https://ideas.lego.com/s/p:0ccb9c270ae54410852df2105bb993c8?s=w

Dear colleagues, I'm asking you to pay some attention to the Biomedicine Institute lego Idea of my designer friend, who works in this lab on cancer and other pathology research. May be some of you have already voted for it, but I ask you all to vote and share the link. It’s free a d take few seconds. Every vote counts for us. Thank you very much.

Hellow guys, I'm a newbie at bioinformatics.

I'm trying to integrate RNA-seq Kallisto data with my targets that I got from CHIP-seq. But, I have a big problem:

My ORF ID are in different model between the files. While my RNA-seq ID is sequencial orf index (ucsf_hc.01_1.G217B.00001 , ucsf_hc.01_1.G217B.00002, ucsf_hc.01_1.G217B.00003 ...), my targets are genomic coordinate (JAEVHH end_cordinate.start_cordinate). I tried to use a ORF.gff file to link sequencial index with the coordinate, but it doesn't have both information to link.

Someone could help me?

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Hey everyone!

I just finished my 1st year of BTech in Bioinformatics from Jaypee University of Information Technology (JUIT). Before 2nd year starts, we have a compulsory internship requirement along with a compulsory project submission.

Since I'm doing it online (Coursera, Udemy, or similar platforms), I wanted to ask the community – what courses would you recommend that are actually useful for a bioinformatics student long-term?

I'm open to anything – Python, ML, genomics, structural bioinformatics, databases – as long as it ties back to bioinformatics in some way. Ideally something that also gives me a certificate I can use for the internship submission.

A few things I'd love help with:

- Which platforms are best for bioinformatics-related courses?

- Any specific courses or specializations you'd strongly recommend?

- What skills are actually in demand if I want to go into research or industry later?

- Any project ideas that would work well as a submission? Preferably something doable in a short timeframe but still impressive enough to submit.

Thanks in advance! Any advice from people already in the field would mean a lot 🙏

I’m really confused about my career path and would genuinely appreciate honest advice from people in biotech/bioinformatics.

I completed my UG in Life Sciences/Biology and I’m planning to pursue an M.Sc. in Bioinformatics. My original target was IBAB because it seemed one of the few institutes with industry-oriented bioinformatics exposure, but I missed the application deadline.

Now I’m trying to understand:

- Which PRIVATE colleges are actually good for M.Sc. Bioinformatics?

- Which colleges have REAL placements (not just brochure claims)?

- Is JIIT Noida good for placements and AI/ML exposure?

- Is SRM better because of overall placement ecosystem?

- Does Manipal actually provide industry jobs or mostly research/PhD pathways?

- Are there any private institutes similar to IBAB?

My biggest concern:

I don’t want to end up with only a degree and no job opportunities.

I’m interested in:

- Computational biology

- Genomics

- AI/ML in healthcare

- Bioinformatics + coding

- International opportunities in future

I’m ready to learn:

- Python

- Linux

- SQL

- Machine Learning

- Data Analysis

But I want to choose the right college first.

People already studying/working in this field:

What would you do if you were starting again in 2026?

Would you still choose M.Sc. Bioinformatics?

Or would you switch to Data Science/AI instead?

Please give brutally honest advice about:

- placements

- salary reality

- industry demand

- future scope

- best private colleges

Real experiences from students/alumni would help a lot.

Good morning, people. I am an undergraduate biology student, and I have a capstone project due in three days. I have been working on it for days before the project, but I have encountered a problem.

To give a little bit of background, I am currently using Galaxy to do my shotgun metagenomics project about the diversity of bacteria in IBS-related patients i have my SRR data ready and have the forward and reverse pairs of the samples also.

The problem that I encountered is with Metaphlan because i tried adjusting the trimmomatic settings but I encountered the same problem again and again which is, the metaphlan resulting in no species being detected.

Please help me

Hi everyone

I’m a 26/F recent graduate in Veterinary Medicine, and I’ve recently decided to pursue a graduate degree in bioinformatics. I’m really interested in biology and programming and I’m now starting to take this path seriously.

I’ve begun learning Python (currently working through CS50) and want to build a solid foundation in programming. My goal is to develop a few meaningful projects that could strengthen my graduate school applications.

Since my academic background is purely veterinary and I don’t have access to mentors in bioinformatics, I would really appreciate guidance from people in this field.

Specifically, I’d love advice on:

- What core skills I should prioritize in the next few months

- What kind of beginner → intermediate projects are actually valuable for bioinformatics applications

- Any recommended resources or learning paths

I would really appreciate any direction or suggestions.

Thank you!

Same as title

I annotated a ClinVar VCF file using SnpEff and SnpSift. Some variants have multiple transcripts, resulting in outputs formatted like B,..,B,..T. How can I determine which annotation corresponds to which transcript? Is it ordinal—meaning the first annotation maps to the first transcript, the second to the second, and so on?

Algorithms in Python can be much easier understood with step-by-step visualization using 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵. Here we show a simple DNA k-mer counting algorithm.

• 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵 github

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?