第1176回生物科学セミナー
Gauss-power mixing distributions comprehensively describe stochastic variations in RNA-seq data（遺伝子発現揺らぎの新規モデル分布）
粟津 暁紀 准教授（広島大学 大学院理学研究科）
2017年11月13日(月) 17:00-18:30 理学部3号館 412号室
Gene expression levels exhibit stochastic variations among genetically identical organisms under the same environmental conditions. In many recent transcriptome analyses based on RNA sequencing (RNA-seq), variations in gene expression levels among replicates were assumed to follow a negative binomial distribution although the physiological basis of this assumption remain unclear.
In this study, RNA-seq data were obtained from Arabidopsis thaliana under eight conditions (21–27 replicates), and the characteristics of gene-dependent distribution profiles of gene expression levels were analyzed. For A. thaliana and Saccharomyces cerevisiae, the distribution profiles could be described by a Gauss-power mixing distribution derived from a simple model of a stochastic transcriptional network containing a feedback loop. The distribution profiles of gene expression levels were roughly classified as Gaussian, power law-like containing a long tail, and intermediate. The fitting function predicted that gene expression levels with long-tailed distributions would be strongly influenced by feedback regulation. Furthermore, the features of gene expression levels are correlated with their functions, with the levels of essential genes tending to follow a Gaussian distribution and those of genes encoding nucleic acid-binding proteins and transcription factors exhibiting long-tailed distributions.
参考文献
A.Awazu, T. Tanabe, M. Kamitani, A. Tezuka, A. J Nagano, bioRxiv
doi: https://doi.org/10.1101/194118
