เปรียบเทียบวิธี

	การเรียกหาตำแหน่งสูงสุดของ ChIP-seq	การแสดงออกแตกต่างกันของ RNA-seq
สาขาวิชา	ชีวสารสนเทศศาสตร์	ชีวสารสนเทศศาสตร์
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2007–2008	2008–2010 (RNA-seq DE methodology established)
ผู้ริเริ่ม	Johnson et al. (ChIP-seq concept, 2007); Zhang et al. (MACS algorithm, 2008)	Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010)
ประเภท	Computational genomics pipeline	Quantitative genomics pipeline
แหล่งต้นตำรับ	Zhang, Y., Liu, T., Meyer, C. A., Eeckhoute, J., Johnson, D. S., Bernstein, B. E., Nusbaum, C., Myers, R. M., Brown, M., Li, W., & Liu, X. S. (2008). Model-based analysis of ChIP-seq (MACS). Genome Biology, 9(9), R137.	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.
ชื่อเรียกอื่น	ChIP-seq analysis, peak detection, MACS peak calling, ChIP peak identification	RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA
ที่เกี่ยวข้อง	6	6
สรุป	ChIP-seq peak calling is a computational pipeline that identifies genomic regions where a protein of interest — a transcription factor or histone modification — is enriched, based on sequencing reads from chromatin immunoprecipitation experiments. It converts raw sequencing data into a set of high-confidence binding or modification sites across the genome, enabling downstream analysis of gene regulation, chromatin state, and epigenetic mechanisms.	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.