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A practical guide to designing a cost-effective and accurate amplicon next-generation sequencing (NGS) strategy to quantify CRISPR editing efficiency and characterises indels.
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- Published on
Run DESeq2 on a real RNA-seq experiment, start to finish: load the airway dataset, filter low counts, model paired donors, and read the results table without fooling yourself.- Published on
T7E1 tells you if CRISPR worked. TIDE and ICE tell you how well. This post explains how both tools deconvolve your Sanger sequencing trace into a real editing percentage — no NGS required.- Published on
Mismatch-cleavage assays are the quickest and cheapest way to see if your CRISPR edit actually worked. This guide breaks down the mechanism of T7E1 and Surveyor nucleases, why the "denature and re-anneal" step is critical, and how to turn gel bands into an editing percentage.- Published on
We’ve covered downloading data, normalization, and visualization. Now, we put it all together. This capstone post walks through a complete end-to-end analysis of a public breast cancer dataset (GSE183947) — from raw GEO download to identifying differentially expressed genes and creating a publication-ready volcano plot.
