MCRiceRepGP: a framework for the identification of genes associated with sexual reproduction in rice

Abstract

Rice is an important cereal crop, being a staple food for over half of the world's population, and sexual reproduction resulting in grain formation underpins global food security. However, despite considerable research efforts, many of the genes, especially long intergenic non-coding RNA (lincRNA) genes, involved in sexual reproduction in rice remain uncharacterized. With an increasing number of public resources becoming available, information from different sources can be combined to perform gene functional annotation. We report the development of MCRiceRepGP, a machine learning framework which integrates heterogeneous evidence and employs multicriteria decision analysis and machine learning to predict coding and lincRNA genes involved in sexual reproduction in rice. The rice genome was reannotated using deep-sequencing transcriptomic data from reproduction-associated tissue/cell types identifying previously unannotated putative protein-coding genes and lincRNAs. MCRiceRepGP was used for genome-wide discovery of sexual reproduction associated coding and lincRNA genes. The protein-coding and lincRNA genes identified have distinct expression profiles, with a large proportion of lincRNAs reaching maximum expression levels in the sperm cells. Some of the genes are potentially linked to male- and female-specific fertility and heat stress tolerance during the reproductive stage. MCRiceRepGP can be used in combination with other genome-wide studies, such as genome-wide association studies, giving greater confidence that the genes identified are associated with the biological process of interest. As more data, especially about mutant plant phenotypes, become available, the power of MCRiceRepGP will grow, providing researchers with a tool to identify candidate genes for future experiments. MCRiceRepGP is available as a web application (http://mcgplannotator.com/MCRiceRepGP/).

Plant pan-genomes are the new reference

“…Improving our understanding of the functions and interactions between the core and variable genes will significantly add value to pan-genome studies. One possible approach involves integrative genomics methodologies, which aim to link properties of genes such as expression level, connectivity in biological networks, and sequence conservation to their function 76,77 to gain a broader understanding of their potential function.…”

Pangenomics Comes of Age: From Bacteria to Plant and Animal Applications

“…Even for the relatively small bacterial genomes, the function of many accessory genes remains unknown, with variable sequences derived from mobile genetic elements making inference about the role of the accessory genome challenging [11,13,15]. Previously methodologies have been developed, aiming to link properties of genes such as expression level, connectivity in biological networks, and sequence conservation to their function [79,81,95]. Similar approaches could be adopted to understand which accessory genes are likely to be functional and have phenotypic effects.…”

Global Role of Crop Genomics in the Face of Climate Change

“…Early on gene expression evidence was mostly derived from expressed sequence tags (ESTs) and extended by full-length sequencing via cloning followed by Sanger sequencing. Later, the information was supplemented by RNASeq data from diverse tissues, and it was shown that gene models and isoforms with highly tissue-specific expression were underrepresented in exiting annotations (Cheng et al, 2017;Golicz et al, 2018b;Van Bel et al, 2019). Currently, addition of long reads generated by PacBio or Oxford Nanopore sequencing technologies allows for recovery of full-length transcripts, providing new insights into the extent of alternative splicing and transcriptome diversity (Cook et al, 2019).…”

“…For example, specialized databases integrating genotypic, phenotypic, and association data have been developed for rice (SNP-Seek), soybean (SoyBase), and wheat (T3; Grant et al, 2010;Blake et al, 2016;Mansueto et al, 2017). Beyond specialized database, tools like KnetMiner and MCRiceRepGP were developed aiming to rank candidate genes involved in biological processes of interest using multicriteria decision analysis (Hassani-Pak and Rawlings, 2017;Golicz et al, 2018b).…”

“…At least some of the lncRNAs are likely to be functional, as evidenced by mutant phenotypes of knock-outs of newly discovered lncRNAs ( Huang et al, 2018 ). Interestingly, lncRNAs have a strong bias toward transcription in reproductive tissues ( Zhang et al, 2014 ; Golicz et al, 2018b ; Johnson et al, 2018 ), suggesting involvement in plant sexual reproduction, a critical process affecting flowering, fruit, and grain formation. Newly characterized lncRNA, which affect important traits, can become genome editing targets.…”