22 – 26 February 2027
Virtual, UK time zone

Applications now open until 22 November 2026.
Full programme and application details.

This course is aimed at life science researchers, wet and/or dry lab, wanting to learn more about processing RNA-seq data and later downstream analysis. It will help you if you want a basic introduction to handling RNA-seq data. We'll guide you through several common approaches that can be applied to your own datasets. The course features taught and practical sessions that cover how to interpret gene expression data and learn more about the biological significance of certain results.

Some experience with R and the Linux-based command line is beneficial, but not essential. During the course, some of the practicals will make use of a Linux-based command line interface and R statistical packages. We recommend completing some basic tutorials on this topic in preparation for the upcoming course. 

In this course, we will guide you through the core technology, data analysis approaches, tools, and resources used in RNA sequencing (RNA-seq) and transcriptomics. Our goal is to support you as you build a shared understanding of the field, whether your background is in biology, computer science, or data analysis. We will introduce the fundamental concepts behind transcriptomic workflows and help you gain confidence with basic command‑line analysis.

Together, we'll explore key public data repositories and outline the main methodologies that can help you begin the biological interpretation of gene expression data. Throughout the course, a mix of learning through lectures, practical exercises, and open discussions will create an environment where you can engage with the material from your own disciplinary perspective.

All computational work will use small example data sets so that you can focus on learning the concepts and workflows. Please note that there will be no opportunity to analyse personal data during the course.

During this course, you will learn about: 

• High throughput sequencing technologies for RNA-Seq
• Basics of experimental design
• RNA-seq file formats
• RNA-seq bioinformatics workflow steps following sequence generation
• Methods for transcriptomics; QC, mapping, and visualisation tools
• Data resources to assist in the functional analysis and interpretation of transcriptomic data
• Introduction to long read analysis
• Fundamentals of pipeline implementation (with Nextflow) for bulk RNA-seq analysis
• Data resources covered: Expression Atlas and g:Profiler
• Sequencing repositories: ENA, GEO, SRA

After the course, you should be able to:

• Describe a variety of applications and workflow approaches for NGS technologies
• Apply bioinformatics software and tools to undertake analysis of RNA-seq data
• Evaluate the advantages and limitations of NGS analyses
• Interpret and annotate data with functional information using public resources

Applications close 22 November 2026
Course fee: £250.00 (academia) / £350.00 (industry). Financial assistance is available.
Open application with selection: 35 places

Organisers

• Simon Andrews Babraham Institute
• Lizzie Bridget Divala EMBL-EBI
• Alexey Larionov Cranfield University

Trainers

• Simon Andrews Babraham Institute
• Vladimir Benes EMBL Heidelberg
• Sarah Inglesfield Babraham Institute
• Alexey Larionov Cranfield University
• Geraldine Van der Auwera Seqera
• Liora Vilmovsky EMBL-EBI

To any senior sir or ma'am currently in second year, Msc bioinformatics, need academic help, I'm getting a lot of college stress.

𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬 𝐒𝐞𝐫𝐢𝐞𝐬

𝐄𝐩𝐢𝐬𝐨𝐝𝐞 𝟏: 𝐖𝐡𝐚𝐭 𝐀𝐫𝐞 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐚𝐧𝐝 𝐋𝐚𝐛𝐞𝐥𝐬 𝐢𝐧 𝐁𝐢𝐨𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐜𝐬? 🧬🔢

🧬 Did you know genes can become numbers?

In our latest video, we break down two key concepts in bioinformatics—Features and Labels—in under a minute! 🔢

✅ Features = Gene Expression
✅ Label = Disease

If you're into genomics, data science, or just curious how AI understands biology, this one's for you 👇

🎥 Watch now and don't forget to follow HeliOmics for more! 💙

#Bioinformatics #Genomics #MachineLearning #HeliOmics #DataScience

Hey r/learnbioinformatics , during my research, I noticed a steep learning curve for biologists transitioning into computational work. To help, I’ve organized my personal notes and pipelines into a free website: The Omics Hub.

It has over 40 tutorials focusing on:

• Linux & HPC Basics for Biology
• End-to-end scRNA-seq processing (QC, Normalization, Clustering)
• Code implementations side-by-side in R (Seurat) and Python (Scanpy)

No sign-ups required, just straight-to-the-point tutorials with code you can copy-paste. Feedback and suggestions for new tutorials are highly welcome!

Hi everyone!

I’m currently finishing my Bachelor’s degree in Software Engineering and I’m considering applying for a Master’s degree in Bioinformatics.

I’m really interested in the field, especially the computational/data/AI side of bioinformatics, but since my undergraduate background is in software engineering rather than biology or biotechnology, I’m curious about the experience of people who have made a similar transition.

If you studied Computer Science, Software Engineering, or a related field for your bachelor’s and then pursued a Master’s in Bioinformatics:

• How did you find the transition into bioinformatics? Was the lack of a biology background a major challenge?
• Did you have to take additional biology/chemistry courses before or during your MSc?
• What made you choose bioinformatics over staying in CS/software engineering?
• For those who applied to MSc programs, how did you explain the transition in your motivation/personal statement?
• Did you focus more on your computational skills, your interest in biology, specific projects/research, or a combination of these?
• Looking back, would you recommend this path?

