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GenoTypeMapper: graphical genotyping on genetic and sequence-based maps
BACKGROUND: The rising availability of assemblies of large genomes (e.g. bread and durum wheat, barley) and their annotations deliver the basis to graphically present genome organization of parents and progenies on a physical scale. Genetic maps are a very important tool for breeders but often repre...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7488165/ https://www.ncbi.nlm.nih.gov/pubmed/32944061 http://dx.doi.org/10.1186/s13007-020-00665-7 |
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author | Deblieck, Mathieu Fatiukha, Andrii Grundman, Norbert Merchuk-Ovnat, Lianne Saranga, Yehoshua Krugman, Tamar Pillen, Klaus Serfling, Albrecht Makalowski, Wojciech Ordon, Frank Perovic, Dragan |
author_facet | Deblieck, Mathieu Fatiukha, Andrii Grundman, Norbert Merchuk-Ovnat, Lianne Saranga, Yehoshua Krugman, Tamar Pillen, Klaus Serfling, Albrecht Makalowski, Wojciech Ordon, Frank Perovic, Dragan |
author_sort | Deblieck, Mathieu |
collection | PubMed |
description | BACKGROUND: The rising availability of assemblies of large genomes (e.g. bread and durum wheat, barley) and their annotations deliver the basis to graphically present genome organization of parents and progenies on a physical scale. Genetic maps are a very important tool for breeders but often represent distorted models of the actual chromosomes, e.g., in centromeric and telomeric regions. This biased picture might lead to imprecise assumptions and estimations about the size and complexity of genetic regions and the selection of suitable molecular markers for the incorporation of traits in breeding populations or near-isogenic lines (NILs). Some software packages allow the graphical illustration of genotypic data, but to the best of our knowledge, suitable software packages that allow the comparison of genotypic data on the physical and genetic scale are currently unavailable. RESULTS: We developed a simple Java-based-software called GenoTypeMapper (GTM) for comparing genotypic data on genetic and physical maps and tested it for effectiveness on data of two NILs that carry QTL-regions for drought stress tolerance from wild emmer on chromosome 2BS and 7AS. Both NILs were more tolerant to drought stress than their recurrent parents but exhibited additional undesirable traits such as delayed heading time. CONCLUSIONS: In this article, we illustrate that the software easily allows users to display and identify additional chromosomal introgressions in both NILs originating from the wild emmer parent. The ability to detect and diminish linkage drag can be of particular interest for pre-breeding purposes and the developed software is a well-suited tool in this respect. The software is based on a simple allele-matching algorithm between the offspring and parents of a crossing scheme. Despite this simple approach, GTM seems to be the only software that allows us to analyse, illustrate and compare genotypic data of offspring of different crossing schemes with up to four parents in two different maps. So far, up to 500 individuals with a maximum number of 50,000 markers can be examined with the software. The main limitation that hampers the performance of the software is the number of markers that are examined in parallel. Since each individual must be analysed separately, a maximum of ten individuals can currently be displayed in a single run. On a computer with an Intel five processor of the 8th generation, GTM can reliably either analyse a single individual with up to 12,000 markers or ten individuals with up to 3,600 markers in less than five seconds. Future work aims to improve the performance of the software so that more complex crossing schemes with more parents and more markers can be analysed. |
format | Online Article Text |
id | pubmed-7488165 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-74881652020-09-16 GenoTypeMapper: graphical genotyping on genetic and sequence-based maps Deblieck, Mathieu Fatiukha, Andrii Grundman, Norbert Merchuk-Ovnat, Lianne Saranga, Yehoshua Krugman, Tamar Pillen, Klaus Serfling, Albrecht Makalowski, Wojciech Ordon, Frank Perovic, Dragan Plant Methods Software BACKGROUND: The rising availability of assemblies of large genomes (e.g. bread and durum wheat, barley) and their annotations deliver the basis to graphically present genome organization of parents and progenies on a physical scale. Genetic maps are a very important tool for breeders but often represent distorted models of the actual chromosomes, e.g., in centromeric and telomeric regions. This biased picture might lead to imprecise assumptions and estimations about the size and complexity of genetic regions and the selection of suitable molecular markers for the incorporation of traits in breeding populations or near-isogenic lines (NILs). Some software packages allow the graphical illustration of genotypic data, but to the best of our knowledge, suitable software packages that allow the comparison of genotypic data on the physical and genetic scale are currently unavailable. RESULTS: We developed a simple Java-based-software called GenoTypeMapper (GTM) for comparing genotypic data on genetic and physical maps and tested it for effectiveness on data of two NILs that carry QTL-regions for drought stress tolerance from wild emmer on chromosome 2BS and 7AS. Both NILs were more tolerant to drought stress than their recurrent parents but exhibited additional undesirable traits such as delayed heading time. CONCLUSIONS: In this article, we illustrate that the software easily allows users to display and identify additional chromosomal introgressions in both NILs originating from the wild emmer parent. The ability to detect and diminish linkage drag can be of particular interest for pre-breeding purposes and the developed software is a well-suited tool in this respect. The software is based on a simple allele-matching algorithm between the offspring and parents of a crossing scheme. Despite this simple approach, GTM seems to be the only software that allows us to analyse, illustrate and compare genotypic data of offspring of different crossing schemes with up to four parents in two different maps. So far, up to 500 individuals with a maximum number of 50,000 markers can be examined with the software. The main limitation that hampers the performance of the software is the number of markers that are examined in parallel. Since each individual must be analysed separately, a maximum of ten individuals can currently be displayed in a single run. On a computer with an Intel five processor of the 8th generation, GTM can reliably either analyse a single individual with up to 12,000 markers or ten individuals with up to 3,600 markers in less than five seconds. Future work aims to improve the performance of the software so that more complex crossing schemes with more parents and more markers can be analysed. BioMed Central 2020-09-10 /pmc/articles/PMC7488165/ /pubmed/32944061 http://dx.doi.org/10.1186/s13007-020-00665-7 Text en © The Author(s) 2020 Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Deblieck, Mathieu Fatiukha, Andrii Grundman, Norbert Merchuk-Ovnat, Lianne Saranga, Yehoshua Krugman, Tamar Pillen, Klaus Serfling, Albrecht Makalowski, Wojciech Ordon, Frank Perovic, Dragan GenoTypeMapper: graphical genotyping on genetic and sequence-based maps |
title | GenoTypeMapper: graphical genotyping on genetic and sequence-based maps |
title_full | GenoTypeMapper: graphical genotyping on genetic and sequence-based maps |
title_fullStr | GenoTypeMapper: graphical genotyping on genetic and sequence-based maps |
title_full_unstemmed | GenoTypeMapper: graphical genotyping on genetic and sequence-based maps |
title_short | GenoTypeMapper: graphical genotyping on genetic and sequence-based maps |
title_sort | genotypemapper: graphical genotyping on genetic and sequence-based maps |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7488165/ https://www.ncbi.nlm.nih.gov/pubmed/32944061 http://dx.doi.org/10.1186/s13007-020-00665-7 |
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