Process / pipelineBioinformatics / omics
RNA-seq Differential Expression — Transcriptomic DE Analysis
RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values.
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Sources
- Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. DOI: 10.1186/s13059-014-0550-8 ↗
- Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139–140. DOI: 10.1093/bioinformatics/btp616 ↗
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Bayesian eQTL analysisBayesian Gene Set Enrichment AnalysisBayesian Metabolomics AnalysisBayesian Proteomics AnalysisBayesian RNA-seq differential expressionBayesian Sequence AlignmentBayesian Variant CallingChIP-seq Peak CallingCopy Number Variation AnalysisDifferential ChIP-seq peak callingDifferential Epigenome-Wide Association StudyDifferential eQTL AnalysisDifferential Metabolomics AnalysisDifferential pathway enrichment analysisDifferential single-cell RNA-seq analysisDifferential Variant CallingeQTL AnalysisGene Set Enrichment AnalysisGenome-wide association studyMachine learning-assisted ChIP-seq peak callingMachine learning-assisted expression quantitative trait loci analysisMachine learning-assisted gene set enrichment analysisMachine learning-assisted microbiome diversity analysisMachine learning-assisted RNA-seq differential expressionMachine learning-assisted single-cell RNA-seq analysisMetabolomics analysisMulti-omics eQTL analysisMulti-omics gene set enrichment analysisMulti-omics metabolomics analysisMulti-omics proteomics analysisMulti-omics single-cell RNA-seq analysisNetwork-based epigenome-wide association studyNetwork-based eQTL analysisNetwork-based gene set enrichment analysisNetwork-based microbiome diversity analysisNetwork-based RNA-seq differential expressionNetwork-based single-cell RNA-seq analysisNetwork-based variant callingPathway Enrichment AnalysisPhylogenetic AnalysisProteomics AnalysisSequence AlignmentSingle-cell eQTL analysisSingle-cell Gene Set Enrichment AnalysisSingle-cell GWASSingle-cell RNA-seq analysisSingle-cell RNA-seq differential expressionSingle-cell sequence alignmentTime-series ChIP-seq peak callingTime-series copy number variation analysisTime-series Epigenome-wide Association StudyTime-series gene set enrichment analysisTime-series microbiome diversity analysisTime-series pathway enrichment analysisTime-series phylogenetic analysisTime-series proteomics analysisTime-series RNA-seq differential expressionTime-series single-cell RNA-seq analysisTime-series variant callingVariant Calling