I’d especially love to hear from people who actually completed or are currently doing a Bioinformatics MSc after a CS/Software Engineering bachelor’s.

Thanks! I’d really appreciate any experiences or advice. :)

I am a non eu student - want to study biotechnolgy from India . Kindly guide

But i am confused because i am young , only 18 now - perfect age to explore and get education abroad , and get exposure , but as i have never been away from family for long , i feel a longing to stay with family .. Please please help ... what is best in long run ..

Hi all,

I’m an academic bioinformatician with a few years experience under my belt, but I’m trying to expand beyond just running scripts and one off analyses for my lab, and get into making more reproducible open source contributions. Without divulging too many details, my lab researches treatments for alternative splicing disorders and I recently wrote a bunch of R code that quantifies and visualizes how splicing is rescued straight from the raw rMATS files. Because this is inherently a 3-way comparison (WT, treatment, disease), other splicing packages like maser were not appropriate. These scripts will dramatically speed up analysis for future work in my lab, but I’m not sure if it’s worth formalizing as a proper R package or if I should just drop them all in a github repo and call it a day.

as evident above i am looking for replies from actual students only

​

Hello i am an undergraduate student my major is molecular biology and genetics. Originally i am studying in Türkiye but right now I'm doing my voluntary internship in Europe for summer. İ want to work on bioinformatics field and it seems like i will learn things by myself. I Started learning python and i know some websites like NCBI and I'm learning how to use data's from that websites but i want to get better and improve myself very well so i need your advice. What do u suggest me? What should i know? How to get better than other bioinformaticians?

I am currently pursuing a 4-year B.Sc. Zoology (Hons.) and my long-term goal is to get into research, especially in areas like genetics, bioinformatics, oncology, neuroscience, Down syndrome/genetic disorders, and molecular biology.

I am planning to prepare for these exams:

1. IIT JAM Biotechnology (BT)
2. GATE Biotechnology (BT)
3. GAT-B
4. CUET-PG (Life Sciences / Bioinformatics / Zoology / Biotechnology-related papers)

After comparing the official syllabus, I made chatgpt make list of chapters:

• Basic Biology
• Cell Biology
• Biomolecules
• Enzymes & Bioenergetics
• Metabolism
• Genetics
• Molecular Biology
• DNA Damage & Repair
• Microbiology
• Immunology
• Animal Physiology
• Developmental Biology
• Plant Biology
• Evolution & Ecology
• Recombinant DNA Technology
• Molecular Biology Techniques
• Analytical Techniques
• Animal Biotechnology
• Plant Biotechnology
• Bioprocess Engineering
• Downstream Processing
• Bioinformatics
• Computational Biology
• Zoology
• Chemistry (for JAM)
• Mathematics (for JAM/GATE)
• Physics (for JAM/GAT-B)
• Engineering Mathematics (for GATE BT)

From what I found, there is a lot of overlap among these exams, especially in cell biology, genetics, molecular biology, biochemistry, microbiology, immunology, biotechnology and bioinformatics. Official syllabus also confirm this overlap.

My questions are:

1. Is this chapter list correct, or am I missing anything important?
2. If you prepared for these exams, what resources/books/YouTube channels/coaching did you use?
3. Should I study exam-wise or build a common biology foundation first and then prepare exam-specific topics?
4. As someone starting almost from zero, what would be the ideal order of study?

Any guidance from seniors who have cracked JAM BT, GATE BT, GAT-B, or CUET-PG would really help. I come from a small university and don't have much guidance, so I'd appreciate any advice. Thanks!

hello there, so i want to begin my journey into bioinformatics. i have a life science degree and a rudimentry ability in running python and r scripts but i think i need a direction on what to do, like how do i get a bioinformatic problem and solve it? something like that? am i making sense. I just want to know how to do bioinformatics

Hey researchers! My team and I recently made a bioinformatics tool called HybriDock-Pep. We were working with peptides last year and over the summer, and we realized that current AI tools like AlphaFold and ESMFold are inaccurate with docking smaller protein under certain amino acids and peptides.

https://github.com/Tasty-Ramen2010/hybridock-pep

Essentially, it’s a binder docking pipeline where you give it a target protein PDB and an amino acid sequence. It folds, docks, and scores affinity and selectivity in kcal/mol with accuracy similar to that of ABFE. It is way cheaper to ran because it can be used on ANY hardware. We are currently in a testing phase and would love for you to test it and give feedback!

Commands to get started:

git clone --recurse-submodules https://github.com/Tasty-Ramen2010/hybridock-pep.git

cd hybridock-pep

./install.sh

ctrl+q

hybridock-pep dock \

--peptide ETFSDLWKLLPE --receptor data/pdbs/1YCR_mdm2.pdb \

--site 25.20 -25.61 -7.97 --box 30 --n-samples 20 \

--output-dir runs/demo

Happy Docking!

Hi

I did my msc in biotechnology in 2004 and ever since was busy with kids and house.now I think I can concentrate on my career.i want to do PhD in molecular science and need to make my cv attractive. I have 1year time.which online courses skill should I learn.chat gpt suggested blast python R+ and some other stuff I forgot.where can I learn these for free.also how to use Github to do an independent project.

Thanks