New
Single-cell QC sandbox
Adjust gene, UMI, and mitochondrial thresholds and watch the retained cell population change live.
Practice Lab (Interactive)
Hands-on browser exercises for building intuition. Adjust parameters and watch how summaries change. The datasets are synthetic, but the reasoning patterns are the same ones you need when reading real QC reports, filtering variants, or tuning single-cell thresholds.
Exercise 1 - Phred score <-> error probability
Convert between Phred score Q and error probability p using Q = -10 log10(p). This is a small formula, but it changes how you think about sequencing quality.
Typical good Illumina bases are around Q30 or higher.
Q30 corresponds to p = 0.001.
Exercise 2 - Trimming tradeoffs
Raising the quality cutoff keeps fewer bases. Raising the minimum length drops short reads entirely. The best thresholds depend on whether you care more about preserving length or reducing low-quality sequence.
Common starting points: Q15 to Q25.
Dropping very short reads can reduce false alignments.
Exercise 3 - Variant filtering
Filtering changes both sensitivity and precision. Slide depth, genotype quality, and variant allele fraction thresholds to see how quickly the accepted call set changes.
Low depth increases false positives and genotype uncertainty.
Genotype quality is caller-specific but useful as a coarse safeguard.
Expected VAF depends on context, coverage, and biology.
More interactive lessons now available
New
Pipeline reproducibility atlas
Explore how sample sheets, references, environments, and workflow engines fit together.
New
Assembly concept explorer
Learn how reads, k-mers, repeats, and bubbles shape contigs and downstream validation.