This tutorial site is designed to help learners move beyond copy-pasting commands. Each section explains what the files mean, why the metrics change, where errors enter a workflow, and how to report results responsibly.
| File | Role |
|---|---|
FASTQ | Raw reads with qualities |
BAM/CRAM | Reads placed on a reference |
VCF | Observed sequence variation |
GTF/GFF | Gene/transcript models |
Matrix | Gene/cell/sample summaries for statistics |
Click a stage to see what question it answers, what file usually comes out, and which later lesson builds on it. This gives you a visual map of the entire learning path.
Every workflow begins with validating what you actually received: correct sample sheet, consistent filenames, paired-end structure, and the right reference files. Many downstream mistakes are really metadata mistakes.
Continue with Data formats and Getting started.
At this stage you ask whether the data are usable, contaminated, adapter-rich, low complexity, or damaged. The goal is not to beautify reads-it is to make principled preprocessing decisions.
Continue with Read QC & Trimming.
Once reads are clean enough, you either place them on a reference or reconstruct longer sequences from the data. The right choice depends on the biological question and the availability of trustworthy references.
Mapped reads become counts, variants, transcripts, taxa, or structural evidence. This is where raw sequence data start turning into data tables suitable for modeling and interpretation.
Continue with Variant calling, RNA-seq, and Metagenomics.
Statistics does not replace biological reasoning. The last step is explaining what the outputs mean, what assumptions were made, and which limitations still matter.
Use Resources & Practice to structure reports and keep learning.
Start with formats, QC, alignment, and variant calling so you learn the core language of modern sequencing analysis.
Getting started -> Data formats -> QC -> Alignment -> Variant calling
If your goal is gene regulation, treatment response, or cell states, focus on RNA-seq and single-cell interpretation after mastering QC basics.
QC -> RNA-seq -> Single-cell RNA-seq
For lab infrastructure, focus on project structure, workflow engines, versioning, and reporting so analyses remain reusable and auditable.
Trimmed read distributions help you see whether preprocessing removed only a tail of low-quality bases or whether a large fraction of reads was heavily shortened.
GC shifts can indicate contamination, protocol bias, or expected organism-specific composition. The lesson is not the shape alone, but whether it matches your expectation.
This overview helps learners understand why workflow engines and checkpointing matter. Expensive steps deserve clear logs, versioning, and reusable outputs.
# Inspect FASTQ header patterns
zcat sample_R1.fastq.gz | head
# Count reads (4 lines per read)
expr $(zcat sample_R1.fastq.gz | wc -l) / 4
# Look at BAM summary metrics
samtools flagstat sample.bam
# Inspect VCF header and first records
bcftools view -h sample.vcf.gz | head
bcftools view -H sample.vcf.gz | head