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ResCap: plant resistance gene prediction and probe generation pipeline for resistance gene sequence capture

Kushwaha, Sandeep Kumar and Kumar Kushwaha, Sandeep and Åhman, Inger and Bengtsson, Therese (2021). ResCap: plant resistance gene prediction and probe generation pipeline for resistance gene sequence capture. Bioinformatics Advances. 1 :1 , vbab033
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Abstract

The discovery of novel resistance genes (R-genes) is an important component in disease resistance breeding. Nevertheless, R-gene identification from wild species and close relatives of plants is not only a difficult but also a cumbersome process. In this study, ResCap, a support vector machine-based high-throughput R-gene prediction and probe generation pipeline has been developed to generate probes from genomic datasets. ResCap contains two integral modules. The first module identifies the R-genes and R-gene like sequences under four categories containing different domains such as TIR-NBS-LRR (TNL), CC-NBS-LRR (CNL), Receptor-like kinase (RLK) and Receptor-like proteins (RLPs). The second module generates probes from extracted nucleotide sequences of resistance genes to conduct sequence capture (SeqCap) experiments. For the validation of ResCap pipeline, ResCap generated probes were synthesized and a sequence capture experiment was performed to capture expressed resistance genes among six spring barley genotypes. The developed ResCap pipeline in combination with the performed sequence capture experiment has shown to increase precision of R-gene identification while simultaneously allowing rapid gene validation including non-sequenced plants.

Authors/Creators:Kushwaha, Sandeep Kumar and Kumar Kushwaha, Sandeep and Åhman, Inger and Bengtsson, Therese
Title:ResCap: plant resistance gene prediction and probe generation pipeline for resistance gene sequence capture
Series Name/Journal:Bioinformatics Advances
Year of publishing :2021
Volume:1
Number:1
Article number:vbab033
Number of Pages:3
Language:English
Publication Type:Other publication in scientific journal
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 4 Agricultural Sciences > 404 Agricultural Biotechnology > Genetics and Breeding
(A) Swedish standard research categories 2011 > 1 Natural sciences > 102 Computer and Information Science > 10203 Bioinformatics (Computational Biology) (applications to be 10610)
URN:NBN:urn:nbn:se:slu:epsilon-p-115570
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-115570
Additional ID:
Type of IDID
DOI10.1093/bioadv/vbab033
Otherhttps://doi.org/10.1093/bioadv/vbab033
Alternative URL:https://doi.org/10.1093/bioadv/vbab033
ID Code:26779
Faculty:LTV - Fakulteten för landskapsarkitektur, trädgårds- och växtproduktionsvetenskap
Department:(LTJ, LTV) > Department of Plant Breeding (from 130101)
Deposited By: SLUpub Connector
Deposited On:21 Jan 2022 04:26
Metadata Last Modified:21 Jan 2022 06:01

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