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

Kushwaha, Sandeep (author): Sveriges lantbruksuniversitet,Swedish University of Agricultural Sciences,Lund University,Lunds universitet,Biodiversitet,Biologiska institutionen,Naturvetenskapliga fakulteten,Biodiversity,Department of Biology,Faculty of Science,Växtskyddsbiologi,Department of Plant Protection Biology

Chauhan, Pallavi (author): Lund University,Lunds universitet,Evolutionär ekologi,Biologiska institutionen,Naturvetenskapliga fakulteten,Evolutionary ecology,Department of Biology,Faculty of Science

Hedlund, Katarina (author): Lund University,Lunds universitet,Biodiversitet,Biologiska institutionen,Naturvetenskapliga fakulteten,Biodiversity,Department of Biology,Faculty of Science

Ahrén, Dag (author): Lund University,Lunds universitet,Biodiversitet,Biologiska institutionen,Naturvetenskapliga fakulteten,Biodiversity,Department of Biology,Faculty of Science

(creator_code:org_t)

2015-12-09
2015
English.
In: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 32:8, s. 1223-1225

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Abstract Subject headings

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.

By the author/editor: Kushwaha, Sandee ...; Chauhan, Pallavi; Hedlund, Katarin ...; Ahrén, Dag

About the subject

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Bioinformatics

By the university: Lund University; Swedish University of Agricultural Sciences

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