桥接(联网)
转录组
计算生物学
基因组
DNA测序
仿形(计算机编程)
背景(考古学)
计算机科学
基因组学
生物
数据科学
遗传学
基因
计算机网络
基因表达
古生物学
操作系统
作者
C. Yuan,Fu Xiang Quah,Martin Hemberg
标识
DOI:10.1016/j.mam.2024.101255
摘要
Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.
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