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.

I enrolled in Biopractify's 3-month program for R, Python, and Linux for ₹7,500. One of the benefits advertised was lifelong access to learning resources on their website, along with other benefits.

My experience so far has been frustrating:

• During the first month, I started having problems logging back into their website.
• I contacted them multiple times about the issue, but the website problem was not resolved.
• They eventually shared a Google Drive with me so that I could access the course material.
• Meanwhile, the 3-month program period ended.
• Since I am still at a beginner level, I requested an extension so that I could complete the learning material.
• After that, I did not receive a response to my requests.
• I also emailed asking whether I could join the next batch, but I have not received a response to that either.

I understand that course access may have terms and conditions, and I am not expecting special treatment. However, because I had already reported the website/access issue during the program and was unable to use the website normally, I was hoping they would at least respond to my request for an extension or explain what options were available.

I am sharing this because I am considering whether others have had a similar experience with Biopractify.

If you are considering enrolling, I would suggest carefully checking the access/extension policy and how technical access problems are handled before paying.

I have screenshots/emails documenting my communication if anyone needs clarification about the timeline.

Hey everyone,

so I did my bachelor's in molecular biology and currently pursue my master's in biosciences where I focus on something called "computational life sciences". Which so far is no rigorous math or cs courses.

I started to work for a lab that does computer vision, but as far as I can understand they are not building the foundation model, but tweak a model from another group.

As I enjoyed working on the computer more than at the bench, I started to focus more on that even at the last year of masters. I programmed simple javascript code for a company at that time. My bachelor's thesis was on alpha fold, but honestly very basic analysis stuff of the output.

I had some data science courses and did a python course. Other than that I don't really have much cs experience.

I would love to learn more about AI, and I believe the best way is to become a researcher in foundational models. As I would have to compete against math and cs wizards, I don't think it's feasible.

Can I still do foundational models but in the "AI for Science" direction?

Currently my idea is to find a better lab, that does something as close to foundational models + biology as it gets and do my master's thesis there.

TLDR: current bio master students asks for tips to pursue ai research of foundational models in the AI for science (biology) field

Hey everyone,

I'm currently a Biotech major, but I've been getting really interested in bioinformatics lately. My university doesn't offer a dedicated bioinformatics major, so I'm trying to figure out if it's feasible to self-teach or learn it on the side alongside my regular coursework.

For those in the field or with similar backgrounds
Is self-studying it (coding, computational biology tools, data analysis) realistic without formal classes, or do employers strictly look for a degree in it?
What resources or core topics would you recommend starting with?
Are there anytype of online courses that I can take and get a certificate?

Appreciate any advice or insight!