When I started my journey into microbiome research, I was completely new to the field. I had no clue what I was doing. Zilch. Nada. But I was determined to learn—not just the science, but also the bioinformatics involved. So, like any eager newbie, I followed every tutorial I could find. And guess what? They worked beautifully…on their sample data. Then came the moment of truth: using my own data. That’s when the real struggle began. Steps that seemed crystal clear with example datasets turned into a tangled mess with my files. Suddenly, “easy” tutorials became confusing, frustrating, and downright maddening. To be fair, I don’t blame the authors of those tutorials. Maybe—just maybe—they wrote them assuming their readers already had some background in bioinformatics and coding. Perhaps they never imagined a total newbie like me would be bold (or crazy) enough to give them a try!
This guide is the result of countless trials, missteps, and ultimately, breakthroughs. It’s designed to help you harness the power of R for 16S rRNA data analysis, bringing together an arsenal of incredible packages like microeco, metacoder, and microbiotaprocess (among others). The pipeline I’ve built isn’t just a collection of steps—it’s a refined roadmap, pieced together from multiple online tutorials, each offering valuable insights but often leaving frustrating gaps. I’ve filled in those blanks, streamlined the process, and created a workflow that actually works—especially when you’re working with your own data and aiming for manuscript-ready results!
Disclaimer;
Here’s the deal: this pipeline is for research purposes only. It’s not a magic wand, and I can’t promise perfect results. You’re still responsible for checking your data quality and interpreting your results based on your research goals. This guide is offered “as is,” and I can’t be held liable for errors or unexpected outcomes. If your research is critical, double-check everything and consult an expert if needed.
Why I Wrote This Guide;
Let’s be real: no one should have to spend hours wrestling with data prep and formatting before getting to the actual analysis. That’s exactly why I wrote this guide: to save you time, frustration, and maybe even a little sanity. If you’re planning to use the DADA2 pipeline, followed by a powerhouse mix of packages for downstream analysis, this guide will walk you through the setup step by step. That way, you can skip the headaches and dive straight into the exciting part—analyzing your microbiome data!