BACKGROUND

Abiotic stresses such as drought, salinity, and heat severely affect plant growth and productivity, making the identification of stress-responsive genes vital for crop improvement. Existing experimental approaches for gene family classification and pathway mapping are database reliant, labour-intensive and time consuming. AbStrPred addresses this gap by providing a computational framework to predict abiotic stress-related gene families along with their associated pathways. This enables faster insights into stress tolerance mechanisms and supports targeted research in plant stress biology.

ABOUT

The AbStrPred server is an advanced AI-based tool designed to predict specific abiotic stress-related gene families, including SPL, NAC, bZIP, HSP, AP2/ERF, SnRK2, DREB/CBF, WRKY, MYB, LEA, and SBT, and their associated pathways directly from nucleotide or protein sequences. It leverages ESM2 transformer-based NLP embeddings for robust feature extraction and employs a Logistic Regression model for accurate classification of these targeted gene families. For pathway prediction, AbStrPred integrates local BLAST searches with a curated database of abiotic stress-related pathways, ensuring biologically relevant results. The server supports both single sequence input and FASTA file uploads, making it versatile for diverse research needs. Users can also download prediction results in CSV format, enabling easy integration into downstream analyses and accelerating research in functional genomics and stress-adaptation studies.