Rice Gene Index: A comprehensive pan-genome database for comparative and functional genomics of Asian rice

Yu, Zhichao; Chen, Yongming; Zhou, Yong; Zhang, Haoyang; Li, Mengyuan; Ouyang, Yidan; Chebotarov, Dmytro; Mauleon, Ramil; Zhao, Hu; Xie, Weibo; McNally, Kenneth L.; Wing, Rod A.; Guo, Weilong; Zhang, Jian Wei

doi:10.1016/j.molp.2023.03.012

Cited by 18 publications

(9 citation statements)

References 12 publications

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“…Differentially expressed genes (DEGs) were analyzed with DESeq2 v1.38.0 (Love et al, 2014) with |log2(fold change)| > 1 and P -adj < 0.05. Gene ontology (GO) and KEGG pathway (Kanehisa and Goto, 2000) enrichment analysis of the DEGs were performed using Rice Gene Index (https://riceome.hzau.edu.cn/) (Yu et al, 2023) and KOBAS v3.0 (Bu et al, 2021). Heat stress responsive related genes were obtained from the funRiceGenes database (Yao et al, 2018).…”

Section: Methodsmentioning

confidence: 99%

Genomic and transcriptomic analyses of the elite rice variety Huizhan provide insight into disease resistance and heat tolerance

Yang,

Yang

et al. 2024

Preprint

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Rice is an important crop and serves as a model for crop genomics and breeding studies. Here, we used Oxford Nanopore ultra-long sequencing and next-generation sequencing technologies to generate a chromosome-scale genome assembly of Huizhan, a disease-resistant and heat-tolerant indica rice variety. The final genome assembly was 395.20 Mb with a scaffold N50 of 31.87 Mb. We identified expanded gene families in Huizhan that are potentially associated with both organ growth and development, as well as stress responses. We observed that three functional rice blast resistance genes, including Pi2, Pia and Ptr, and bacterial blight resistance gene Xa27, likely contribute to disease resistance of Huizhan. In addition, integrated genomics and transcriptomics analyses show that OsHIRP1, OsbZIP60, the SOD gene family, and various transcription factors are likely involved in heat tolerance of Huizhan. Results presented in this study will serve as a valuable resource for rice functional genomics studies and breeding.

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Section: Methodsmentioning

confidence: 99%

Genomic and transcriptomic analyses of the elite rice variety Huizhan provide insight into disease resistance and heat tolerance

Yang,

Yang

et al. 2024

Preprint

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“…For that aim a selection of 18,728 coding SNPs from the 171 varieties was used. To obtain the pool, we intersected the 124,019 filtered SNPs with coding regions (CDS) of the 21,418 IRGSP Nipponbare genes conserved in the whole Rice Gene Index (RGI) collection ( core genes) (Yu et al 2023 ). The RGI collection encompasses 16 platinum standard reference genomes of rice (Zhou et al 2020 ).…”

Section: Methodsmentioning

confidence: 99%

Deciphering the Genetic Basis of Allelopathy in japonica Rice Cultivated in Temperate Regions Using a Genome-Wide Association Study

García-Romeral,

Castanera,

Casacuberta

et al. 2024

Rice

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Allelopathy has been considered as a natural method of weed control. Despite the nature of allelochemical compounds has been studied, little is known about the genetic basis underlying allelopathy. However, it is known that rice exhibits diverse allelopathic potentials across varieties, and breeding for rice plants exhibiting allelopathic potential conferring an advantage against weeds in paddy fields would be highly desirable. Knowledge of the gene factors and the identification of the genomic regions responsible for allelopathy would facilitate breeding programs. Taking advantage of the existing genetic diversity in rice, particularly in temperate japonica rice, we conducted a comprehensive investigation into the genetic determinants that contribute to rice allelopathy. Employing Genome-Wide Association Study, we identified four Quantitative Trait Loci, with the most promising loci situated on chromosome 2 and 5. Subsequent inspection of the genes located within these QTLs revealed genes associated with the biosynthesis of secondary metabolites such as Phenylalanine Ammonia Lyase (PAL), a key enzyme in the synthesis of phenolic compounds, and two genes coding for R2R3-type MYB transcription factors. The identification of these two QTLs associated to allelopathy in rice provides a useful tool for further exploration and targeted breeding strategies.

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“…The MIP dataset has been deposited in NCBI BioProject with accession number PRJNA983493. The datasets for Arabidopsis, rice, wheat, and potato can be accessed with NCBI BioProject PRJNA382842 (Arabidopsis), PRJNA760839 (rice; Yu et al ., 2023), NGDC BioProject PRJCA007997 (potato; Bao et al ., 2022), and ENA BioProject PRJEB15048 (wheat; Clavijo et al ., 2017). All source codes in the iFLAS toolkit can be available at the GitHub repository: https://github.com/CrazyHsu/iFLAS.…”

Section: Data Availabilitymentioning

confidence: 99%

iFLAS: positive‐unlabeled learning facilitates full‐length transcriptome‐based identification and functional exploration of alternatively spliced isoforms in maize

Xu,

Liu,

Zhao

et al. 2024

New Phytologist

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Summary The advent of full‐length transcriptome sequencing technologies has accelerated the discovery of novel splicing isoforms. However, existing alternative splicing (AS) tools are either tailored for short‐read RNA‐Seq data or designed for human and animal studies. The disparities in AS patterns between plants and animals still pose a challenge to the reliable identification and functional exploration of novel isoforms in plants. Here, we developed integrated full‐length alternative splicing analysis (iFLAS), a plant‐optimized AS toolkit that introduced a semi‐supervised machine learning method known as positive‐unlabeled (PU) learning to accurately identify novel isoforms. iFLAS also enables the investigation of AS functions from various perspectives, such as differential AS, poly(A) tail length, and allele‐specific AS (ASAS) analyses. By applying iFLAS to three full‐length transcriptome sequencing datasets, we systematically identified and functionally characterized maize (Zea mays) AS patterns. We found intron retention not only introduces premature termination codons, resulting in lower expression levels of isoforms, but may also regulate the length of 3′UTR and poly(A) tail, thereby affecting the functional differentiation of isoforms. Moreover, we observed distinct ASAS patterns in two genes within heterosis offspring, highlighting their potential value in breeding. These results underscore the broad applicability of iFLAS in plant full‐length transcriptome‐based AS research.

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Rice Gene Index: A comprehensive pan-genome database for comparative and functional genomics of Asian rice

Cited by 18 publications

References 12 publications

Genomic and transcriptomic analyses of the elite rice variety Huizhan provide insight into disease resistance and heat tolerance

Genomic and transcriptomic analyses of the elite rice variety Huizhan provide insight into disease resistance and heat tolerance

Deciphering the Genetic Basis of Allelopathy in japonica Rice Cultivated in Temperate Regions Using a Genome-Wide Association Study

iFLAS: positive‐unlabeled learning facilitates full‐length transcriptome‐based identification and functional exploration of alternatively spliced isoforms in maize

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