方法对比

	贝叶斯 RNA-seq 差异表达	变异检测
领域	生物信息学	生物信息学
方法族	Process / pipeline	Process / pipeline
起源年份	2010–2013	2009–2010 (modern high-throughput era)
提出者	Kendziorski et al. (EBSeq); Hardcastle & Kelly (baySeq)	Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)
类型	Bayesian statistical inference pipeline	Computational genomics pipeline
开创性文献	Leng, N., Dawson, J. A., Thomson, J. A., Ruotti, V., Rissman, A. I., Smits, B. M., Haag, J. D., Gould, M. N., Stewart, R. M., & Kendziorski, C. (2013). EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics, 29(8), 1035–1043.	McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., ... & DePristo, M. A. (2010). The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Research, 20(9), 1297–1303.
别名	Bayesian DE analysis, Bayesian RNA-seq DE, baySeq, EBSeq	SNP calling, genotyping from sequencing, mutation detection, variant detection
相关	6	6
摘要	Bayesian RNA-seq differential expression analysis applies hierarchical Bayesian models to RNA sequencing read-count data to identify genes whose expression levels differ significantly between biological conditions. Rather than relying solely on p-values, these methods quantify the posterior probability that a gene is differentially expressed, borrowing statistical strength across genes and naturally accommodating low sample sizes common in genomics experiments.	Variant calling is the computational process of identifying positions in a sequenced genome that differ from a reference sequence — including single nucleotide polymorphisms (SNPs), small insertions and deletions (indels), and structural variants. It transforms aligned sequencing reads into an interpretable catalogue of genetic differences, forming the foundation for population genetics, disease-gene discovery, and clinical genomics applications.