Bayesian inference of gene expression

Víctor Jiménez-Jiménez, Carlos Martí-Gómez, Miguel Ángel del Pozo, Enrique Lara-Pezzi and Fátima Sánchez-Cabo.


Omics techniques have changed the way we depict the molecular features of a cell. The integrative and quantitative analysis of omics data raises unprecedented expectations for understanding biological systems on a global scale. However, its inherently noisy nature, together with limited knowledge of potential sources of variation impacting health and disease, require the use of proper mathematical and computational methods for its analysis and integration. Bayesian inference of probabilistic models allows propagation of the uncertainty from the experimental data to our beliefs of the model parameters, allowing us to appropriately answer complex biological questions. In this chapter, we build probabilistic models of gene expression from RNA-seq data and make inference about their parameters using Bayesian methods. We present models of increasing complexity, from the quantification of a single gene expression to differential gene expression for a whole transcriptome, comparing them to the available tools for analysis of gene expression data. We provide Stan scripts that introduce the reader into the implementation of Bayesian statistics for omics data. The rationale that we apply for transcriptomics data may be easily extended to model the particularities of other omics data and to integrate the different regulatory layers.

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Application of the Spider Silk Standardization Initiative (S3I) methodology to the characterization of major ampullate gland silk fibers spun by spiders from Pantanos de Villa wetlands (Lima, Peru)

Javier Garrote, Víctor Ruiz, Omar P. Troncoso, Fernando G. Torres, Miquel Arnedo, Manuel Elices, Gustavo V. Guinea, José Pérez-Rigueiro.

Abstract: Spider silk is a natural material with unique properties and a great potential for engineering and biomedical applications. In spite of its simple composition and highly conserved and stereotypical production, spider silks show a wide range of variability in their mechanical properties which, for long, have defied their classification and standardization. Here we propose to launch the Spider Silk Standardization Initiative (S3I), a methodology based on the definition of the α* parameter, in an attempt to define a systematic procedure to classify the tensile properties exhibited by major ampullate gland silk (MAS) spun by Entelegynae spiders. The α* parameter is calculated from the comparison of the true stress-true strain curve of any MAS fiber after being subjected to maximum supercontraction, with the true stress-true strain curve of the species Argiope aurantia, which is set as a reference curve. This work presents the details of the S3I methodology and, as an example, shows its application to an assemblage of Entelegynae spiders from different families collected at the Pantanos de Villa wetlands (Lima, Peru). The systematic and objective classification of the tensile properties of MAS fibers allowed by the S3I will offer insights into key aspects of the biological evolution of the material, and address questions such as how history and adaptation contributed to shape those properties. In addition, it will surely have far reaching consequences in fields such as Materials Science, and Molecular and Evolutionary Biology, by organizing the range of tensile properties exhibited by spider silk fibers.

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