Linganisha mbinu

	Wito wa Kilele cha ChIP-seq	Upigaji wa Vigezo
Nyanja	Bioinformatiki	Bioinformatiki
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	2007–2008	2009–2010 (modern high-throughput era)
Mwanzilishi	Johnson et al. (ChIP-seq concept, 2007); Zhang et al. (MACS algorithm, 2008)	Li et al. (SAMtools/bcftools, 2009); McKenna et al. (GATK, 2010)
Aina	Computational genomics pipeline	Computational genomics pipeline
Chanzo asilia	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.	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.
Majina mbadala	ChIP-seq analysis, peak detection, MACS peak calling, ChIP peak identification	SNP calling, genotyping from sequencing, mutation detection, variant detection
Zinazohusiana	6	6
Muhtasari	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.	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.