Multi-strategic RNA-seq analysis reveals a high-resolution transcriptional landscape in cotton

Kun Wang, Dehe Wang, Xiaomin Zheng, Ai Qin, Jie Zhou, Boyu Guo, Yanjun Chen, Xingpeng Wen, Wen Ye, Yu Zhou () and Yuxian Zhu ()
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Kun Wang: Wuhan University
Dehe Wang: Wuhan University
Xiaomin Zheng: Wuhan University
Ai Qin: Wuhan University
Jie Zhou: Wuhan University
Boyu Guo: Wuhan University
Yanjun Chen: Wuhan University
Xingpeng Wen: Wuhan University
Wen Ye: Wuhan University
Yu Zhou: Wuhan University
Yuxian Zhu: Wuhan University

Nature Communications, 2019, vol. 10, issue 1, 1-15

Abstract: Abstract Cotton is an important natural fiber crop, however, its comprehensive and high-resolution gene map is lacking. Here we integrate four complementary high-throughput techniques, including Pacbio long read Iso-seq, strand-specific RNA-seq, CAGE-seq, and PolyA-seq, to systematically explore the transcription landscape across 16 tissues or different organ types in Gossypium arboreum. We devise a computational pipeline, named IGIA, to reconstruct accurate gene structures from the integrated data. Our results reveal a dynamic and diverse transcriptional map in cotton: tissue-specific gene expression, alternative usage of TSSs and polyadenylation sites, hotspot of alternative splicing, and transcriptional read-through. These regulated events affect many genes in various aspects such as gain or loss of functional RNA motifs and protein domains, fine-tuning of DNA binding activity, and co-regulation for genes in the same complex or pathway. The methods and findings provide valuable resources for further functional genomic studies such as understanding natural SNP variations for plant community.

Date: 2019
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DOI: 10.1038/s41467-019-12575-x

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