ASPTF: A computational tool to predict abiotic stress-responsive transcription factors in plants by employing machine learning algorithms.

Journal: Biochimica et biophysica acta. General subjects
Published Date: Mar 14, 2024

Abstract

BACKGROUND: Abiotic stresses pose serious threat to the growth and yield of crop plants. Several studies suggest that in plants, transcription factors (TFs) are important regulators of gene expression, especially when it comes to coping with abiotic stresses. Therefore, it is crucial to identify TFs associated with abiotic stress response for breeding of abiotic stress tolerant crop cultivars.

Authors

  • Upendra Kumar Pradhan
    Studio of Computational Biology & Bioinformatics, The Himalayan Centre for High-throughput Computational Biology, (HiCHiCoB, A BIC supported by DBT, India)CSIR-Institute of Himalayan Bioresource Technology (CSIR-IHBT), Palampur (HP), India.
  • Anuradha Mahapatra
    Department of Bioinformatics, Odisha University of Agriculture & Technology, Bhubaneswar 751003, Odisha, India.
  • Sanchita Naha
    Division of Computer Applications, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India. Electronic address: sanchita.naha@icar.gov.in.
  • Ajit Gupta
    Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India. Electronic address: ajit@icar.gov.in.
  • Rajender Parsad
    ICAR-Indian Agricultural Statistics Research Institute, PUSA, New Delhi 110012, India. Electronic address: rajender.parsad@icar.gov.in.
  • Vijay Gahlaut
    University Centre for Research & Development, Chandigarh University, Mohali, Punjab, India. Electronic address: vijay.e14220@cumail.in.
  • Surya Narayan Rath
    Department of Bioinformatics, Odisha University of Agriculture & Technology, Bhubaneswar 751003, Odisha, India.
  • Prabina Kumar Meher
    Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012, India.

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