Research Projects

Fusion transcripts formed by the combination of sequences from two distinct genes represent a hidden and dynamic layer of the plant transcriptome. These transcripts can arise from DNA-level rearrangements or RNA-level processes such as trans-splicing and read-through transcription. Plant fusion transcripts are not merely transcriptional artifacts but may contribute to development, stress responses, and the evolution of novel gene functions. Despite increasing reports of fusion transcripts in plants, their biological significance remains poorly understood. Most studies rely solely on computational predictions from RNA-Seq data, which can yield high false-positive rates and lack experimental validation. This has created a critical knowledge gap in understanding how fusion transcripts contribute to gene regulation, protein diversity, and phenotypic traits in plants. This project aims to address this gap through an integrated approach that combines computational detection, AI-based classification, and experimental validation. In the first phase, fusion transcripts will be identified in Arabidopsis and rice using public RNA sequencing (RNA-Seq) and whole-genome sequencing (WGS) datasets. A WGS-based validation pipeline will distinguish true genomic fusions from artifacts. High-confidence fusion events will then be used to train an AI model for genome-wide fusion detection in plants. In the second phase, rice plants will be subjected to drought stress, and transcriptome profiling will be performed using both Illumina and PacBio sequencing platforms to identify drought-responsive fusion transcripts. Candidate fusions will be validated using RT-PCR, Sanger sequencing, and qRT-PCR. To assess their translation, proteomic analysis using high-resolution LC-MS/MS will be conducted to detect fusion-derived proteins. Crucially, the project goes beyond detection by incorporating functional characterization of selected fusion transcripts. Subcellular localization assays with GFP-tagged constructs will identify where fusion proteins function in the cell. To explore biological roles, RNA interference (RNAi) and overexpression lines will be developed for specific fusions in rice. These genetic tools will allow functional analysis of fusion transcripts in plant growth, drought response, and key molecular pathways. The novelty of this proposal lies in its integrative use of bioinformatics, AI, and functional genomics to systematically decode fusion transcript function in plants. This approach was not previously applied at this depth. The outcomes will deepen our understanding of transcriptomic complexity, reveal novel regulators of drought response, and clarify the functional role of fusion transcripts in plant growth and stress adaptation. Additionally, they will provide resources for the research community and lay the groundwork for enhancing crop performance and developing drought-resistant rice varieties.