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Why are some plants taller? Researchers on the unveiling of genetic variation associated with complex quantitative phenotypes
Francisco Pereira Lobo, Giovanni Marques de Castro, and Felipe Campelo are part of an international team of collaborators that developed CALANGO, a comparative genomics tool to investigate quantitative genotype-phenotype relationships. Their Patterns article highlights how the tool integrates specie...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318352/ https://www.ncbi.nlm.nih.gov/pubmed/37409052 http://dx.doi.org/10.1016/j.patter.2023.100774 |
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author | de Castro, Giovanni Marques Campelo, Felipe Lobo, Francisco Pereira |
author_facet | de Castro, Giovanni Marques Campelo, Felipe Lobo, Francisco Pereira |
author_sort | de Castro, Giovanni Marques |
collection | PubMed |
description | Francisco Pereira Lobo, Giovanni Marques de Castro, and Felipe Campelo are part of an international team of collaborators that developed CALANGO, a comparative genomics tool to investigate quantitative genotype-phenotype relationships. Their Patterns article highlights how the tool integrates species-centric data to perform genome-wide search and detect genes potentially involved in the emergence of complex quantitative traits across species. Here, they talk about their view of data science, their experience with interdisciplinary research, and the potential applications of their tool. |
format | Online Article Text |
id | pubmed-10318352 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-103183522023-07-05 Why are some plants taller? Researchers on the unveiling of genetic variation associated with complex quantitative phenotypes de Castro, Giovanni Marques Campelo, Felipe Lobo, Francisco Pereira Patterns (N Y) People of Data Francisco Pereira Lobo, Giovanni Marques de Castro, and Felipe Campelo are part of an international team of collaborators that developed CALANGO, a comparative genomics tool to investigate quantitative genotype-phenotype relationships. Their Patterns article highlights how the tool integrates species-centric data to perform genome-wide search and detect genes potentially involved in the emergence of complex quantitative traits across species. Here, they talk about their view of data science, their experience with interdisciplinary research, and the potential applications of their tool. Elsevier 2023-06-09 /pmc/articles/PMC10318352/ /pubmed/37409052 http://dx.doi.org/10.1016/j.patter.2023.100774 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | People of Data de Castro, Giovanni Marques Campelo, Felipe Lobo, Francisco Pereira Why are some plants taller? Researchers on the unveiling of genetic variation associated with complex quantitative phenotypes |
title | Why are some plants taller? Researchers on the unveiling of genetic variation associated with complex quantitative phenotypes |
title_full | Why are some plants taller? Researchers on the unveiling of genetic variation associated with complex quantitative phenotypes |
title_fullStr | Why are some plants taller? Researchers on the unveiling of genetic variation associated with complex quantitative phenotypes |
title_full_unstemmed | Why are some plants taller? Researchers on the unveiling of genetic variation associated with complex quantitative phenotypes |
title_short | Why are some plants taller? Researchers on the unveiling of genetic variation associated with complex quantitative phenotypes |
title_sort | why are some plants taller? researchers on the unveiling of genetic variation associated with complex quantitative phenotypes |
topic | People of Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10318352/ https://www.ncbi.nlm.nih.gov/pubmed/37409052 http://dx.doi.org/10.1016/j.patter.2023.100774 |
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