It's DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR

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csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows (2015, methodology): “Assessing DB windows. csaw uses the quasi-likelihood functionality (9,18) of the edgeR package (10) to assess DB between treatment conditions for each window.”
From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline (2016, methodology): “The NB model can be extended with quasi-likelihood (QL) methods to account for gene-specific variability from both biological and technical sources [7, 12].”
Root microbiota drive direct integration of phosphate stress and immunity (2017, positive): “We used the edgeR package 40 to fit a quasi-negative-binomial-GLM model with the glmQLFit function, and significance was tested with the glmQLFtest function 55 .”
edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets (2024, neutral): “The second major revision was the introduction of quasi-likelihood (QL) methods in January 2012 [ 10 , 19–21 ].”