Sammenlign metoder

	Tidsserie-ChIP-seq-toppkalling	ATAC-seq-analyse
Fagfelt	Bioinformatikk	Genetikk
Familie	Process / pipeline	Process / pipeline
Opprinnelsesår	2008–2012 (ChIP-seq); time-series extensions ~2015–2020	2013
Opphavsperson	ENCODE Consortium; extended by Haiminen et al. and broader epigenomics community	Jason Buenrostro, Paul Giresi & William Greenleaf
Type	Computational epigenomics pipeline	Chromatin profiling method
Opprinnelig kilde	Landt, S. G., Marinov, G. K., Kundaje, A., Kheradpour, P., Pauli, F., Batzoglou, S., ... & Snyder, M. (2012). ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Research, 22(9), 1813–1831.	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.
Alias	longitudinal ChIP-seq analysis, dynamic ChIP-seq peak calling, time-course ChIP-seq, temporal chromatin profiling	Chromatin accessibility, Open chromatin, Accessible chromatin analysis
Relaterte	5	2
Sammendrag	Time-series ChIP-seq peak calling extends standard chromatin immunoprecipitation sequencing analysis to samples collected at multiple time points. By identifying and comparing protein-DNA binding peaks across a temporal dimension, the method reveals how transcription factor occupancy, histone modifications, or chromatin remodeler binding evolve during biological processes such as differentiation, circadian cycles, or stimulus response.	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.