Machine learning-augmented m6A-Seq analysis without a reference genome.

Methylated RNA m6A immunoprecipitation sequencing (m6A-Seq) is a powerful technique for investigating transcriptome-wide m6A modification. However, most of the existing m6A-Seq protocols rely on reference genomes, limiting their use in species lacking sequenced genomes. Here, we introduce mlPEA, a user-friendly, multi-functional platform specifically tailored to the streamlined processing of m6A-Seq data in a reference genome-free manner.

deepTFBS: Improving within- and Cross-Species Prediction of Transcription Factor Binding Using Deep Multi-Task and Transfer Learning.

The precise prediction of transcription factor binding sites (TFBSs) is crucial in understanding gene regulation. In this study, deepTFBS, a comprehensive deep learning (DL) framework that builds a robust DNA language model of TF binding grammar for accurately predicting TFBSs within and across plant species is presented. Taking advantages of multi-task DL and transfer learning, deepTFBS is capable of leveraging the knowledge learned from large-scale TF binding profiles to enhance the prediction of TFBSs under small-sample training and cross-species prediction tasks.

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

Fusarium heading blight (FHB) is a devastating disease in wheat, primarily caused by field invasion of . 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.

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.

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.

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

We developed SMART v1.0 ( http://smart.omicstudio.

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.

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.