The Ethylene-responsive element binding factor-associated Amphiphilic Repression (EAR) motifs, which were initially identified in members of the Arabidopsis ethylene response factor (ERF) family, are transcriptional repression motifs in plants and are defined by the consensus sequence patterns of either LxLxL or DLNxxP. EAR motif-containing proteins can function as transcription repressors, thus interacting with co-repressors, such as TOPLESS and AtSAP18, affecting the structure of chromatin by histone modifications and thereby repressing gene transcription. EAR motif-containing proteins are highly conserved across diverse plant species and play important roles in hormone signal transduction, stress responses and development, but they have not been identified in most plants. In this study, we identified 20,542 EAR motif-containing proteins from 71 plant species based on a Hidden Markov Model and orthologous gene search, and then we constructed a functional analysis platform for plant EAR motif-containing proteins (PlantEAR, http://structuralbiology.cau.edu.cn/plantEAR) by integrating a variety of functional annotations and processed data. Several tools were provided as functional support for EAR motif-containing proteins, such as browse, search, co-expression and protein-protein interaction (PPI) network analysis as well as cis-element analysis and gene set enrichment analysis (GSEA). In addition, basing on the identified EAR motif-containing proteins, we also explored their distribution in various species and found that the numbers of EAR motif-containing proteins showed an increasing trend in evolution from algae to angiosperms.

As an economically important crop, apple is one of the most cultivated fruit trees in temperate regions worldwide. Recently, a large number of high-quality transcriptomic and epigenomic datasets for apple were made available to the public, which could be helpful in inferring gene regulatory relationships and thus predicting gene function at the genome level. Through integration of the available apple genomic, transcriptomic, and epigenomic datasets, we constructed co-expression networks, identified functional modules, and predicted chromatin states. A total of 112 RNA-seq datasets were integrated to construct a global network and a conditional network (tissue-preferential network). Furthermore, a total of 1,076 functional modules with closely related gene sets were identified to assess the modularity of biological networks and further subjected to functional enrichment analysis. The results showed that the function of many modules was related to development, secondary metabolism, hormone response, and transcriptional regulation. Transcriptional regulation is closely related to epigenetic marks on chromatin. A total of 20 epigenomic datasets, which included ChIP-seq, DNase-seq, and DNA methylation analysis datasets, were integrated and used to classify chromatin states. Based on the ChromHMM algorithm, the genome was divided into 620,122 fragments, which were classified into 24 states according to the combination of epigenetic marks and enriched-feature regions. Finally, through the collaborative analysis of different omics datasets, the online database AppleMDO () was established for cross-referencing and the exploration of possible novel functions of apple genes. In addition, gene annotation information and functional support toolkits were also provided. Our database might be convenient for researchers to develop insights into the function of genes related to important agronomic traits and might serve as a reference for other fruit trees.

Catharanthus roseus is a medicinal plant, which can produce monoterpene indole alkaloid (MIA) metabolites with biological activity and is rich in vinblastine and vincristine. With release of the scaffolded genome sequence of C. roseus , it is necessary to annotate gene functions on the whole-genome level. Recently, 53 RNA-seq datasets are available in public with different tissues (flower, root, leaf, seedling, and shoot) and different treatments (MeJA, PnWB infection and yeast elicitor). We used in-house data process pipeline with the combination of PCC and MR algorithms to construct a co-expression network exploring multi-dimensional gene expression (global, tissue preferential, and treat response) through multi-layered approaches. In the meanwhile, we added miRNA-target pairs, predicted PPI pairs into the network and provided several tools such as gene set enrichment analysis, functional module enrichment analysis, and motif analysis for functional prediction of the co-expression genes. Finally, we have constructed an online croFGD database ( http://bioinformatics.cau.edu.cn/croFGD/ ). We hope croFGD can help the communities to study the C. roseus functional genomics and make novel discoveries about key genes involved in some important biological processes.

With the accumulation of massive data sets from high-throughput experiments and the rapid emergence of new types of omics data, gene sets have become more diverse and essential for the refinement of gene annotation at multidimensional levels. Accordingly, we collected and defined 236 007 gene sets across different categories for 44 plant species in the Plant Gene Set Annotation Database (PlantGSAD). These gene sets were divided into nine main categories covering many functional subcategories, such as trait ontology, co-expression modules, chromatin states, and liquid-liquid phase separation. The annotations from the collected gene sets covered all of the genes in the Brassicaceae species Arabidopsis and Poaceae species Oryza sativa. Several GSEA tools are implemented in PlantGSAD to improve the efficiency of the analysis, including custom SEA for a flexible strategy based on customized annotations, SEACOMPARE for the cross-comparison of SEA results, and integrated visualization features for ontological analysis that intuitively reflects their parent-child relationships. In summary, PlantGSAD provides numerous gene sets for multiple plant species and highly efficient analysis tools. We believe that PlantGSAD will become a multifunctional analysis platform that can be used to predict and elucidate the functions and mechanisms of genes of interest. PlantGSAD is publicly available at http://systemsbiology.cau.edu.cn/PlantGSEAv2/.

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