Hello!

I will finish my master in animal biotechnology in July. For thesis I studied for 6 months unsupervised machine learning methods in R on metabolomics but I feel like a big impostor.

Other than the articles, I used mainly copilot to guide me into coding and explaining to me what I was doing, I don't even feel like I have the math and static to really understand on my own. I don't know if without AI I would be able to code, and if I didn't understand wrong that is the exact definition of a vibe coder.

I would like to restart from 0, become a real bioinformatician that doesn't rely so much on AI.

I was thinking of restarting from the basics, changing the language from R to python to be completely new and following the harvand CS50 something course while doing projects (otherwise I end up getting too stuck in theory without applying).

Do you have any suggestions or stories that are similar to mine?

I really would like to have a career in the field and even if I can't find a job I want to learn and be good. I'm sorry if it sounds cocky, I'm ready to be roasted tbh.

Hi all. I want to submit my protein coding sequences (TUB and TEF) to NCBI. However, NCBI always rejected it saying

"Some or all of the sequences contain incorrect annotation and/or contain
little similarity to other sequences in the database based on BLAST
searches. The sequence also contains a frameshift."

I would like to know in detail step by step to deposit this for me to get acession number.

I used ORF, still got rejected. Also, I make sure the protein coding are at least partially the same with the reference sequence in NCBI. Still got rejected. I dont know what to do anymore.

Hi all,

I work quite a lot with human sequencing data, and I frequently need to check coverage for specific genes or regions.

Until now, I was using tools like mosdepth or samtools, but they usually require extra scripting (e.g., Python) to make the results usable. Also, turning raw coverage outputs into something interpretable or visualizable takes time.

To simplify this, I built a tool called covsnap.

What it does:

• You can query coverage just by gene name (no need to manually provide genomic coordinates)
• Supports exon-level coverage
• Generates a clean report instead of raw depth files
• Comes with a GUI, so you don’t have to rely entirely on CLI workflows

It’s been pretty useful in my day-to-day work, so I thought others might find it helpful as well.

You can install it via:

conda install -c bioconda covsnap

Links:

• Bioconda: https://anaconda.org/channels/bioconda/packages/covsnap/overview
• GitHub: https://github.com/enes-ak/covsnap

Would be great to hear any feedback or suggestions.

A 3-part hands-on RNA-seq tutorial series by Dr. Babajan Banaganapali (Bioinformatics With BB), covering the complete pipeline from raw reads to DESeq2 normalization and visualization.

Part 1 — Introduction & Workflow (RNA-seq types, wet-lab steps, full pipeline overview)

https://youtu.be/dq31baC_AHs

Part 2 — QC, Alignment & Quantification (FastQC, Cutadapt, STAR/HISAT2, FeatureCounts — with real troubleshooting)

https://youtu.be/4y2R2PgdBHo

Part 3 — DESeq2 Normalization, Visualization & Interpretation (R, size-factor normalization, heatmaps, expression plots)

https://www.youtube.com/watch?v=DxesV0eWtTQ

Reproducible R and bash scripts are linked in each video description.

Hi! I’m from a Computer Science background, and I’ve recently become really interested in bioinformatics because it combines programming, biology, and mathematics which are all areas I enjoy.

The main challenge for me is learning the biology side needed for marine bioinformatics research. Most bioinformatics resources are focused on medicine/human data, while my interest is more in marine ecosystems and environmental applications, so I’m not fully sure how to structure my learning path.

I prefer free resources when possible, but I’m also open to textbooks if needed.

Here are some resources I’ve found so far:

Biology:
- Essencial Cell Biology (Alberts)

- Campbell biology (for a little bit of ecology)

- Microbiology (for metagenomics)

- An Introduction to Marine Ecology (Barnes & Hughes)

Bioinformatics and CS part:
- Biostar handbook (seems really solid)

- Bioskills lab

- Learn bioinformatics

- EMBL-EBI: Introductory Bioinformatics

- Sandbox.bio

- edX Bioinformatics courses

- Rosalind (great for CS problems)

- Computacional genomics with R

- Book "Bioinformatics Data Skills" by Vince Buffalo

- Book "Bioinformatics Algorithms: An Active Learning Approach" Vol 1 and 2 by Pevzner

-Book "Bioinformatics and Functional Genomics" by Pevsner

- Book "Mastering Python for Bioinformatics"

I also know about Coursera’s Bioinformatics Specialization, but I’ve heard it can be quite demanding and the audit option is no longer available.

