Menu

Javascript is not activated in your browser. This website needs javascript activated to work properly.
You are here

NBSPred: A support vector machine-based high throughput pipeline for plant resistance protein NBSLRR prediction.

Author:
  • Sandeep Kushwaha
  • Pallavi Chauhan
  • Katarina Hedlund
  • Dag Ahrén
Publishing year: 2015-12-09
Language: English
Pages: 1223-1225
Publication/Series: Bioinformatics
Volume: 32
Issue: 8
Document type: Journal article
Publisher: Oxford University Press

Abstract english

The nucleotide binding site-leucine-rich repeats (NBSLRR) belong to one of the largest known families of disease resistance genes that encode resistance proteins (R-protein) against the pathogens of plants. Various defence mechanisms have explained the regulation of plant immunity, but still, we have limited understanding about plant defence against different pathogens. Identification of R-proteins and proteins having R-protein-like features across the genome, transcriptome and proteome would be highly useful to develop the global understanding of plant defence mechanisms, but it is laborious and time consuming task. Therefore, we have developed a support vector machine (SVM) based high throughput pipeline called NBSPred to differentiate NBSLRR and NBSLRR-like protein from Non-NBSLRR proteins from genome, transcriptome and protein sequences. The pipeline was tested and validated with input sequences from 3 dicot and 2 monocot plants including Arabidopsis thaliana, Boechera stricta, Brachypodium distachyon Solanum lycopersicum and Zea mays.

Keywords

  • Bioinformatics (Computational Biology)

Other

Published
  • ISSN: 1367-4803
Katarina Hedlund
E-mail: katarina.hedlund [at] biol.lu.se

Professor

Biodiversity

+46 46 222 37 98

+46 72 562 10 04

E-A321

50

Researcher

Centre for Environmental and Climate Research (CEC)

+46 46 222 37 98

50

Centre for Animal Movement Research
Evolutionary Ecology, Department of Biology
Ecology building S-223 62 Lund Sweden