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

Kushwaha, Sandeep LU ; Chauhan, Pallavi LU ; Hedlund, Katarina LU and Ahrén, Dag LU (2015) In Bioinformatics 32(8). p.1223-1225
Abstract
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... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Bioinformatics
volume
32
issue
8
pages
1223 - 1225
publisher
Oxford University Press
external identifiers
  • pmid:26656003
  • scopus:84966461401
ISSN
1367-4803
DOI
10.1093/bioinformatics/btv714
language
English
LU publication?
yes
id
68a8b4e0-ae6c-45d8-ada7-fbf817fd1dd0 (old id 8505184)
date added to LUP
2016-01-08 09:36:21
date last changed
2017-05-21 03:10:11
@article{68a8b4e0-ae6c-45d8-ada7-fbf817fd1dd0,
  abstract     = {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.},
  author       = {Kushwaha, Sandeep and Chauhan, Pallavi and Hedlund, Katarina and Ahrén, Dag},
  issn         = {1367-4803},
  language     = {eng},
  month        = {12},
  number       = {8},
  pages        = {1223--1225},
  publisher    = {Oxford University Press},
  series       = {Bioinformatics},
  title        = {NBSPred: A support vector machine-based high throughput pipeline for plant resistance protein NBSLRR prediction.},
  url          = {http://dx.doi.org/10.1093/bioinformatics/btv714},
  volume       = {32},
  year         = {2015},
}