Shi Huajuan, Zhou Ying, Jia Erteng, Pan Min, Bai Yunfei, Ge Qinyu
State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.
School of Medicine, Southeast University, Nanjing 210097, China.
Biomed Res Int. 2021 Apr 19;2021:6647597. doi: 10.1155/2021/6647597. eCollection 2021.
Although RNA sequencing (RNA-seq) has become the most advanced technology for transcriptome analysis, it also confronts various challenges. As we all know, the workflow of RNA-seq is extremely complicated and it is easy to produce bias. This may damage the quality of RNA-seq dataset and lead to an incorrect interpretation for sequencing result. Thus, our detailed understanding of the source and nature of these biases is essential for the interpretation of RNA-seq data, finding methods to improve the quality of RNA-seq experimental, or development bioinformatics tools to compensate for these biases. Here, we discuss the sources of experimental bias in RNA-seq. And for each type of bias, we discussed the method for improvement, in order to provide some useful suggestions for researcher in RNA-seq experimental.
尽管RNA测序（RNA-seq）已成为转录组分析的最先进技术，但它也面临各种挑战。众所周知，RNA-seq的工作流程极其复杂，且容易产生偏差。这可能会损害RNA-seq数据集的质量，并导致对测序结果的错误解读。因此，我们对这些偏差的来源和性质有详细的了解，对于解读RNA-seq数据、找到提高RNA-seq实验质量的方法或开发生物信息学工具来补偿这些偏差至关重要。在此，我们讨论RNA-seq实验偏差的来源。对于每种偏差类型，我们都讨论了改进方法，以便为从事RNA-seq实验的研究人员提供一些有用的建议。
