可扩展性
计算生物学
计算机科学
核糖核酸
灵敏度(控制系统)
单细胞测序
集合(抽象数据类型)
吞吐量
生物
基因
遗传学
工程类
表型
电信
数据库
无线
外显子组测序
电子工程
程序设计语言
出处
期刊:Methods in molecular biology
日期:2019-01-01
卷期号:: 25-44
被引量:48
标识
DOI:10.1007/978-1-4939-9240-9_3
摘要
In the last few years single-cell RNA sequencing (scRNA-seq) has enabled the investigation of cellular heterogeneity at the transcriptional level, the characterization of rare cell types as well as the detailed analysis of the stochastic nature of gene expression. A large number of methods have been developed, varying in their throughput, sensitivity, and scalability. A major distinction is whether they profile only 5′- or 3′-terminal part of the transcripts or allow for the characterization of the entire length of the transcripts. Among the latter, Smart-seq2 is still considered the “gold standard” due to its sensitivity, precision, lower cost, scalability and for being easy to set up on automated platforms. In this chapter I describe how to efficiently generate sequencing-ready libraries, highlight common issues and pitfalls, and offer solutions for generating high-quality data.
科研通智能强力驱动
Strongly Powered by AbleSci AI