European Research Institute for the Biology of Ageing, University of Groningen, University Medical Centre Groningen , Groningen , The Netherlands.
Institute of Cytology and Genetics, Siberian Branch of RAS, Novosibirsk , Russia.
Physiol Genomics. 2019 May 1;51(5):145-158. doi: 10.1152/physiolgenomics.00128.2018. Epub 2019 Mar 15.
Transcriptome sequencing is a powerful technique to study molecular changes that underlie the differences in physiological conditions and disease progression. A typical question that is posed in such studies is finding genes with significant changes between sample groups. In this respect expression variability is regarded as a nuisance factor that is primarily of technical origin and complicates the data analysis. However, it is becoming apparent that the biological variation in gene expression might be an important molecular phenotype that can affect physiological parameters. In this review we explore the recent literature on technical and biological variability in gene expression, sources of expression variability, (epi-)genetic hallmarks, and evolutionary constraints in genes with robust and variable gene expression. We provide an overview of recent findings on effects of external cues, such as diet and aging, on expression variability and on other biological phenomena that can be linked to it. We discuss metrics and tools that were developed for quantification of expression variability and highlight the importance of future studies in this direction. To assist the adoption of expression variability analysis, we also provide a detailed description and computer code, which can easily be utilized by other researchers. We also provide a reanalysis of recently published data to highlight the value of the analysis method.
转录组测序是研究生理状态和疾病进展差异所涉及的分子变化的强大技术。在这类研究中，通常会提出一个问题，即找到在样本组之间有显著变化的基因。在这方面，表达变异性被认为是一种主要源于技术的干扰因素，它会使数据分析变得复杂。然而，越来越明显的是，基因表达中的生物学变异性可能是一个重要的分子表型，它会影响生理参数。在这篇综述中，我们探讨了关于基因表达中的技术和生物学变异性、表达变异性的来源、（表观）遗传特征以及具有稳健和可变表达基因的进化限制的最新文献。我们概述了关于外部线索（如饮食和衰老）对表达变异性的影响以及其他可能与之相关的生物学现象的最新发现。我们讨论了用于量化表达变异性的指标和工具，并强调了未来在这一方向进行研究的重要性。为了帮助采用表达变异性分析，我们还提供了详细的描述和计算机代码，其他研究人员可以轻松地使用这些代码。我们还对最近发表的数据进行了重新分析，以突出分析方法的价值。
