ArticleBMC genomics2016
ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences.
Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Biology-informed risk stratification of glioblastoma by integrating MRI-based intratumoral heterogeneity with clinical features: a multicenter validation study.Experimental hematology & oncology · 2026Article
- Revisiting differential expression analysis: An updated six-dimensional comparative study.PloS one · 2026Article
- Renal ischemia alters the mRNA and miRNA profile of vasculature-related genes in scattered tubular-like cells from female pigs.American journal of physiology. Renal physiology · 2025Article
- Identifying Hub Genes and miRNAs Associated with Alzheimer's Disease: A Bioinformatics Pathway to Novel Therapeutic Strategies.Biomolecules · 2024Article
- Systematic characterization of the HOXA9 downstream targets in MLL-r leukemia by noncoding CRISPR screens.Nature communications · 2023Article
- Epigenetic activation of the FLT3 gene by ZNF384 fusion confers a therapeutic susceptibility in acute lymphoblastic leukemia.Nature communications · 2022Article
- Fibromine is a multi-omics database and mining tool for target discovery in pulmonary fibrosis.Scientific reports · 2021Article
- Increased Autotaxin Levels in Severe COVID-19, Correlating with IL-6 Levels, Endothelial Dysfunction Biomarkers, and Impaired Functions of Dendritic Cells.International journal of molecular sciences · 2021Article
- Effector and regulator: Diverse functions of C. elegans C-type lectin-like domain proteins.PLoS pathogens · 2021Article
- dearseq: a variance component score test for RNA-seq differential analysis that effectively controls the false discovery rate.NAR genomics and bioinformatics · 2020Article
- The C. elegans GATA transcription factor elt-2 mediates distinct transcriptional responses and opposite infection outcomes towards different Bacillus thuringiensis strains.PLoS pathogens · 2020Article
- mitch: multi-contrast pathway enrichment for multi-omics and single-cell profiling data.BMC genomics · 2020Article
- Massively parallel variant characterization identifiesProceedings of the National Academy of Sciences of the United States of America · 2020Article
- TopControl: A Tool to Prioritize Candidate Disease-associated Genes based on Topological Network Features.Scientific reports · 2019Article
- aFold - using polynomial uncertainty modelling for differential gene expression estimation from RNA sequencing data.BMC genomics · 2019Article
- The Inducible Response of the NematodeFrontiers in microbiology · 2019Article
- Identification of placental nutrient transporters associated with intrauterine growth restriction and pre-eclampsia.BMC genomics · 2018Article
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3 authors.
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Abstract
backgroundThe recent advances in next generation sequencing technology have made the sequencing of RNA (i.e., RNA-Seq) an extemely popular approach for gene expression analysis. Identification of significant differential expression represents a crucial initial step in these analyses, on which most subsequent inferences of biological functions are built. Yet, for identification of these subsequently analysed genes, most studies use an additional minimal threshold of differential expression that is not captured by the applied statistical procedures.
resultsHere we introduce a new analysis approach, ABSSeq, which uses a negative binomal distribution to model absolute expression differences between conditions, taking into account variations across genes and samples as well as magnitude of differences. In comparison to alternative methods, ABSSeq shows higher performance on controling type I error rate and at least a similar ability to correctly identify differentially expressed genes.
conclusionsABSSeq specifically considers the overall magnitude of expression differences, which enhances the power in detecting truly differentially expressed genes by reducing false positives at both very low and high expression level. In addition, ABSSeq offers to calculate shrinkage of fold change to facilitate gene ranking and effective outlier detection.
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