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Biology subjects

Immadi, M. S.

Publications and source records attributed to Immadi, M. S..

2 recordsLinked to original sources

G2PDeep-v2: a web-based deep-learning framework for phenotype prediction and biomarker discovery using multi-omics data

The G2PDeep-v2 server is a web-based platform powered by deep learning, for phenotype prediction and markers discovery from multi-omics data in any organisms including humans, plants, animals, and viruses. The server provides multiple services for researchers to create deep-learning models through an interactive interface and train these models using an automated hyperparameter tuning algorithm on high-performance computing resources. Users can visualize the results of phenotype and markers predictions and perform Gene Set Enrichment Analysis for the significant markers to provide insights into the molecular mechanisms underlying complex diseases and other biological processes. The G2PDeep-v2 server is publicly available at https://g2pdeep.org/.

bioinformatics↗

Integrative phenotypic-transcriptomic analysis of soybean plants subjected to multifactorial stress combination

Global warming, climate change, and industrial pollution are altering our environment subjecting crops to an increasing number and complexity of abiotic stress conditions, concurrently or sequentially. Recent studies revealed that a combination of 3 or more stresses simultaneously impacting a plant (termed multifactorial stress combination; MFSC) can cause a drastic decline in plant growth and survival, even if the level of each stress involved in the MFSC has a negligible effect on plants. However, the impacts of MFSC on crops are largely unknown. We subjected soybean plants to a MFSC of up to five different stresses (water deficit, salinity, low phosphate, acidity, and cadmium), in an increasing level of complexity, and conducted integrative transcriptomic-phenotypic analysis of reproductive and vegetative tissues. We reveal that MFSC has a negative cumulative effect on soybean yield, that each set of MFSC condition elicits a unique transcriptomic response (that is different between flowers and leaves), and that selected genes expressed in leaves or flowers are linked to the effects of MFSC on different vegetative, physiological, and/or reproductive parameters. We further reveal that the transcriptomic response of soybean and Arabidopsis to MFSC shares common features associated with reactive oxygen and iron/copper signaling/metabolism. Our study provides unique phenotypic and transcriptomic datasets for dissecting the mechanistic effects of MFSC on the vegetative, physiological, and reproductive processes of a crop plant.

plant biology↗