RNA-seq Analysis
for Absolute Beginners
A 7-session live workshop that takes you from zero command-line experience to a complete, publication-ready RNA-seq analysis — step by step, with your instructor’s hands guiding every line of code. You also leave with a full end-to-end pipeline — and a private one-on-one session afterward where we run it on your own data.
NGS data analysis is no longer optional
Whether you’re in academia or industry, the ability to analyze your own sequencing data is rapidly becoming a baseline expectation — not a bonus skill.
You understand your biology — own your data too
RNA-seq has become routine in labs of every size. No external bioinformatician knows your experimental system, your controls, or your biological hypotheses the way you do. Analyzing your own data doesn’t just save time — it generates deeper, more accurate insights.
Wet lab + dry lab = an unfair advantage
Top pharmaceutical and biotech companies actively seek researchers who can move fluidly between bench and computation. This hybrid profile is rare, commands higher salaries, and opens doors that remain closed to specialists on either side alone.
The field has moved. Don’t get left behind.
NGS data is now universal across life sciences. Labs without dedicated bioinformaticians are competing for collaborators, waiting months for results, and missing publication deadlines. Knowing how to handle this data is simply part of being a competitive researcher today.
I spent years lost in the dark.
You don’t have to.
I was a wet lab researcher in graduate school when RNA-seq was cutting-edge. My lab sequenced a non-model animal’s transcriptome — and nobody in the entire department knew how to analyze the data. I had to beg someone in the medical school for help. They took forever, and eventually told me they couldn’t do it.
That moment changed my career. I swore I would learn to analyze NGS data independently. But it wasn’t easy. I didn’t know where to start. I spent years in the dark, Googling desperately, piecing together fragments from tutorials that all assumed prerequisites I didn’t have. It took me years to find the actual learning path.
Today, I’m a computational biologist who has collaborated with hundreds of wet-lab researchers, and trained individual scientists, whole labs, and entire departments at universities and research institutes. And I see the same frustration I felt — every single week. Researchers waiting months for results. PIs unable to evaluate their own data. Students stuck before they even get started.
I built NGS101.com to help. Thousands of researchers use my tutorials every month. But email after email told me the same thing: even with detailed written tutorials, beginners still couldn’t find a clear place to start. So I built this workshop — to do what a written guide never can: walk alongside you, in real time, step by step.
Why beginners get stuck
The biology isn’t the hard part. Here’s what actually blocks researchers from getting started.
The Linux command line — before a single analysis even begins
Almost every bioinformatics tool runs in a Linux terminal. Most tutorials skip this entirely, assuming you already know it. Most beginners don’t — and get stuck immediately.
Tool installation feels like black magic
Setting up STAR, featureCounts, or Salmon from scratch involves dependency management, PATH variables, and environment configuration — all opaque to someone just trying to analyze RNA-seq data.
Personal computers simply can’t handle the compute
Genome indexing, alignment, and quantification require significant RAM and storage. These jobs need a server or HPC — and most beginners have no path to one.
A maze of file formats with no map
FASTQ, BAM, SAM, GTF, BED, VCF — knowing which tool requires which format, and how to convert between them, is genuinely confusing when you’re starting out.
Written tutorials have no “start here” arrow
Even comprehensive tutorials can overwhelm a beginner who doesn’t know which section to read first, what to skip, or how to connect the pieces into a working pipeline.
Everything designed
for the absolute beginner
Every obstacle above has a specific solution built into this workshop. Here’s how.
Pre-configured cloud environment — just log in
No installation, no local setup, no HPC account needed. I provide a ready-to-go Linux environment in the cloud. On Day 1, you’re already running real commands on real data.
Linux from zero — live, guided practice
We start with “how to open a terminal” and build up. Every command is explained, practiced, and applied in the context of bioinformatics. You’ll be fluent in the commands that actually matter.
Real data formats, demystified live
I’ll open and explain every file format you’ll encounter — FASTQ quality scores, SAM/BAM alignment files, GTF annotations — using real sequencing data, not abstract examples.
Lean R — only what you need for RNA-seq
Most R courses try to teach the whole language. We won’t. We cover exactly the R you need for differential expression analysis — nothing more, nothing less. Efficient and immediately applicable.
Ready-to-use scripts for your own data
You’ll leave with annotated, working scripts covering the complete RNA-seq pipeline — code you can run on your own data starting the day after the workshop ends.
