RiceENCODE: A comprehensive epigenomic database as a rice Encyclopedia of DNA Elements

Abstract

Rice (Oryza sativa) is one of the most important crops in the world and a common model plant for genomic research. The genomes of Xian/Indica and Geng/Japonica have been completely sequenced and annotated with accurate genome information. Over the past few years, epigenomic information, including DNA methylation, histone modification, and chromatin accessibility, has been characterized in the Xian/Indica and Geng/Japonica genomes (Zhao et al., 2020Zhao L. Xie L. Zhang Q. Ouyang W. Deng L. Guan P. Ma M. Li Y. Zhang Y. Xiao Q. et al.Integrative analysis of reference epigenomes in 20 rice varieties.Nat. Commun. 2020; 11: 2658https://doi.org/10.1038/s41467-020-16457-5Crossref PubMed Scopus (28) Google Scholar). Quite a few rice three-dimensional genome studies have been published in the meantime (Zhao et al., 2019Zhao L. Wang S. Cao Z. Ouyang W. Zhang Q. Xie L. Zheng R. Guo M. Ma M. Hu Z. et al.Chromatin loops associated with active genes and heterochromatin shape rice genome architecture for transcriptional regulation.Nat. Commun. 2019; 10: 3640https://doi.org/10.1038/s41467-019-11535-9Crossref PubMed Scopus (42) Google Scholar). However, it is still a big challenge for many groups that lack dedicated bioinformatic personnel or sufficient computational resources to utilize such epigenetic data. We built a comprehensive epigenomic database RiceENCODE as a rice Encyclopedia of DNA Elements (http://glab.hzau.edu.cn/RiceENCODE/), with three-dimensional chromatin interactions, histone modification, chromatin states, chromatin accessibility, DNA methylation, and transcriptomes data (Figure 1A ). We downloaded raw data of 972 datasets, such as ChIP-seq, FAIRE-seq, MNase-seq, ATAC-seq, ncRNA-seq, RNA-seq, Hi-C, and ChIA-PET (Supplemental Tables 1, 2, 3, and 4), and reprocessed them with a standardized pipeline (Figure 1B) for different data types, mapped the reads to the MSU7.0 (Kawahara et al., 2013Kawahara Y. de la Bastide M. Hamilton J.P. Kanamori H. McCombie W.R. Ouyang S. Schwartz D.C. Tanaka T. Wu J. Zhou S. et al.Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data.Rice (New York, N.Y.). 2013; 6: 4https://doi.org/10.1186/1939-8433-6-4Crossref PubMed Scopus (874) Google Scholar), MH63RS1, and ZS97RS1 (Song et al., 2018Song J.M. Lei Y. Shu C.C. Ding Y. Xing F. Liu H. Wang J. Xie W. Zhang J. Chen L.L. Rice information GateWay: a comprehensive bioinformatics platform for indica rice genomes.Mol. Plant. 2018; 11: 505-507https://doi.org/10.1016/j.molp.2017.10.003Abstract Full Text Full Text PDF PubMed Scopus (23) Google Scholar), and performed peak calling, DNA methylation signal, or interaction calling using these data, and visualized them in the WashU epigenome browser (Li et al., 2019Li D. Hsu S. Purushotham D. Sears R.L. Wang T. WashU epigenome browser update 2019.Nucleic Acids Res. 2019; 47 (W158–w165)https://doi.org/10.1093/nar/gkz348Crossref Scopus (79) Google Scholar) for each library. Such a database can provide a comprehensive view of the expression and regulation of genes across different tissues and species, which strengthens our understanding of the epigenomic regulation processes. Epigenetic information could also facilitate the development of molecular design breeding of rice. In RiceENCODE, users can query our database to retrieve epigenomic information of three different rice accessions: Xian/Indica (MH63 and ZS97) and Geng/Japonica (Nipponbare). All subpopulation datasets were mapped to the Nipponbare genome, which is convenient for users to compare the datasets with each other. On the home page (Supplemental Figure 2A), a quick search engine is provided for users to search information. For example, the query MH03g0764200 (GNP1/OsGA20ox1) returns the basic information of MH03g0764200. The online genome browser could be used to browse each epigenetic profile on the MH03g0764200 locus (Supplemental Figure 1A), including chromatin interactions between MH03g0764200 and other genes (Supplemental Figure 1B), chromatin accessibility (Supplemental Figure 1C), histone modification (Supplemental Figure 1D), and gene expression levels in 20 tissues (Supplemental Figure 1E). Chromatin accessibility characterized by ATAC-seq, FAIRE-seq, and MNase-seq is the hallmark of DNA regulatory regions. On the chromatin accessibility page, RiceENCODE offered a gene ID and gene locus search engine to query the related open chromatin regions called by different technologies (Figure 1C). RiceENCODE also provides the graphical visualization of the genome-wide histone modification information (Supplemental Figure 2B). Such information could be accessed through a tabular data browser or visualized by the genome browser. The RiceENCODE transcriptome page (Figure 1D) provides a friendly web interface that allows users to search by gene ID or miRbase miRNA ID (Kozomara and Griffiths-Jones, 2014Kozomara A. Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data.Nucleic Acids Res. 2014; 42: D68-D73https://doi.org/10.1093/nar/gkt1181Crossref PubMed Scopus (3588) Google Scholar). The bar graphs showed the expression levels of the selected transcript in different tissues. Combining the previous histone modification information, it could strengthen the understanding of the relationship between histone signaling and gene expression regulation. Previous studies showed that the expression of genes is affected by three-dimensional chromatin interactions of the rice genome (Zhao et al., 2019Zhao L. Wang S. Cao Z. Ouyang W. Zhang Q. Xie L. Zheng R. Guo M. Ma M. Hu Z. et al.Chromatin loops associated with active genes and heterochromatin shape rice genome architecture for transcriptional regulation.Nat. Commun. 2019; 10: 3640https://doi.org/10.1038/s41467-019-11535-9Crossref PubMed Scopus (42) Google Scholar). Based on the three-dimensional interaction data of rice ChIA-PET we collected in RiceENCODE, users could query genomic region to region interactions, gene to region interactions, and interaction network information about genes (Figure 1E). Alternatively, the Hi-C or ChIA-PET interaction information could be viewed in the genome browser (Figure 1F). Chromatin states, which are mainly determined by a variety of epigenomic features, could reflect genome activities and transcriptional regulation activities (Roudier et al., 2011Roudier F. Ahmed I. Bérard C. Sarazin A. Mary-Huard T. Cortijo S. Bouyer D. Caillieux E. Duvernois-Berthet E. Al-Shikhley L. et al.Integrative epigenomic mapping defines four main chromatin states in Arabidopsis.EMBO J. 2011; 30: 1928-1938https://doi.org/10.1038/emboj.2011.103Crossref PubMed Scopus (442) Google Scholar). We used previously published information on chromatin states (Zhao et al., 2020Zhao L. Xie L. Zhang Q. Ouyang W. Deng L. Guan P. Ma M. Li Y. Zhang Y. Xiao Q. et al.Integrative analysis of reference epigenomes in 20 rice varieties.Nat. Commun. 2020; 11: 2658https://doi.org/10.1038/s41467-020-16457-5Crossref PubMed Scopus (28) Google Scholar) and trained them with datasets by different tissues and subpopulations to make the classification of chromatin states more accurate. The rice genome was partitioned into four major parts (active, repressive, inactive, and quiescent) with distinct percentage of genome coverage. On the chromatin states page (Supplemental Figure 2C), after selecting tissues and cultivars, users could search gene ID or genome locus conveniently, and obtain components of the chromatin states in the corresponding regions at 200 bp resolution. As an example, we checked the annotations that underlie quantitative traits (Wei et al., 2021Wei X. Qiu J. Yong K. Fan J. Zhang Q. Hua H. Liu J. Wang Q. Olsen K.M. Han B. et al.A quantitative genomics map of rice provides genetic insights and guides breeding.Nat. Genet. 2021; 53: 243-253https://doi.org/10.1038/s41588-020-00769-9Crossref PubMed Scopus (36) Google Scholar), and chromatin states in our database that showed different landscapes in different varieties (Supplemental Figure 3). With the DNA methylation search module (Supplemental Figure 2D), users could get the depth and DNA methylation levels of CG, CHG, and CHH at single-base resolution for the query gene. We also collected epigenetic data from mutant and wild-type rice, such as DRM2 and DDM1 (Tan et al., 2016Tan F. Zhou C. Zhou Q. Zhou S. Yang W. Zhao Y. Li G. Zhou D.X. Analysis of chromatin regulators reveals specific features of rice DNA methylation pathways.Plant Physiol. 2016; 171: 2041-2054https://doi.org/10.1104/pp.16.00393Crossref PubMed Scopus (63) Google Scholar), which have significant lower methylation levels than the wild type (Supplemental Figure 2E). We offered an interval search function for DNA differentially methylated regions as well as regions with differential enrichment of histone modification between wild type and mutants. These functions are useful to analyze the alternative relationship between DNA methylation levels, histone modifications, and gene expression. Many excellent web-based resources have been developed for rice epigenetic data, such as the Rice Epigenetic and Epigenomic Database (Zhang et al., 2020Zhang P. Wang Y. Chachar S. Tian J. Gu X. eRice: a refined epigenomic platform for japonica and indica rice.Plant Biotechnol. J. 2020; 18: 1642-1644https://doi.org/10.1111/pbi.13329Crossref PubMed Scopus (7) Google Scholar) and the Plant Chromatin State Database (Liu et al., 2018Liu Y. Tian T. Zhang K. You Q. Yan H. Zhao N. Yi X. Xu W. Su Z. PCSD: a plant chromatin state database.Nucleic Acids Res. 2018; 46 (D1157–d1167)https://doi.org/10.1093/nar/gkx919Crossref Scopus (36) Google Scholar). However, their application was limited by the lack of high-quality epigenetic data and three-dimensional chromatin interaction data. Our database combined epigenomic information from a large collection about different tissues and subpopulations in rice. We not only constructed a user-friendly interface to help users to quickly obtain epigenomic information of any target gene or region but also collected three-dimensional interaction data and chromatin state information that could further improve our knowledge on the rice epigenomic regulation mechanism. RiceENCODE provides a comprehensive map of the rice epigenetic landscape, which will be of great value for elucidating the regulatory effects of DNA elements on transcription, growth, and development. We will integrate more available resources and provide more analysis tools in RiceENCODE in the future to make it as an important platform for rice epigenomic and functional genomic research.