PEA-m6A: an ensemble learning framework for accurately predicting N6-methyladenosine modifications in plants.

N 6-methyladenosine (m6A), which is the mostly prevalent modification in eukaryotic mRNAs, is involved in gene expression regulation and many RNA metabolism processes. Accurate prediction of m6A modification is important for understanding its molecular mechanisms in different biological contexts. However, most existing models have limited range of application and are species-centric. Here we present PEA-m6A, a unified, modularized and parameterized framework that can streamline m6A-Seq data analysis for predicting m6A-modified regions in plant genomes. The PEA-m6A framework builds ensemble learning-based m6A prediction models with statistic-based and deep learning-driven features, achieving superior performance with an improvement of 6.7% to 23.3% in the area under precision-recall curve compared with state-of-the-art regional-scale m6A predictor WeakRM in 12 plant species. Especially, PEA-m6A is capable of leveraging knowledge from pretrained models via transfer learning, representing an innovation in that it can improve prediction accuracy of m6A modifications under small-sample training tasks. PEA-m6A also has a strong capability for generalization, making it suitable for application in within- and cross-species m6A prediction. Overall, this study presents a promising m6A prediction tool, PEA-m6A, with outstanding performance in terms of its accuracy, flexibility, transferability, and generalization ability. PEA-m6A has been packaged using Galaxy and Docker technologies for ease of use and is publicly available at https://github.com/cma2015/PEA-m6A.

IPOP: An Integrative Plant Multi-omics Platform for Cross-species Comparison and Evolutionary Study.

The advent of high-throughput sequencing technologies has led to the production of a significant amount of omics data in plants, which serves as valuable assets for conducting cross-species multi-omics comparative analysis. Nevertheless, the current dearth of comprehensive platforms providing evolutionary annotation information and multi-species multi-omics data impedes users from systematically and efficiently performing evolutionary and functional analysis on specific genes. In order to establish an advanced plant multi-omics platform that provides timely, accurate, and high-caliber omics information, we collected 7 distinct types of omics data from 6 monocots, 6 dicots, and 1 moss, and reanalyzed these data using standardized pipelines. Additionally, we furnished homology information, duplication events, and phylostratigraphic stages of 13 species to facilitate evolutionary examination. Furthermore, the integrative plant omics platform (IPOP) is bundled with a variety of online analysis tools that aid users in conducting evolutionary and functional analysis. Specifically, the Multi-omics Integration Analysis tool is available to consolidate information from diverse omics sources, while the Transcriptome-wide Association Analysis tool facilitates the linkage of functional analysis with phenotype. To illustrate the application of IPOP, we conducted a case study on the YTH domain gene family, wherein we observed shared functionalities within orthologous groups and discerned variations in evolutionary patterns across these groups. To summarize, the IPOP platform offers valuable evolutionary insights and multi-omics data to the plant sciences community, effectively addressing the need for cross-species comparison and evolutionary research platforms. All data and modules within IPOP are freely accessible for academic purposes (http://omicstudio.cloud:4012/ipod/).

Novel Endophytic Pseudescherichia sp. GSE25 Strain Significantly Controls Fusarium graminearum and Reduces Deoxynivalenol in Wheat.

Fusarium heading blight (FHB) is a devastating disease in wheat, primarily caused by field invasion of Fusarium graminearum. Due to the scarcity of resistant wheat varieties, the agricultural sector resorts to chemical fungicides to control FHB incidence. On the other hand, biocontrol represents a promising, eco-friendly approach aligned with sustainable and green agriculture concepts. In the present study, a bacterial endophyte, Pseudescherichia sp. (GSE25), was isolated from wheat seeds and identified through complete genome sequencing and phylogenetic analysis. In vitro testing of this endophytic strain demonstrated strong antifungal activity against F. graminearum PH-1 by inhibiting spore germination, suppressing germ tube growth, and causing cell membrane damage. Under field conditions, the strain GSE25 significantly reduced the FHB incidence and the associated deoxynivalenol mycotoxin accumulation by over 60% and 80%, respectively. These findings highlight the potential of the isolated bacterial endophyte Pseudescherichia sp. GSE25 strain as a biocontrol agent in protecting wheat from FHB-caused F. graminearum. This is the first report showing a biocontrol effect of Pseudescherichia sp. a strain against phytopathogens.