Lifetime recordings + 1 month of email support
Every session is recorded. Re-watch any step as many times as needed. Plus one month of direct email support while you apply what you learned to your own data.
Seven sessions. One complete pipeline.
From opening a terminal for the first time to submitting your data to NCBI GEO — every step, every tool, every concept. Each session comes with a detailed workbook and its own example dataset, so you practice every skill hands-on instead of just watching.
Linux from scratch + environment setup
Your first time in the terminal — we start here. Learn to navigate the Linux command line, organize a project directory, and install bioinformatics tools using conda. By the end, the terminal feels powerful, not scary.
From raw reads to your first count matrix
Learn to read FASTQ files, interpret Phred quality scores, and explore SAM/BAM and GTF formats. Run FastQC to assess data quality, then move straight into alignment: build a STAR genome index, map your samples to the reference genome, and run featureCounts to generate the count matrix that drives all downstream analysis.
The R you actually need — nothing more
Transition from Linux to R. Learn RStudio, R data structures, and Bioconductor package management — only what’s needed for RNA-seq. Import and normalize your count matrix, and understand why raw counts can’t be compared directly.
Statistical analysis — the session you’ve been working toward
Run differential expression analysis with limma, perform PCA and sample QC, interpret logFC and adjusted p-values, and extract your DEG list. Compare DESeq2, edgeR, and limma-voom so you can choose the right tool for your data.
Publication-ready figures and biological meaning
Create volcano plots, heatmaps, PCA plots, and MA plots using ggplot2. Then translate DEGs into biology: GO enrichment, KEGG pathways, and GSEA. Build the figures and narrative that go directly into your paper.
Batch effects and complex experimental designs
Real data is messy. Learn to detect and visualize batch effects using PCA, adjust your statistical model to account for known covariates, apply ComBat for batch correction when appropriate, and design multi-factor experiments properly — including paired samples and blocking factors.
Your complete end-to-end RNA-seq pipeline + publication prep
The session that ties everything together. You receive a complete, production-ready RNA-seq pipeline and learn to run it yourself: a SLURM-ready set of HPC scripts that takes raw FASTQ files all the way to a counts table, plus a fully annotated R pipeline covering count QC, normalization, covariate correction, differential expression, visualization, and pathway enrichment. Whether you start from FASTQ files or a counts table, you can point this pipeline at your own data the day after the workshop. We then close the loop on publication — preparing metadata, walking through the NCBI GEO submission portal step by step, and leaving you with a reproducible analysis checklist for every future project.
You don’t just learn the pipeline.
You leave with it.
In Session 7 you receive the same complete, production-ready RNA-seq pipeline Dr. Guo uses in his own research — fully annotated and ready to run. Point it at your own FASTQ files or counts table and get results the day after the workshop ends.
RNAseq_Quantification_SLURM/
Six HPC-ready bash scripts that take you from raw FASTQ files to a clean gene-level counts table — built for SLURM, the job scheduler used by most university and institute clusters.
├─ 6 bash scripts · FASTQ → counts table
└─ SLURM job submission, ready to adapt
RNAseq_Analysis_Pipeline/
The complete downstream analysis as two R scripts and one R notebook — package installation, count QC, normalization, covariate correction, differential expression, visualization, and pathway enrichment.
├─ 2 R scripts · setup + analysis
└─ 1 R notebook · QC → DE → pathways
Start from raw reads or from a counts table — the pipeline meets your data wherever it is.
“But will this work on my data?”
It’s the question I get more than any other — and the reason people put off learning for another year. New in Cohort 3: after the seven sessions are finished, you and I sit down one-on-one and run the pipeline on your own dataset. Not a demo. Your genes, your samples, your results.
Before Session 1 — I look at your dataset
After you enroll, you fill out a short intake form: organism, sample count, experimental design, and what form your data is currently in. I read every one personally and tell you straight whether it’s ready for the 1:1, and what you’d need to prepare if it isn’t. No surprises later.
Sessions 1–7 — we all learn on the same data
Every session uses the example dataset I provide, so nobody gets blocked by a broken file at 8:15 pm and everyone is looking at the same screen. You don’t need a dataset of your own to take this workshop, and you don’t need to bring one to keep up.
After Session 7 — your private 1:1
Once you’ve seen the complete pipeline demonstrated end to end, every participant gets a scheduled one-on-one session with me. We open your counts table and walk the pipeline through it together — QC, normalization, differential expression, figures, pathways — on your own experiment.
