funbarRF: DNA barcode-based fungal species prediction using multiclass Random Forest supervised learning model.

Journal: BMC genetics
Published Date:

Abstract

BACKGROUND: Identification of unknown fungal species aids to the conservation of fungal diversity. As many fungal species cannot be cultured, morphological identification of those species is almost impossible. But, DNA barcoding technique can be employed for identification of such species. For fungal taxonomy prediction, the ITS (internal transcribed spacer) region of rDNA (ribosomal DNA) is used as barcode. Though the computational prediction of fungal species has become feasible with the availability of huge volume of barcode sequences in public domain, prediction of fungal species is challenging due to high degree of variability among ITS regions within species.

Authors

  • Prabina Kumar Meher
    Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012, India.
  • Tanmaya Kumar Sahu
    Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012, India.
  • Shachi Gahoi
    Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India.
  • Ruchi Tomar
    Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, 110012, India.
  • Atmakuri Ramakrishna Rao
    Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012, India.