Publications of NIPGR Scientists

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    PFGPred: A stack ensemble classifier for the identification of fusion genes in plants
    (Oxford University Press, 2026) Hamid, Fiza; Mukherjee, Kanka; Chaudhary, Sakshi; Kaushik, Love; Kumar, Shailesh
    Fusion genes play crucial roles in plant biological processes but remain far less explored than their human counterparts, largely due to limited validated datasets and the absence of plant-specific prediction tools. Existing approaches often produce high false-positive rates, restricting reliable discovery. To address this gap, we developed Plant Fusion Gene Predictor (PFGPred), an ensemble machine learning framework that integrates Random Forest, XGBoost, and long short-term memory (LSTM) models into a meta-classifier for accurate identification of true and false fusion genes from RNA-Seq data. PFGPred was trained on a high-confidence dataset of fusion genes validated by both RNA-Seq and whole-genome sequencing from Arabidopsis thaliana, Oryza sativa, Triticum aestivum, and Zea mays, to predict and rank candidate fusion genes for future functional validation. It outperformed individual baseline models, achieving accuracies of 0.97 on training data and 0.77 on independent test data. When evaluated on human datasets, it achieved 0.71 accuracy with lower sensitivity, reflecting biological differences between plant and human fusion events. Comparative analyses confirmed that PFGPred reliably identifies validated fusions, demonstrating its utility as a cost-effective, plant-specific prediction tool for high-throughput fusion gene screening and functional genomics research. It is freely available as a web server at http://www.nipgr.ac.in/PFGPred.
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    AraNSdb: a dedicated database of stress-responsive non-coding RNAs in Arabidopsis thaliana
    (Springer Nature Publishing AG, 2026) Vivek, A.T.; Bhatia, Manika; Sahu, Namrata; Kalakoti, Garima; Kaushik, Love; Mukherjee, Kanka; Kumar, Shailesh
    Plants, as sessile organisms, are constantly exposed to biotic and abiotic stresses, making their ability to respond crucial for survival. Non-coding RNAs (ncRNAs) have emerged as key regulators in these stress responses, with several studies identifying numerous stress-responsive ncRNAs (SRNs). However, a comprehensive collection of SRNs derived from sequencing data in Arabidopsis thaliana has been lacking. To address this, we utilized high-throughput experimental data and mined published studies to construct AraNSdb (Arabidopsis ncRNA Stress Database), a systematic resource for storing and querying SRNs. AraNSdb documents over 1,000 expression profiles from diverse stress datasets, encompassing 6,616 SRNs, including microRNAs (miRNAs), small interfering RNAs (siRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs). The database features an intuitive web interface for exploring SRNs associated with specific stress types and provides detailed ncRNA annotations to support functional and regulatory studies. AraNSdb offers a valuable platform for advancing our understanding of ncRNA-mediated stress responses and is freely accessible at http://www.nipgr.ac.in/AraNSdb.