Two honest limits, so nobody is surprised. The 1:1 starts from a counts table, not raw reads. Alignment and quantification need serious compute, and I can’t run your FASTQ or BAM files for you. You will learn that half of the pipeline in Sessions 1–2 and leave with SLURM-ready scripts to run on your own institution’s cluster — then bring me the counts table it produces. And this is a bulk RNA-seq workshop. If your data is single-cell, ATAC-seq, or whole-genome sequencing, the Linux, R, and statistics foundation transfers directly, but the pipeline we build isn’t the one you need. Tell me at intake and I’ll say so before you pay.
Everything you need,
nothing you don’t
One enrollment covers the full workshop experience — tools, support, and materials.
7 live sessions with Dr. Guo~2 hours each, hands-on from minute one
Pre-configured cloud environmentLog in and start coding — no local setup
Lifetime access to all recordingsRe-watch any session whenever you need
A detailed recap after every sessionVideo, scripts, datasets, workbooks, cheatsheets, and slides — straight to your inbox
Complete end-to-end RNA-seq pipelineSLURM quantification scripts + full R analysis pipeline, ready to run on your own data
A workbook + dataset for every sessionDetailed hands-on exercises with example data — you practice, not just watch
Bring your own dataA private 1:1 after Session 7 where we run the pipeline on your own counts table
1 month of email supportGet help applying the skills to your data
All slides and teaching materialsKeep them for future reference
Certificate of completionDocumenting your training hours
Can’t make every session live?
You won’t fall behind.
Travel, conflicts, time zones — they happen. You’ll have lifetime access to everything from the workshop, so you can catch up on your own schedule.
Miss a session? Catch up on your own schedule with the session recap email — no pressure. Many participants find the recordings most useful after the workshop ends, when they’re applying the pipeline to their own data and want to revisit specific steps.
Dr. Lei Guo
Computational Biologist & Bioinformatics Educator · Founder, NGS101.com
Dr. Guo is a computational biologist with over a decade of experience in genomic data analysis, and a researcher-turned-educator who has made it his mission to demystify bioinformatics for the life science community.
He has trained individual researchers, entire labs, and whole departments at universities and research institutes — most of them wet-lab scientists who had never opened a terminal before they started.
As the founder of NGS101.com, Dr. Guo has built a library of 80+ in-depth tutorials covering RNA-seq, single-cell analysis, epigenetics, Hi-C, ATAC-seq, DNA methylation, and whole-genome/whole-exome sequencing (WGS/WES) — helping thousands of researchers worldwide analyze their own NGS data every month.
His teaching philosophy is fire before aim. You don’t need to master Bash, Python, R, and statistics before you’re allowed to touch your data. That path keeps beginners aiming forever and never firing. You run a real pipeline in Session 1, and learn each piece in context, at the moment you actually need it — lean learning, only what you need, nothing you don’t. And no step is too small to explain.
What participants said
Cohort 1 has completed the full workshop. Here is what participants had to say, in their own words.
This class was incredibly informative and comprehensive when it came to learning how to analyze RNA-seq data. I had some experience with Linux and R prior to joining the workshop, but it was an amazing refresher and included many new things I had not known before. The ability to work through the exercises at my own pace after each live session was immensely helpful and led me to absorb a lot more information than I was expecting. I would highly recommend this workshop to anyone who is interested in learning how to analyze their own data but doesn’t know where to start.
When I first joined this workshop, I had only used the Linux command line a couple of times and also only possessed minimal experience with the R programming language, both of which are crucial to becoming a successful bioinformatician. However, my understanding in both of these areas, as well as various broader concepts regarding working with datasets, have been significantly improved following the completion of this workshop, and I am now equipped with a solid foundation to move forward with processing my own RNA-seq dataset.
Dr. Lei Guo’s exceptional ability to break down the various complicated concepts into very detailed, simplified explanations at each and every step was something I found to be priceless — and something I felt was lacking when I had tried asking other bioinformaticians for help in the past. Overall, I cannot recommend this workshop enough to beginner and novice bioinformaticians looking for an effective way to develop a proper and strong understanding of bioinformatics data analysis pipelines.
Stop waiting for a bioinformatician.
Become one.
Cohort 3 enrollment is open. Cohort size is intentionally limited, so every participant gets real attention, real feedback, and a real learning experience.
Session time may be adjusted based on the time zones of enrolled participants.
⚠ Registration closes September 5, 2026 · Limited seats remaining
Group rates available for 3+ participants from the same institution. Questions? Contact Dr. Guo directly.