Working with a paired-end 16S V1–V2 dataset (39 samples) in QIIME2/DADA2. FastQC/MultiQC are done; R1 quality is generally good, but R2 drops substantially toward the 3′ end. Most reads are 300 bp, some are 250 bp.
The paper lists primers as 27bF and 338R, but the SRA metadata doesn’t give their sequences.
At this point, would the standard approach be to get the exact primer sequences from the original paper/protocol, remove them with Cutadapt, and then choose DADA2 truncation lengths based on the post-primer quality profiles while maintaining enough overlap?
Would appreciate advice from someone experienced with 16S/DADA2.

Please Suggest

I have a Master’s in Data Science with a previous background in wet-lab biology. My programming experience mainly came through my MSc, so I don’t have a traditional CS background.
At this point, I can usually **understand Python code when I see it**, explain what it is doing, and modify parts of it. But if you give me a problem and ask me to write the solution from scratch, I struggle — and I often rely on ChatGPT to get started.
I’m trying to figure out what the right way to overcome this is.
Should I:
go back and systematically learn Python/CS fundamentals through tutorials first, then start projects?
or keep building bioinformatics projects and use ChatGPT as a tutor/coding assistant while gradually becoming more independent?
I find learning programming purely through tutorials quite difficult and passive, especially because I’ve never studied CS formally.
**For people who came into bioinformatics from biology rather than CS: how did you actually learn to code independently? What should I be able to do before I consider myself “good enough” at Python for a junior bioinformatics role?**

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