The general tasks performed were:* Integration of genomic epidemiology into the study of outbreaks caused by pathogenic microorganisms for epidemiology surveillance purposes. I have analysed whole-genome sequencing data obtained with Illumina and Oxford Nanopore (ONT) next-generation sequencing (NGS) technologies using bioinformatic pipelines developed in Python and R (for viruses and bacteria). The analyses performed include genome assembly of Illumina and/or ONT data, identification of resistance and plasmids, and phylogenetics (measurement of SNP distance).* Study of the phylodynamics of outbreaks/clusters using programs such as BEAST and TransPhylo in bash and R, respectively.* Development of a systematic identification pipeline for SARS-CoV-2 co-infections in Python. The pipeline enabled the integration of sequences to clarify nosocomial outbreaks where co-infection had complicated the analysis.* Analysis of shotgun metagenomics data from various clinical samples, such as sputum, urine, and serum, sequenced using ONT sequencing technologies. I performed the analysis with a custom pipeline that I developed in Snakemake, combining different bash scripts and bioinformatic tools.

The general tasks performed were:* Installation and configuration of the SLURM queue management system for the development, optimisation and parallelisation of bioinformatics genomic pipelines based on Bash and Python.* Analysis of high-throughput next-generation sequencing (NGS) data from Illumina and Oxford Nanopore technologies for variant calling in different pathogenic microorganisms: SARS-CoV-2 virus, Monkeypox virus and Mycobacterium tuberculosis with different Python-based pipelines.* Building and analysis of SNP databases from public sequences using Whole Genome Sequencing (WGS) data with Python and Excel.* Management and analysis of large databases of SARS-CoV-2 positive samples, using Excel and Python.

Master’s Thesis directed by Dr. Enrique Marcos Benteo and carried out at the Department of Protein Design and Molecular Modelling of the Institute of Molecular Biology of Barcelona (IBMB - CSIC). Title of the work: ‘Computational optimisation of de novo designed proteins for the binding of therapeutic targets’. In this work, I applied machine learning models to predict the folding and binding properties of de novo proteins, using a large dataset of computational and experimental data. Furthermore, I cross-validated the designs with different computational methods to improve the design selection and reduce the experimental testing.

Final Degree Project supervised by Dr. Jesús Pérez Gil and Dr. Begoña García Álvarez, as part of the ‘Biophysics of membranes and lipoprotein interfaces’ group within the Biochemistry Department of the Complutense University of Madrid. Title of the work: ‘Pulmonary surfactant protein C (SP-C) oligomerization effect in membrane fragmentation, vesicle uptake and innate immunity’.