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A TILLING by sequencing approach to identify induced mutations in sunflower genes
The Targeting Induced Local Lesions in Genomes (TILLING) technology is a reverse genetic strategy broadly applicable to every kind of genome and represents an attractive tool for functional genomic and agronomic applications. It consists of chemical random mutagenesis followed by high-throughput scr...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110748/ https://www.ncbi.nlm.nih.gov/pubmed/33972605 http://dx.doi.org/10.1038/s41598-021-89237-w |
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author | Fanelli, Valentina Ngo, Kathie J. Thompson, Veronica L. Silva, Brennan R. Tsai, Helen Sabetta, Wilma Montemurro, Cinzia Comai, Luca Harmer, Stacey L. |
author_facet | Fanelli, Valentina Ngo, Kathie J. Thompson, Veronica L. Silva, Brennan R. Tsai, Helen Sabetta, Wilma Montemurro, Cinzia Comai, Luca Harmer, Stacey L. |
author_sort | Fanelli, Valentina |
collection | PubMed |
description | The Targeting Induced Local Lesions in Genomes (TILLING) technology is a reverse genetic strategy broadly applicable to every kind of genome and represents an attractive tool for functional genomic and agronomic applications. It consists of chemical random mutagenesis followed by high-throughput screening of point mutations in targeted genomic regions. Although multiple methods for mutation discovery in amplicons have been described, next-generation sequencing (NGS) is the tool of choice for mutation detection because it quickly allows for the analysis of a large number of amplicons. The aim of the present work was to screen a previously generated sunflower TILLING population and identify alterations in genes involved in several important and complex physiological processes. Twenty-one candidate sunflower genes were chosen as targets for the screening. The TILLING by sequencing strategy allowed us to identify multiple mutations in selected genes and we subsequently validated 16 mutations in 11 different genes through Sanger sequencing. In addition to addressing challenges posed by outcrossing, our detection and validation of mutations in multiple regulatory loci highlights the importance of this sunflower population as a genetic resource. |
format | Online Article Text |
id | pubmed-8110748 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81107482021-05-12 A TILLING by sequencing approach to identify induced mutations in sunflower genes Fanelli, Valentina Ngo, Kathie J. Thompson, Veronica L. Silva, Brennan R. Tsai, Helen Sabetta, Wilma Montemurro, Cinzia Comai, Luca Harmer, Stacey L. Sci Rep Article The Targeting Induced Local Lesions in Genomes (TILLING) technology is a reverse genetic strategy broadly applicable to every kind of genome and represents an attractive tool for functional genomic and agronomic applications. It consists of chemical random mutagenesis followed by high-throughput screening of point mutations in targeted genomic regions. Although multiple methods for mutation discovery in amplicons have been described, next-generation sequencing (NGS) is the tool of choice for mutation detection because it quickly allows for the analysis of a large number of amplicons. The aim of the present work was to screen a previously generated sunflower TILLING population and identify alterations in genes involved in several important and complex physiological processes. Twenty-one candidate sunflower genes were chosen as targets for the screening. The TILLING by sequencing strategy allowed us to identify multiple mutations in selected genes and we subsequently validated 16 mutations in 11 different genes through Sanger sequencing. In addition to addressing challenges posed by outcrossing, our detection and validation of mutations in multiple regulatory loci highlights the importance of this sunflower population as a genetic resource. Nature Publishing Group UK 2021-05-10 /pmc/articles/PMC8110748/ /pubmed/33972605 http://dx.doi.org/10.1038/s41598-021-89237-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Fanelli, Valentina Ngo, Kathie J. Thompson, Veronica L. Silva, Brennan R. Tsai, Helen Sabetta, Wilma Montemurro, Cinzia Comai, Luca Harmer, Stacey L. A TILLING by sequencing approach to identify induced mutations in sunflower genes |
title | A TILLING by sequencing approach to identify induced mutations in sunflower genes |
title_full | A TILLING by sequencing approach to identify induced mutations in sunflower genes |
title_fullStr | A TILLING by sequencing approach to identify induced mutations in sunflower genes |
title_full_unstemmed | A TILLING by sequencing approach to identify induced mutations in sunflower genes |
title_short | A TILLING by sequencing approach to identify induced mutations in sunflower genes |
title_sort | tilling by sequencing approach to identify induced mutations in sunflower genes |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110748/ https://www.ncbi.nlm.nih.gov/pubmed/33972605 http://dx.doi.org/10.1038/s41598-021-89237-w |
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