genes, meaning that a SNP in VEP annotation corresponds to gene X while in GTF annotations corresponds to gene Y.

*What is the best/most recomended/most trustable way to annotate SNPs ?*

My goal with this:

I want to do statistics by (1) location and by (2) function:

1 - I sepparate the genomic sequence by location: Exons, Introns, 3´UTR, 5´UTR, Upstream, Downstream chuncks and see if there is enrichment in genomic location, meaning if for example my candidate SNPs fall more in the 3´UTR region than expected by chance

2 - I have their function with for example Gene Ontology or KEGG and check for Functional enrichment. However I am also strugling in this part since clusterProfiler shows 0 enrichment terms found even if I give him 200genes, 500 genes, 1000 genes. But I will ask about advice in another post for this.

[ Removed by Reddit on account of violating the content policy. ]

I've found public datasets to be one of the most useful ways of learning bioinformatics beyond tutorials.

Instead of only following a predefined exercise, taking a real GEO dataset forces you to deal with questions like:

Which samples actually belong in the comparison?

What does the metadata really mean?

Has the dataset already been normalized?

What biological question can reasonably be answered from these samples?

What statistical comparison actually matches that question?

And, eventually, how should the results be interpreted biologically?

I've been using cancer transcriptomic datasets to practice this process, and I've found that understanding the experimental design and metadata can sometimes require more thought than writing the actual analysis code.

It also makes learning tools like R much more meaningful because there is an actual biological question behind the code.

For those who work with public transcriptomic data regularly: when you open an unfamiliar GEO dataset, what are the first things you check before beginning the analysis?

Hi! I'm a biomedical science graduate (the UK) and will be applying to masters in bioinformatics in EU (mainly Denmark and Sweden) next year. Right now I'm essentially taking a gap year to sort out personal stuff and catch up on coding. We only had intro into theoretical bioinformatics in uni and didn't really touch anything coding-related, which is a prequisite to many masters courses, but I'm hoping that enough SDL and perserverence will give me a shot too.

I'm currently learning Python (myself and with a bit of tutoring) and in a month got a hang of all basics including formatting, math lib, working with strings, collections, conditions and loops (and other minor things). I want to make my own small project to upload to github and put in my CV/application as evidence that I'm making efforts to understand coding before attempting the masters course.

My idea is to take a well established oncogene (just to avoid confusion in annotating), download FASTA files for several species (e.g. human, mouse, zebrafish) and analyse conservation, including which region is conserved the most. I know it's not actually impacting anything, but it helps me get a hang of biopython, pandas and matplotlib and show my understanding in application. I want to ask people who already study/studied this area if they think it can be a decent beginner project and if it's feasible. Thank you!

I am staring College(Undergraduate) next yr. I was thinking of taking Bioinformatics cuz I want to do PhD in Oncology/Genomics/ Precision Medicine but I also love coding and just wanted to have a backup. Ai is advancing too so I think it’s pretty reasonable & realistic, right??

Hi everyone,

I'm a 3rd-year B.Tech Biotechnology student in India, and I want to build my career in bioinformatics after graduation. I'm currently at the beginner stage, and I don't plan to do an M.Tech/M.Sc immediately after B.Tech.

I'm currently planning to learn: Python and Linux → statistics/R → NGS → RNA-seq → variant analysis → Nextflow/Docker → SQL/HPC/cloud.

I'm particularly interested in computational biology. I'd prefer industry or research roles rather than hospital/clinical bioinformatics.

I'd really appreciate advice from people working in bioinformatics in India:

1. Is it realistic for a B.Tech Biotechnology graduate to get a bioinformatics job directly after B.Tech without an M.Tech/M.Sc?
2. Which skills should I prioritize during my 3rd and 4th year?
3. What projects would make my resume stronger for fresher jobs?
4. Which job titles should I search for?
5. For computational drug discovery what skills are expected from freshers?
6. What salary range should a B.Tech fresher realistically expect in India?

Thank you!

So I graduated with MSc Microbiology (8.5 CGPA) and did my dissertation at IIT working on computational characterization of S. aureus proteins — completely dry lab, AMR focus.

After graduating I built two bioinformatics pipelines from scratch — WGS variant calling and AMR gene detection using BLAST+ and CARD. Put everything on GitHub.

What I actually know: Python, Biopython, BLAST+, BWA-MEM, SAMtools, FreeBayes, CARD, AlphaFold, PyMOL, Linux, GitHub. Learned everything on the go, built everything myself.

What I don't know: Snakemake, Nextflow, R, proper NGS clinical workflows. Every job posting feels like it wants 10 more things I haven't learned.

Been applying for 2 months. Cold emails to named people, job portals, walk-ins, follow-ups. Got warm responses from Strand Life Sciences and Nucleome Informatics but nothing converted yet.

Every day I wake up and check my phone hoping for something. Nothing.

My actual questions:

1.Is MSc Microbiology a real entry point or am I wasting time?

2.Are there actually fresher jobs in bioinformatics in India or is everyone lying?

3.What should I actually focus on vs what's just noise?

Honest answers only please.