English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Long intergenic non-coding RNAs (lincRNAs) may play widespread roles in gene regulation and other biological processes, however, a systematic examination of the functions of lincRNAs in the biological responses of rice to phosphate (Pi) starvation has not been performed. Here, we used a computational method to predict the functions of lincRNAs in Pi-starved rice. Overall, 3,170 lincRNA loci were identified using RNA sequencing data from the roots and shoots of control and Pi-starved rice. A competing endogenous RNA (ceRNA) network was constructed for each tissue by considering the competing relationships between lincRNAs and genes, and the correlations between the expression levels of RNAs in ceRNA pairs. Enrichment analyses showed that most of the communities in the networks were related to the biological processes of Pi starvation. The lincRNAs in the two tissues were individually functionally annotated based on the ceRNA networks, and the differentially expressed lincRNAs were biologically meaningful. For example, XLOC_026030 was upregulated from 3 days after Pi starvation, and its functional annotation was ‘cellular response to Pi starvation’. In conclusion, we systematically annotated lincRNAs in rice and identified those involved in the biological response to Pi starvation.

Functional analysis of long intergenic non-coding RNAs in phosphate-starved rice using competing endogenous RNA network