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Botía Blaya, Juan Antonio

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Botía Blaya, Juan Antonio
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Universidad de Murcia. Departamento de Ingeniería de la Informacióny las Comunicaciones
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    Modeling multifunctionality of genes with secondary gene co-expression networks in human brain provides novel disease insights
    (Oxford University Press, 2021-03-18) Sánchez Laguna, Juan Antonio; Gil Martínez, Ana Luisa; Cisterna, Alejandro; García Ruiz, Sonia; Gómez Pascual, Alicia; Reynolds, Regina H.; Botía Blaya, Juan Antonio; Nalls, Mike; Hardy, John; Ryten, Mina; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    Motivation Co-expression networks are a powerful gene expression analysis method to study how genes co-express together in clusters with functional coherence that usually resemble specific cell type behavior for the genes involved. They can be applied to bulk-tissue gene expression profiling and assign function, and usually cell type specificity, to a high percentage of the gene pool used to construct the network. One of the limitations of this method is that each gene is predicted to play a role in a specific set of coherent functions in a single cell type (i.e. at most we get a single for each gene). We present here GMSCA (Gene Multifunctionality Secondary Co-expression Analysis), a software tool that exploits the co-expression paradigm to increase the number of functions and cell types ascribed to a gene in bulk-tissue co-expression networks. Results We applied GMSCA to 27 co-expression networks derived from bulk-tissue gene expression profiling of a variety of brain tissues. Neurons and glial cells (microglia, astrocytes and oligodendrocytes) were considered the main cell types. Applying this approach, we increase the overall number of predicted triplets by 46.73%. Moreover, GMSCA predicts that the SNCA gene, traditionally associated to work mainly in neurons, also plays a relevant function in oligodendrocytes.
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    Open Access
    CoExp: A Web Tool for the Exploitation of Co-expression Networks
    (Frontiers Media SA, 2021-02-24) García-Ruiz, Sonia ; Cisterna, Alejandro; Jurado-Ruiz, Federico; Reynolds, Regina H. ; NABEC (North American Brain Expression Consortium); Cookson, Mark R.; Hardy, John ; Ryten, Mina; Gil Martínez, Ana Luisa; Botía Blaya, Juan Antonio; Ingeniería de la Información y las Comunicaciones
    Gene co-expression networks are a powerful type of analysis to construct gene groupings based on transcriptomic profiling. Co-expression networks make it possible to discover modules of genes whose mRNA levels are highly correlated across samples. Subsequent annotation of modules often reveals biological functions and/or evidence of cellular specificity for cell types implicated in the tissue being studied. There are multiple ways to perform such analyses with weighted gene co-expression network analysis (WGCNA) amongst one of the most widely used R packages. While managing a few network models can be done manually, it is often more advantageous to study a wider set of models derived from multiple independently generated transcriptomic data sets (e.g., multiple networks built from many transcriptomic sources). However, there is no software tool available that allows this to be easily achieved. Furthermore, the visual nature of co-expression networks in combination with the coding skills required to explore networks, makes the construction of a web-based platform for their management highly desirable. Here, we present the CoExp Web application, a user-friendly online tool that allows the exploitation of the full collection of 109 co-expression networks provided by the CoExpNets suite of R packages. We describe the usage of CoExp, including its contents and the functionality available through the family of CoExpNets packages. All the tools presented, including the web front- and back-ends are available for the research community so any research group can build its own suite of networks and make them accessible through their own CoExp Web application. Therefore, this paper is of interest to both researchers wishing to annotate their genes of interest across different brain network models and specialists interested in the creation of GCNs looking for a tool to appropriately manage, use, publish, and share their networks in a consistent and productive manner.
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    Open Access
    A predictive model for hospitalization and survival to COVID‑19 in a retrospective population‑based study
    (Nature Research, 2022-10-28) Cisterna García, Alejandro; Guillén Teruel, Antonio; Caracena, Marcos; Pérez-Cuadrado Martínez, Enrique; Jiménez Barrionuevo, Fernando; Francisco Verdú, Francisco J.; Reina, Gabriel; González Billalabeitia, Enrique; Palma Méndez, José Tomás; Sánchez Ferrer, Álvaro; Botía Blaya, Juan Antonio; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    The development of tools that provide early triage of COVID-19 patients with minimal use of diagnostic tests, based on easily accessible data, can be of vital importance in reducing COVID-19 mortality rates during high-incidence scenarios. This work proposes a machine learning model to predict mortality and risk of hospitalization using both 2 simple demographic features and 19 comorbidities obtained from 86,867 electronic medical records of COVID-19 patients, and a new method (LR-IPIP) designed to deal with data imbalance problems. The model was able to predict with high accuracy (90–93%, ROC-AUC = 0.94) the patient's final status (deceased or discharged), while its accuracy was medium (71–73%, ROC-AUC = 0.75) with respect to the risk of hospitalization. The most relevant characteristics for these models were age, sex, number of comorbidities, osteoarthritis, obesity, depression, and renal failure. Finally, to facilitate its use by clinicians, a user-friendly website has been developed (https://alejandrocisterna.shinyapps.io/PROVIA).
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    IntroVerse: a comprehensive database of introns across human tissues
    (Oxford University Press , 2022-11-18) Garcia-Ruiz, Sonia; Gustavsson, Emil K. ; Zhang, David; Reynolds, Regina H. ; Chen, Zhongbo; Fairbrother-Browne, Aine; Gil Martínez, Ana Luisa; Botía Blaya, Juan Antonio; Collado-Torres, Leonardo; Ryten, Mina; Ingeniería de la Información y las Comunicaciones; Facultades de la UMU::Facultad de Informática
    Dysregulation of RNA splicing contributes to both rare and complex diseases. RNA-sequencing data from human tissues has shown that this process can be inaccurate, resulting in the presence of novel introns detected at low frequency across samples and within an individual. To enable the full spectrum of intron use to be explored, we have developed Intro- Verse, which offers an extensive catalogue on the splicing of 332,571 annotated introns and a linked set of 4,679,474 novel junctions covering 32,669 different genes. This dataset has been generated through the analysis of 17,510 human control RNA samples from 54 tissues provided by the Genotype-Tissue Expression Consortium. IntroVerse has two unique features: (i) it provides a complete catalogue of novel junctions and (ii) each novel junction has been assigned to a specific annotated intron. This unique, hierarchical structure offers multiple uses, including the identification of novel transcripts from known genes and their tissue-specific usage, and the assessment of background splicing noise for introns thought to be mis-spliced in disease states. Intro-Verse provides a user-friendly web interface and is freely available at https://rytenlab.com/browser/app/introverse.
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