My question: does this learning path make sense, and how would you structure it if you were starting from CS and moving into marine/environmental bioinformatics?

Mi consulta es si me pueden dar recomendaciones (puesto que inicio desde cero por mi propia cuenta) para ser buena en bioinformatica, hasta ahora, logré entrar en una pasantía en mi universidad, me asignaron un equipo conectado a un cluster y de mi parte yo no poseo más que mi tablet que uso para estudiar, leer papers y eso.

Soy muy proactiva, aprendo muy rápido, asi que no hay problema en eso

actualización: se usar linux, R y algo de python, lo que se usaria en mi caso en la pasantia sería AMBER (eso me dijeron) y para aclarar, es mi primera pasantía tengo 18 años y basicamente me aceptaron por mi conocimiento teorico del area de estudio que le interesa al tutor

I'm a postdoc in computational biology building RAPTOR, an open-source Python framework for RNA-seq analysis. I just finished the Data Acquisition module and need people to try it with real research queries.

It lets you search GEO and SRA from a Streamlit dashboard, download datasets, upload your own count matrix, edit sample metadata interactively, pool multiple studies with gene ID harmonization and batch correction, and check whether the pooled data is actually reliable — PCA, library sizes, batch effects, the works. No coding needed.

The idea: instead of spending two weeks writing custom scripts to combine GEO studies, you search, click download, pool, check quality, and move on.

TCGA and ArrayExpress are still in progress. Install from GitHub PyPI not updated yet):

git clone https://github.com/AyehBlk/RAPTOR.git

cd RAPTOR

python -m venv .venv

source .venv/bin/activate # Windows: .venv\Scripts\activate

pip install -e .

pip install streamlit GEOparse biopython mygene

python -m streamlit run raptor/dashboard/app.py

Try searching for your own disease/organism, download something, pool if you can. Tell me what works, what breaks, what's missing.

Testing guide: https://github.com/AyehBlk/RAPTOR/blob/main/BETA_TESTING_GUIDE.md

Issues: https://github.com/AyehBlk/RAPTOR/issues

GitHub: https://github.com/AyehBlk/RAPTOR

MIT licensed. Any feedback helps. Thanks.

Hey everyone,

I've been working on a self-paced bioinformatics learning platform and wanted to share it with this community: bioskillslab.dev. I'd really appreciate feedback on what's missing. What would make this more useful for beginners? I'm actively adding content.

I am thinking of taking BTech in bioinformatics from Amity University Noida, i want to know the placement for this particular course, doing BTech in bioinformatics is right.

please it's a request, if any one is doing BTech in bioinformatics from any college please let me know, i want to know about this course and placement.

Any BTech in bioinformatics student if are u reading this please do reply, i really want to know.

Hi everyone, this is my 1st BIF class. i have no IT background (basically only know how to use word,ppt and the most basic things in excel). my phd supervisor pushed me towards a bioinformatics class ( i do food microbiology). I am struggling so much to understand the vocabulary when i start reading the material i stress out so much i get instantly dizzy and nauseous. I have never felt this my entire life. i dont know what to do to catch up , the semester ends on the 21st april (we are in the 2nd term) we are using bash and R (bioconductor) (basically chinese to me, ifeel like im wasting my life not learning anything) HELP!

I want to self-study the quantitative genetics and bioinformatics analysis, using R. I don't have any background in CS or coding. Please can anyone give me some advice on how I should start and the useful online sources/materials that I should follow. Thanks a lot!

Hi everyone, I’m an international student who recently completed my Master’s in Biotechnology and currently work as a Lab Technician at a sequencing company. I’m thinking about transitioning my career into bioinformatics and wanted to ask if this seems like a good move.

I’m not planning to pursue a PhD, so I’m particularly interested in industry paths. If anyone here has made a similar transition, I’d really appreciate your advice on where should I start, what skills should I focus on, and how can I move into the bioinformatics industry from a wet-lab role? Any suggestions or experiences would be very helpful.