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Combining Partially Overlapping Multi-Omics Data in Databases Using Relationship Matrices

Private and public breeding programs, as well as companies and universities, have developed different genomics technologies that have resulted in the generation of unprecedented amounts of sequence data, which bring new challenges in terms of data management, query, and analysis. The magnitude and c...

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Autores principales: Akdemir, Deniz, Knox, Ron, Isidro y Sánchez, Julio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7381228/
https://www.ncbi.nlm.nih.gov/pubmed/32765543
http://dx.doi.org/10.3389/fpls.2020.00947
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author Akdemir, Deniz
Knox, Ron
Isidro y Sánchez, Julio
author_facet Akdemir, Deniz
Knox, Ron
Isidro y Sánchez, Julio
author_sort Akdemir, Deniz
collection PubMed
description Private and public breeding programs, as well as companies and universities, have developed different genomics technologies that have resulted in the generation of unprecedented amounts of sequence data, which bring new challenges in terms of data management, query, and analysis. The magnitude and complexity of these datasets bring new challenges but also an opportunity to use the data available as a whole. Detailed phenotype data, combined with increasing amounts of genomic data, have an enormous potential to accelerate the identification of key traits to improve our understanding of quantitative genetics. Data harmonization enables cross-national and international comparative research, facilitating the extraction of new scientific knowledge. In this paper, we address the complex issue of combining high dimensional and unbalanced omics data. More specifically, we propose a covariance-based method for combining partial datasets in the genotype to phenotype spectrum. This method can be used to combine partially overlapping relationship/covariance matrices. Here, we show with applications that our approach might be advantageous to feature imputation based approaches; we demonstrate how this method can be used in genomic prediction using heterogeneous marker data and also how to combine the data from multiple phenotypic experiments to make inferences about previously unobserved trait relationships. Our results demonstrate that it is possible to harmonize datasets to improve available information across gene-banks, data repositories, or other data resources.
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spelling pubmed-73812282020-08-05 Combining Partially Overlapping Multi-Omics Data in Databases Using Relationship Matrices Akdemir, Deniz Knox, Ron Isidro y Sánchez, Julio Front Plant Sci Plant Science Private and public breeding programs, as well as companies and universities, have developed different genomics technologies that have resulted in the generation of unprecedented amounts of sequence data, which bring new challenges in terms of data management, query, and analysis. The magnitude and complexity of these datasets bring new challenges but also an opportunity to use the data available as a whole. Detailed phenotype data, combined with increasing amounts of genomic data, have an enormous potential to accelerate the identification of key traits to improve our understanding of quantitative genetics. Data harmonization enables cross-national and international comparative research, facilitating the extraction of new scientific knowledge. In this paper, we address the complex issue of combining high dimensional and unbalanced omics data. More specifically, we propose a covariance-based method for combining partial datasets in the genotype to phenotype spectrum. This method can be used to combine partially overlapping relationship/covariance matrices. Here, we show with applications that our approach might be advantageous to feature imputation based approaches; we demonstrate how this method can be used in genomic prediction using heterogeneous marker data and also how to combine the data from multiple phenotypic experiments to make inferences about previously unobserved trait relationships. Our results demonstrate that it is possible to harmonize datasets to improve available information across gene-banks, data repositories, or other data resources. Frontiers Media S.A. 2020-07-14 /pmc/articles/PMC7381228/ /pubmed/32765543 http://dx.doi.org/10.3389/fpls.2020.00947 Text en Copyright © 2020 Akdemir, Knox and Isidro y Sánchez http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Akdemir, Deniz
Knox, Ron
Isidro y Sánchez, Julio
Combining Partially Overlapping Multi-Omics Data in Databases Using Relationship Matrices
title Combining Partially Overlapping Multi-Omics Data in Databases Using Relationship Matrices
title_full Combining Partially Overlapping Multi-Omics Data in Databases Using Relationship Matrices
title_fullStr Combining Partially Overlapping Multi-Omics Data in Databases Using Relationship Matrices
title_full_unstemmed Combining Partially Overlapping Multi-Omics Data in Databases Using Relationship Matrices
title_short Combining Partially Overlapping Multi-Omics Data in Databases Using Relationship Matrices
title_sort combining partially overlapping multi-omics data in databases using relationship matrices
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7381228/
https://www.ncbi.nlm.nih.gov/pubmed/32765543
http://dx.doi.org/10.3389/fpls.2020.00947
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