Daniel Hebenstreit's Research Group University of Warwick, CV4 7AL Coventry, UK.
Physics Department, University of Warwick, CV4 7AL Coventry, UK.
Brief Bioinform. 2021 Nov 5;22(6). doi: 10.1093/bib/bbab148.
RNA-seq, including single cell RNA-seq (scRNA-seq), is plagued by insufficient sensitivity and lack of precision. As a result, the full potential of (sc)RNA-seq is limited. Major factors in this respect are the presence of global bias in most datasets, which affects detection and quantitation of RNA in a length-dependent fashion. In particular, scRNA-seq is affected by technical noise and a high rate of dropouts, where the vast majority of original transcripts is not converted into sequencing reads. We discuss these biases origins and implications, bioinformatics approaches to correct for them, and how biases can be exploited to infer characteristics of the sample preparation process, which in turn can be used to improve library preparation.
RNA-seq，包括单细胞 RNA-seq (scRNA-seq)，存在灵敏度不足和缺乏精确性的问题。因此，(sc)RNA-seq 的全部潜力受到限制。在这方面的主要因素是大多数数据集存在全局偏差，这会以长度依赖的方式影响 RNA 的检测和定量。特别是，scRNA-seq 受到技术噪声和高辍学率的影响，其中绝大多数原始转录物没有转化为测序reads。我们讨论了这些偏差的起源和影响、用于纠正这些偏差的生物信息学方法，以及如何利用偏差来推断样本制备过程的特征，这反过来又可以用于改进文库制备。
