- Published on
An honest head-to-head comparison of DESeq2 and edgeR. Learn the differences in their normalization, statistical tests, and run-times, and see if they actually give different biological answers.
All Posts
All Posts
- bioinformatics (32)
- how-to (25)
- r (18)
- r-series (18)
- crispr (17)
- gene-editing (14)
- beginner (14)
- crispr-series (13)
- tool-review (10)
- intermediate (10)
- rna-seq (9)
- molecular-biology (4)
- learning (4)
- ai (3)
- sequencing (3)
- explainer (3)
- ngs (2)
- free-tools (2)
- biology (2)
- protein-design (1)
- esm3 (1)
- alphafold3 (1)
- trends (1)
- protein-structure (1)
- bash (1)
- command-line (1)
- lab-software (1)
- scientific-illustration (1)
- conda (1)
- synthetic-biology (1)
- bioelectronics (1)
- nanotechnology (1)
- drug-mechanism (1)
- metabolism (1)
- off-target (1)
- reviewing (1)
- bench-biologist (1)
- nanopore (1)
- pacbio (1)
- statistics (1)
- pcr (1)
- python (1)
- drug-discovery (1)
- rnaseq (1)
- career (1)
- Published on
A practical guide to designing a cost-effective and accurate amplicon next-generation sequencing (NGS) strategy to quantify CRISPR editing efficiency and characterises indels.
- Published on
A step-by-step practical guide to performing differential gene expression analysis in R using DESeq2. Learn how to load counts, run the analysis, and interpret your results table.
- 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
Before you run DESeq2, you need to understand what those numbers in your count matrix actually represent. This post explains what RNA-seq count data is, why raw counts are misleading, and exactly what format DESeq2 expects.