Repositorio UFRO · Omeka S

CRISPR-Cas-Based Biomonitoring for Marine Environments: Toward CRISPR RNA Design Optimization Via Deep Learning

Primer Autor	Gemmell, Neil J.
Co-autores	Duran-Vinet, Benjamin Araya-Castro, Karla Zaiko, Anastasija Pochon, Xavier Wood, Susanna A. Stanton, Jo-Ann L. Jeunen, Gert-Jan Scriver, Michelle Kardailsky, Anya Chao, Tzu-Chiao Ban, Deependra K. Moarefian, Maryam Aran, Kiana
Título	CRISPR-Cas-Based Biomonitoring for Marine Environments: Toward CRISPR RNA Design Optimization Via Deep Learning
Editorial	MARY ANN LIEBERT, INC
Revista	CRISPR JOURNAL
Lenguaje	en
Resumen	Almost all of Earth's oceans are now impacted by multiple anthropogenic stressors, including the spread of nonindigenous species, harmful algal blooms, and pathogens. Early detection is critical to manage these stressors effectively and to protect marine systems and the ecosystem services they provide. Molecular tools have emerged as a promising solution for marine biomonitoring. One of the latest advancements involves utilizing CRISPR-Cas technology to build programmable, rapid, ultrasensitive, and specific diagnostics. CRISPR-based diagnostics (CRISPR-Dx) has the potential to allow robust, reliable, and cost-effective biomonitoring in near real time. However, several challenges must be overcome before CRISPR-Dx can be established as a mainstream tool for marine biomonitoring. A critical unmet challenge is the need to design, optimize, and experimentally validate CRISPR-Dx assays. Artificial intelligence has recently been presented as a potential approach to tackle this challenge. This perspective synthesizes recent advances in CRISPR-Dx and machine learning modeling approaches, showcasing CRISPR-Dx potential to progress as a rising molecular tool candidate for marine biomonitoring applications.
Fecha Publicación	2023
Tipo de Recurso	artículo original
doi	10.1089/crispr.2023.0019
Formato Recurso	PDF
Palabras Claves	Marine Environments CRISPR-Cas-Based Biomonitoring Deep learning
Ubicación del archivo	http://dx.doi.org/10.1089/crispr.2023.0019
Categoría OCDE	Genética y herencia
Materias	Ambientes Marinos Biomonitoreo basado en CRISPR-Cas Deep learning
Página de inicio (Recomendado-único)	316.0
Página final (Recomendado-único)	324
Identificador del recurso (Mandatado-único)	artículo original
Versión del recurso (Recomendado-único)	versión publicada
Derechos de acceso	metadata
Access Rights	metadata
Id de Web of Science	WOS:001027934400001
ISSN	2573-1599
Tipo de ruta	verde
Categoría WOS	Genética y herencia
Referencia del Financiador (Mandatado si es aplicable-repetible)	ANID-FONDECYT 3220346 MBIE CO5X1707 NIH 1R01HL161361-01 NSF 2048283.