Open AccessBoosting the power of transcriptomics by developing an efficient gene expression profiling approach
Author(s) -
Wang Jing,
Xu Jun,
Yang Xiaohan,
Xu Song,
Zhang Ming,
Lu Fei
Publication year - 2022
Publication title -
plant biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.525
H-Index - 115
eISSN - 1467-7652
pISSN - 1467-7644
DOI - 10.1111/pbi.13706
Subject(s) - biology , transcriptome , computational biology , gene expression profiling , genome , genomics , gene expression , gene , boosting (machine learning) , population , rna seq , functional genomics , profiling (computer programming) , population genomics , genetics , computer science , machine learning , demography , sociology , operating system
Summary Recent advances in plant genomics are scaling up gene expression profiling from the individual level to the population level, making transcriptomics a more powerful tool while deciphering the genome function. This study developed an efficient 3′RNA‐seq method, Simplified Poly(A) Anchored Sequencing (SiPAS), to perform large‐scale experiments of gene expression quantification. Aside from being cost‐effective, by conducting a comprehensive performance assessment of SiPAS in hexaploid wheat, we demonstrated that SiPAS is highly sensitive, accurate, and reproducible while quantifying gene expression. Our method is anticipated to boost studies of population transcriptomics in plants and improve our understanding of genome biology.