Ruth Barral-Arca
Bioinformatic
“Disease and adaptation are two sides of the same coin”
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Ruth Barral Arca is a Biology graduate of the University of Santiago de Compostela (2014). During her bachelor’s degree, she was granted a scholarship called “Sergas pre-professional practices” that allowed her to join a training period at the laboratory of clinical analysis of A Coruña University Hospital.
In 2015 she studied a master’s degree in biomedical research at the University of Santiago de Compostela, she graduated top of her class being awarded the Outstanding Graduate of the Year award. During her master’s degree, she participated in a project about mitochondrial DNA variability within the Iberian Peninsula, at the Luis Concheiro Institute of forensic sciences, under the supervision of Pr. Antonio Salas.
In July 2016 she obtained the predoctoral fellowship “Axudas de apoio á etapa predoutoral nas universidades do SUG”. Her research focuses on pediatric infectious diseases, focusing on unravelling how the host transcriptome is affected by infectious diseases and vaccines using machine learning algorithms and statistical modelling. She is particularly interested in transcriptomics and its integration with epigenomic data.
She has collaborated in the development of an R package for the detection of differentially methylated regions through a Kernel regression sliding-window approach (METKMR), that also allows the integration of transcriptomic and epigenomic data. As a result of her research in 2019, she obtained a European Patent for the discovery of two host LncRNAs biomarkers of viral infections. The results of her research have been published in peer-reviewed international journals such as RNA, Scientific Reports, Plos One, Genes etc.
Also, in 2019, she obtained an MSc in Omic data analysis from the University of Vic. The program covered genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, interactomics and integromics and provided knowledge of the most important techniques for omic data acquisition and analysis.
Currently, she is a PhD candidate who will have her thesis dissertation in 2020.
She has developed skills in Statistics, machine learning: supervised and unsupervised methods, programming (R, Python), linux bash scripting, genomics data analysis (DNA Array-Based Gene Profiling and NGS): analysis of Complex Diseases Association Studies, GWAS, transcriptomics data analysis: RNA-seq, microarray , epigenomics data analysis: microarray and Bisulfite sequencing, metagenomics data analysis and multi-omic data analysis.
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