পদ্ধতির তুলনা করুন

	হাই-সি বিশ্লেষণ	আরএনএ ভেলোসিটি
ক্ষেত্র	বংশগতিবিদ্যা	বংশগতিবিদ্যা
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2009	2018
প্রবর্তক	Erez Lieberman-Aiden & Job Dekker	Gioele La Manno & Pavel Soldatov
ধরন	Chromatin interaction method	Trajectory inference method
মৌলিক উৎস	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.	La Manno, G., Soldatov, R., Zeisel, A., Braun, E., Hochgerner, H., Petukhov, V., & Merad, M. (2018). RNA velocity of single cells. Nature, 560(7737), 494–498.
অপর নাম	Chromosome conformation capture, 3D genome, Chromatin contact mapping	Velocity analysis, Transcriptomic velocity, Cell fate prediction
সম্পর্কিত	2	2
সারসংক্ষেপ	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.	RNA velocity is a computational method that infers the future developmental state of individual cells from single-cell RNA-sequencing data. Developed by La Manno and colleagues in 2018, RNA velocity analysis measures the direction and pace of cell state transitions by analyzing the ratio of unspliced to spliced mRNA transcripts within individual cells. This enables prediction of cell trajectories and differentiation pathways without requiring temporal sampling or manipulation, providing unique insights into cell fate decisions during development and disease.