Mostrar el registro sencillo del ítem
dc.contributor.author
Manacorda, Carlos Augusto
dc.contributor.author
Asurmendi, Sebastian
dc.date.available
2022-11-02T12:39:55Z
dc.date.issued
2018-06
dc.identifier.citation
Manacorda, Carlos Augusto; Asurmendi, Sebastian; Arabidopsis phenotyping through geometric morphometrics; Oxford University Press; GigaScience; 7; 7; 6-2018; 1-20
dc.identifier.issn
2047-217X
dc.identifier.uri
http://hdl.handle.net/11336/175942
dc.description.abstract
Background: Recently, great technical progress has been achieved in the field of plant phenotyping. High-throughput platforms and the development of improved algorithms for rosette image segmentation make it possible to extract shape and size parameters for genetic, physiological, and environmental studies on a large scale. The development of low-cost phenotyping platforms and freeware resources make it possible to widely expand phenotypic analysis tools for Arabidopsis. However, objective descriptors of shape parameters that could be used independently of the platform and segmentation software used are still lacking, and shape descriptions still rely on ad hoc or even contradictory descriptors, which could make comparisons difficult and perhaps inaccurate. Modern geometric morphometrics is a family of methods in quantitative biology proposed to be the main source of data and analytical tools in the emerging field of phenomics studies. Based on the location of landmarks (corresponding points) over imaged specimens and by combining geometry, multivariate analysis, and powerful statistical techniques, these tools offer the possibility to reproducibly and accurately account for shape variations among groups and measure them in shape distance units. Results: Here, a particular scheme of landmark placement on Arabidopsis rosette images is proposed to study shape variation in viral infection processes. Shape differences between controls and infected plants are quantified throughout the infectious process and visualized. Quantitative comparisons between two unrelated ssRNA+ viruses are shown, and reproducibility issues are assessed. Conclusions: Combined with the newest automated platforms and plant segmentation procedures, geometric morphometric tools could boost phenotypic features extraction and processing in an objective, reproducible manner.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Oxford University Press
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ARABIDOPSIS
dc.subject
GEOMETRIC MORPHOMETRICS
dc.subject
LANDMARKS
dc.subject
ORMV
dc.subject
PHENOTYPING
dc.subject
PROCRUSTES ANALYSIS
dc.subject
TUMV
dc.subject.classification
Bioquímica y Biología Molecular
dc.subject.classification
Ciencias Biológicas
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Arabidopsis phenotyping through geometric morphometrics
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-11-01T23:21:19Z
dc.journal.volume
7
dc.journal.number
7
dc.journal.pagination
1-20
dc.journal.pais
Reino Unido
dc.description.fil
Fil: Manacorda, Carlos Augusto. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentina
dc.description.fil
Fil: Asurmendi, Sebastian. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Biotecnología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
GigaScience
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giy073/5039702
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1093/gigascience/giy073
Archivos asociados