Salīdzināt metodes

	RNA ātrums	Hi-C analīze
Nozare	Ģenētika	Ģenētika
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2018	2009
Autors	Gioele La Manno & Pavel Soldatov	Erez Lieberman-Aiden & Job Dekker
Tips	Trajectory inference method	Chromatin interaction method
Pirmavots	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.	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.
Citi nosaukumi	Velocity analysis, Transcriptomic velocity, Cell fate prediction	Chromosome conformation capture, 3D genome, Chromatin contact mapping
Saistītās	2	2
Kopsavilkums	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.	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.