Prediction of RNA subcellular localization: Learning from heterogeneous data sources

Summary: RNA subcellular localization has recently emerged as a widespread phenomenon, which may apply to the majority of RNAs. The two main sources of data for characterization of RNA localization are sequence features and microscopy images, such as obtained from single-molecule fluorescent in situ...

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Autores principales:	Anca Flavia Savulescu, Emmanuel Bouilhol, Nicolas Beaume, Macha Nikolski
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Cell biology Transcriptomics Machine learning Science Q
Acceso en línea:	https://doaj.org/article/c13daf0585c841d29ced38766221a951
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spelling	oai:doaj.org-article:c13daf0585c841d29ced38766221a9512021-11-20T05:09:36ZPrediction of RNA subcellular localization: Learning from heterogeneous data sources2589-004210.1016/j.isci.2021.103298https://doaj.org/article/c13daf0585c841d29ced38766221a9512021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2589004221012670https://doaj.org/toc/2589-0042Summary: RNA subcellular localization has recently emerged as a widespread phenomenon, which may apply to the majority of RNAs. The two main sources of data for characterization of RNA localization are sequence features and microscopy images, such as obtained from single-molecule fluorescent in situ hybridization-based techniques. Although such imaging data are ideal for characterization of RNA distribution, these techniques remain costly, time-consuming, and technically challenging. Given these limitations, imaging data exist only for a limited number of RNAs. We argue that the field of RNA localization would greatly benefit from complementary techniques able to characterize location of RNA. Here we discuss the importance of RNA localization and the current methodology in the field, followed by an introduction on prediction of location of molecules. We then suggest a machine learning approach based on the integration between imaging localization data and sequence-based data to assist in characterization of RNA localization on a transcriptome level.Anca Flavia SavulescuEmmanuel BouilholNicolas BeaumeMacha NikolskiElsevierarticleCell biologyTranscriptomicsMachine learningScienceQENiScience, Vol 24, Iss 11, Pp 103298- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Cell biology Transcriptomics Machine learning Science Q
spellingShingle	Cell biology Transcriptomics Machine learning Science Q Anca Flavia Savulescu Emmanuel Bouilhol Nicolas Beaume Macha Nikolski Prediction of RNA subcellular localization: Learning from heterogeneous data sources
description	Summary: RNA subcellular localization has recently emerged as a widespread phenomenon, which may apply to the majority of RNAs. The two main sources of data for characterization of RNA localization are sequence features and microscopy images, such as obtained from single-molecule fluorescent in situ hybridization-based techniques. Although such imaging data are ideal for characterization of RNA distribution, these techniques remain costly, time-consuming, and technically challenging. Given these limitations, imaging data exist only for a limited number of RNAs. We argue that the field of RNA localization would greatly benefit from complementary techniques able to characterize location of RNA. Here we discuss the importance of RNA localization and the current methodology in the field, followed by an introduction on prediction of location of molecules. We then suggest a machine learning approach based on the integration between imaging localization data and sequence-based data to assist in characterization of RNA localization on a transcriptome level.
format	article
author	Anca Flavia Savulescu Emmanuel Bouilhol Nicolas Beaume Macha Nikolski
author_facet	Anca Flavia Savulescu Emmanuel Bouilhol Nicolas Beaume Macha Nikolski
author_sort	Anca Flavia Savulescu
title	Prediction of RNA subcellular localization: Learning from heterogeneous data sources
title_short	Prediction of RNA subcellular localization: Learning from heterogeneous data sources
title_full	Prediction of RNA subcellular localization: Learning from heterogeneous data sources
title_fullStr	Prediction of RNA subcellular localization: Learning from heterogeneous data sources
title_full_unstemmed	Prediction of RNA subcellular localization: Learning from heterogeneous data sources
title_sort	prediction of rna subcellular localization: learning from heterogeneous data sources
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/c13daf0585c841d29ced38766221a951
work_keys_str_mv	AT ancaflaviasavulescu predictionofrnasubcellularlocalizationlearningfromheterogeneousdatasources AT emmanuelbouilhol predictionofrnasubcellularlocalizationlearningfromheterogeneousdatasources AT nicolasbeaume predictionofrnasubcellularlocalizationlearningfromheterogeneousdatasources AT machanikolski predictionofrnasubcellularlocalizationlearningfromheterogeneousdatasources
_version_	1718419564856868864

Prediction of RNA subcellular localization: Learning from heterogeneous data sources

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