Σύγκριση μεθόδων

	Ανάλυση Hi-C	Ανάλυση ATAC-seq
Πεδίο	Γενετική	Γενετική
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2009	2013
Δημιουργός	Erez Lieberman-Aiden & Job Dekker	Jason Buenrostro, Paul Giresi & William Greenleaf
Τύπος	Chromatin interaction method	Chromatin profiling method
Θεμελιώδης πηγή	Lieberman-Aiden, E., van Berkum, N. L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., & Dekker, J. (2009). Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science, 326(5950), 289–293.	Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013). Transposition of native chromatin for fast and sensitive epigenomic profiling of cell populations and tissues. Nature Methods, 10(12), 1213–1218.
Εναλλακτικές ονομασίες	Chromosome conformation capture, 3D genome, Chromatin contact mapping	Chromatin accessibility, Open chromatin, Accessible chromatin analysis
Συναφείς	2	2
Σύνοψη	Hi-C (High-Chromosome Conformation Capture) is a technique and associated computational methods for mapping the 3D architecture of the genome within cells. Developed by Lieberman-Aiden and Dekker in 2009, Hi-C identifies physical interactions between genomic regions that may be distant in linear sequence but spatially proximal in 3D nuclear space. Hi-C analysis has revealed fundamental principles of genome organization, including the existence of topologically associating domains (TADs), and provides insights into how 3D structure regulates gene expression and DNA replication.	ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) is a method for profiling the landscape of chromatin accessibility genome-wide. Developed by Buenrostro and colleagues in 2013, ATAC-seq uses hyperactive transposase to tag open, accessible chromatin regions, enabling rapid and sensitive identification of regulatory DNA elements. ATAC-seq has become a standard technique for characterizing gene regulatory landscapes, discovering cell-type-specific regulatory elements, and inferring gene regulatory networks.