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Omics data analysis pipelines convert raw high-throughput measurements from genomics, transcriptomics, epigenomics, proteomics, metabolomics, or single-cell assays into quality-controlled, statistically tested, biologically interpretable results. A reproducible omics pipeline requires predefined experimental metadata, raw-data quality control, modality-specific preprocessing, normalization, statistical modeling, multiple-testing correction, biological annotation, and independent validation. RNA-seq pipelines commonly include read QC, alignment or pseudoalignment, quantification, normalization, and differential-expression testing, while single-cell pipelines additionally require cell-level QC, normalization, dimensionality reduction, clustering, cell annotation, and sample-aware differential testing. Multi-omics integration can connect molecular layers such as transcriptome, proteome, metabolome, and epigenome, but unresolved problems include batch effects, missing values, unequal featu