השוואת שיטות

	קריאת שיאים ב-ChIP-seq של תא בודד	מיפוי רצפי תא בודד
תחום	ביואינפורמטיקה	ביואינפורמטיקה
משפחה	Process / pipeline	Process / pipeline
שנת המקור	2019	2013–2016
הוגה השיטה	Grosselin et al.; Ku et al. (parallel independent development)	Adapted from bulk RNA-seq aligners; single-cell extensions by Dobin et al. (STAR) and 10x Genomics Cell Ranger team
סוג	Epigenomic profiling pipeline	Computational pipeline step
מקור מכונן	Grosselin, K., Durand, A., Marsolier, J., Poitou, A., Marangoni, E., Nemati, F., ... & Vallot, C. (2019). High-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer. Nature Genetics, 51(6), 1060-1066.	Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013). STAR: ultrafast universal RNA-seq aligner. Bioinformatics, 29(1), 15–21.
כינויים	scChIP-seq peak calling, single-cell chromatin profiling, scChIC-seq analysis, single-cell epigenomic peak detection	scRNA-seq alignment, single-cell read mapping, scSeq alignment, cell barcode-aware alignment
קשורות	5	1
תקציר	Single-cell ChIP-seq peak calling is a bioinformatics pipeline that identifies genomic regions enriched for histone modifications or transcription factor binding in individual cells. By profiling chromatin states at single-cell resolution, it reveals epigenomic heterogeneity hidden in bulk ChIP-seq experiments, enabling researchers to map regulatory landscapes across distinct cell populations within a complex tissue sample.	Single-cell sequence alignment is the computational step that maps millions of short sequencing reads produced by single-cell RNA-seq experiments back to a reference genome or transcriptome. Unlike bulk RNA-seq alignment, each read carries a cell barcode and a Unique Molecular Identifier (UMI) that together identify the originating cell and the individual RNA molecule. Accurate alignment and barcode demultiplexing are prerequisites for constructing the cell-by-gene count matrix that drives all downstream single-cell analyses.