Morphology Clustering Software for AFM Images, Based on Particle Isolation and Artificial Neural Networks

Advanced microscopy techniques currently allow scientists to visualize biomolecules at high resolution. Among them, atomic force microscopy (AFM) shows the advantage of imaging molecules in their native state, without requiring any staining or coating of the sample. Biopolymers, including proteins a...

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Autores principales:	Soledad Delgado, Miguel Moreno, Luis F. Vazquez, Jose Angel Martingago, Carlos Briones
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2019
Materias:	Artificial neural networks atomic force microscopy (AFM) biomolecules growing cell structures (GCS) hepatitis C virus (HCV) Image analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/9b13c643379442cf84ef22dc09941a30
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spelling	oai:doaj.org-article:9b13c643379442cf84ef22dc09941a302021-11-19T00:04:24ZMorphology Clustering Software for AFM Images, Based on Particle Isolation and Artificial Neural Networks2169-353610.1109/ACCESS.2019.2950984https://doaj.org/article/9b13c643379442cf84ef22dc09941a302019-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/8890647/https://doaj.org/toc/2169-3536Advanced microscopy techniques currently allow scientists to visualize biomolecules at high resolution. Among them, atomic force microscopy (AFM) shows the advantage of imaging molecules in their native state, without requiring any staining or coating of the sample. Biopolymers, including proteins and structured nucleic acids, are flexible molecules that can fold into alternative conformations for any given monomer sequence, as exemplified by the different three-dimensional structures adopted by RNA in solution. Therefore, the manual analysis of images visualized by AFM and other microscopy techniques becomes very laborious and time-consuming (and may also be inadvertently biased) when large populations of biomolecules are studied. Here we present a novel morphology clustering software, based on particle isolation and artificial neural networks, which allows the automatic image analysis and classification of biomolecules that can show alternative conformations. It has been tested with a set of AFM images of RNA molecules (a 574 nucleotides-long functinal region of the hepatitis C virus genome that contains its internal ribosome entry site element) structured in folding buffers containing 0, 2, 4, 6 or 10 mM Mg<sup>2+</sup>. The developed software shows a broad applicability in the microscopy-based analysis of biopolymers and other complex biomolecules.Soledad DelgadoMiguel MorenoLuis F. VazquezJose Angel MartingagoCarlos BrionesIEEEarticleArtificial neural networksatomic force microscopy (AFM)biomoleculesgrowing cell structures (GCS)hepatitis C virus (HCV)Image analysisElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 7, Pp 160304-160323 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Artificial neural networks atomic force microscopy (AFM) biomolecules growing cell structures (GCS) hepatitis C virus (HCV) Image analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Artificial neural networks atomic force microscopy (AFM) biomolecules growing cell structures (GCS) hepatitis C virus (HCV) Image analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971 Soledad Delgado Miguel Moreno Luis F. Vazquez Jose Angel Martingago Carlos Briones Morphology Clustering Software for AFM Images, Based on Particle Isolation and Artificial Neural Networks
description	Advanced microscopy techniques currently allow scientists to visualize biomolecules at high resolution. Among them, atomic force microscopy (AFM) shows the advantage of imaging molecules in their native state, without requiring any staining or coating of the sample. Biopolymers, including proteins and structured nucleic acids, are flexible molecules that can fold into alternative conformations for any given monomer sequence, as exemplified by the different three-dimensional structures adopted by RNA in solution. Therefore, the manual analysis of images visualized by AFM and other microscopy techniques becomes very laborious and time-consuming (and may also be inadvertently biased) when large populations of biomolecules are studied. Here we present a novel morphology clustering software, based on particle isolation and artificial neural networks, which allows the automatic image analysis and classification of biomolecules that can show alternative conformations. It has been tested with a set of AFM images of RNA molecules (a 574 nucleotides-long functinal region of the hepatitis C virus genome that contains its internal ribosome entry site element) structured in folding buffers containing 0, 2, 4, 6 or 10 mM Mg<sup>2+</sup>. The developed software shows a broad applicability in the microscopy-based analysis of biopolymers and other complex biomolecules.
format	article
author	Soledad Delgado Miguel Moreno Luis F. Vazquez Jose Angel Martingago Carlos Briones
author_facet	Soledad Delgado Miguel Moreno Luis F. Vazquez Jose Angel Martingago Carlos Briones
author_sort	Soledad Delgado
title	Morphology Clustering Software for AFM Images, Based on Particle Isolation and Artificial Neural Networks
title_short	Morphology Clustering Software for AFM Images, Based on Particle Isolation and Artificial Neural Networks
title_full	Morphology Clustering Software for AFM Images, Based on Particle Isolation and Artificial Neural Networks
title_fullStr	Morphology Clustering Software for AFM Images, Based on Particle Isolation and Artificial Neural Networks
title_full_unstemmed	Morphology Clustering Software for AFM Images, Based on Particle Isolation and Artificial Neural Networks
title_sort	morphology clustering software for afm images, based on particle isolation and artificial neural networks
publisher	IEEE
publishDate	2019
url	https://doaj.org/article/9b13c643379442cf84ef22dc09941a30
work_keys_str_mv	AT soledaddelgado morphologyclusteringsoftwareforafmimagesbasedonparticleisolationandartificialneuralnetworks AT miguelmoreno morphologyclusteringsoftwareforafmimagesbasedonparticleisolationandartificialneuralnetworks AT luisfvazquez morphologyclusteringsoftwareforafmimagesbasedonparticleisolationandartificialneuralnetworks AT joseangelmartingago morphologyclusteringsoftwareforafmimagesbasedonparticleisolationandartificialneuralnetworks AT carlosbriones morphologyclusteringsoftwareforafmimagesbasedonparticleisolationandartificialneuralnetworks
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Morphology Clustering Software for AFM Images, Based on Particle Isolation and Artificial Neural Networks

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