Interactive Web-based Annotation of Plant MicroRNAs with iwa-miRNA.

MicroRNAs (miRNAs) are important regulators of gene expression. The large-scale detection and profiling of miRNAs have been accelerated with the development of high-throughput small RNA sequencing (sRNA-Seq) techniques and bioinformatics tools. However, generating high-quality comprehensive miRNA annotations remains challenging due to the intrinsic complexity of sRNA-Seq data and inherent limitations of existing miRNA prediction tools. Here, we present iwa-miRNA, a Galaxy-based framework that can facilitate miRNA annotation in plant species by combining computational analysis and manual curation. iwa-miRNA is specifically designed to generate a comprehensive list of miRNA candidates, bridging the gap between already annotated miRNAs provided by public miRNA databases and new predictions from sRNA-Seq datasets. It can also assist users in selecting promising miRNA candidates in an interactive mode, contributing to the accessibility and reproducibility of genome-wide miRNA annotation. iwa-miRNA is user-friendly and can be easily deployed as a web application for researchers without programming experience. With flexible, interactive, and easy-to-use features, iwa-miRNA is a valuable tool for the annotation of miRNAs in plant species with reference genomes. We also illustrate the application of iwa-miRNA for miRNA annotation using data from plant species with varying genomic complexity. The source codes and web server of iwa-miRNA are freely accessible at http://iwa-miRNA.omicstudio.cloud/.

easyMF: A Web Platform for Matrix Factorization-Based Gene Discovery from Large-scale Transcriptome Data.

With the development of high-throughput experimental technologies, large-scale RNA sequencing (RNA-Seq) data have been and continue to be produced, but have led to challenges in extracting relevant biological knowledge hidden in the produced high-dimensional gene expression matrices. Here, we develop easyMF ( https://github.com/cma2015/easyMF ), a web platform that can facilitate functional gene discovery from large-scale transcriptome data using matrix factorization (MF) algorithms. Compared with existing MF-based software packages, easyMF exhibits several promising features, such as greater functionality, flexibility and ease of use. The easyMF platform is equipped using the Big-Data-supported Galaxy system with user-friendly graphic user interfaces, allowing users with little programming experience to streamline transcriptome analysis from raw reads to gene expression, carry out multiple-scenario MF analysis, and perform multiple-way MF-based gene discovery. easyMF is also powered with the advanced packing technology to enhance ease of use under different operating systems and computational environments. We illustrated the application of easyMF for seed gene discovery from temporal, spatial, and integrated RNA-Seq datasets of maize (Zea mays L.), resulting in the identification of 3,167 seed stage-specific, 1,849 seed compartment-specific, and 774 seed-specific genes, respectively. The present results also indicated that easyMF can prioritize seed-related genes with superior prediction performance over the state-of-art network-based gene prioritization system MaizeNet. As a modular, containerized and open-source platform, easyMF can be further customized to satisfy users' specific demands of functional gene discovery and deployed as a web service for broad applications.

SMART v1.0: A Database for Small Molecules with Functional Implications in Plants.

We developed SMART v1.0 ( http://smart.omicstudio.cloud ), the first database for small molecules with functional implications in plants. The SMART database is devoted to providing and managing small molecules and their associated structural data, chemoinformatic data, protein targets, pathways and induced phenotype/function information. Currently, SMART v1.0 encompasses 1218 unique small molecules which are involved in multiple biological pathways. SMART v1.0 is featured with user-friendly interfaces, through which pathway-centered visualization of small molecules can be efficiently performed, and multiple types of searches (i.e., text search, structure similarity search and sequence similarity search) can be conveniently conducted. SMART v1.0 is also specifically designed to be a small molecule-sharing database, allowing users to release their newly discovered small molecules to public via the Contribute webpage. The SMART database will facilitate the comprehensive understanding of small molecules in complex biological processes in plants.

An activity-based fluorogenic probe for sensitive and selective monoamine oxidase-B detection.

We report the design and synthesis of a new class of fluorogenic probes based on monoamine oxidase-triggered oxidative C-O bond cleavage. The selectivity of probe P1 towards MAO-B was 22-fold higher than that towards MAO-A.