scholarly journals A functional m 6 A‐RNA methylation pathway in the oyster Crassostrea gigas assumes epitranscriptomic regulation of lophotrochozoan development

FEBS Journal ◽  
2020 ◽  
Author(s):  
Lorane Le Franc ◽  
Benoit Bernay ◽  
Bruno Petton ◽  
Marc Since ◽  
Pascal Favrel ◽  
...  
Keyword(s):  
Crassostrea Gigas ◽  
Rna Methylation ◽  
Methylation Pathway

Retos de la acuicultura marina litoral: Caso cultivo de ostras en el Golfo de Nicoya, Costa Rica

2019 ◽  
Author(s):  
Rebeca Quesada Céspedes ◽  
Sidey Arias Valverde ◽  
Oscar Pacheco Urpí ◽  
Gerardo Zúñiga Calero ◽  
Oscar José Pacheco Prieto ◽  
...  
Keyword(s):  
Costa Rica ◽  
Crassostrea Gigas

Costa Rica muestra una condición incipiente en el desarrollo de la acuicultura marina, a pesar de los esfuerzos invertidos por más de veinte años. La experiencia en el cultivo de Crassostrea gigas alcanzada por comunidades, la institucionalidad pública y ONG´s, desde el ámbito social- económico y ambiental, evidencian que el país cuenta con las condiciones idóneas, para direccionar la ostricultura como un nuevo sector socio-económico con y desde las comunidades marino costeras del litoral pacífico. El presente trabajo expone los logros obtenidos por comunidades y actores locales; además los retos que ambas partes se han propuesto para hacer de esta actividad una herramienta más que contribuye con la sustentabilidad y empoderamiento de familias y comunidades que actualmente se encuentran en condiciones de pobreza.

Clustering Count-based RNA Methylation Data Using a Nonparametric Generative Model

2018 ◽  
Vol 14 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Lin Zhang ◽  
Yanling He ◽  
Huaizhi Wang ◽  
Hui Liu ◽  
Yufei Huang ◽  
...  
Keyword(s):  
Clustering Analysis ◽  
Methylation Level ◽  
Optimal Number ◽  
Generative Model ◽  
Methylation Data ◽  
Sequencing Data ◽  
Number Of Clusters ◽  
Rna Methylation ◽  
Clustering Effect ◽  
Optimal Number Of Clusters

Background: RNA methylome has been discovered as an important layer of gene regulation and can be profiled directly with count-based measurements from high-throughput sequencing data. Although the detailed regulatory circuit of the epitranscriptome remains uncharted, clustering effect in methylation status among different RNA methylation sites can be identified from transcriptome-wide RNA methylation profiles and may reflect the epitranscriptomic regulation. Count-based RNA methylation sequencing data has unique features, such as low reads coverage, which calls for novel clustering approaches. <P><P> Objective: Besides the low reads coverage, it is also necessary to keep the integer property to approach clustering analysis of count-based RNA methylation sequencing data. <P><P> Method: We proposed a nonparametric generative model together with its Gibbs sampling solution for clustering analysis. The proposed approach implements a beta-binomial mixture model to capture the clustering effect in methylation level with the original count-based measurements rather than an estimated continuous methylation level. Besides, it adopts a nonparametric Dirichlet process to automatically determine an optimal number of clusters so as to avoid the common model selection problem in clustering analysis. <P><P> Results: When tested on the simulated system, the method demonstrated improved clustering performance over hierarchical clustering, K-means, MClust, NMF and EMclust. It also revealed on real dataset two novel RNA N6-methyladenosine (m6A) co-methylation patterns that may be induced directly by METTL14 and WTAP, which are two known regulatory components of the RNA m6A methyltransferase complex. <P><P> Conclusion: Our proposed DPBBM method not only properly handles the count-based measurements of RNA methylation data from sites of very low reads coverage, but also learns an optimal number of clusters adaptively from the data analyzed. <P><P> Availability: The source code and documents of DPBBM R package are freely available through the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/DPBBM/.

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