Highlights

  1. Generation and analysis of the first somatic mutation landscape in a human population. I pioneered the development of computational methods and techniques that leverage public genomic data to understand the somatic mutation landscape in the human healthy body. In this project I had to process and analyze more than 10,000 sequencing samples originated from more than 500 people. I acquired the experience necessary to apply pipelines and statistical analyses to big data in an efficient fashion. I successfully integrated genomics, transcriptomics and phenotypic data to perform association analysis, which together have uncovered the molecular mechanisms underlying the acquisition of somatic mutations in the human body. Garcia-Nieto PE et. al. Genome Biology. 2019

  2. Understanding carcinogen susceptibility of the human genome. I led a project where we developed a sequencing technology to understand how sun-induced DNA damage accumulates in the human genome and its relevance to melanoma. This project expanded the current understanding of why and what regions of the human genome are more susceptible to DNA damage. Garcia-Nieto PE et. al. EMBO J. 2017

Other

  1. Retrotransposition in yeast I worked on analyzing yeast RNA-sequencing data to understand DNA transposition and how it is regulated by chromatin dynamics. Martín GA, King DA, Green EM, Garcia-Nieto PE, Alexander R, Collins SR, Krogan NJ, Gozani OP, Morrison AJ. Epigenetics. 2014

  2. Deciphering yeast genetic interaction networks. I worked in high-throughput genetic experiments in yeast, tackling experimental and bioinformatics tasks which led to the completion of a study that shed light to the interplay between chromatin and metabolism. Beckwith SL, Schwartz EK, García-Nieto PE, et al. PLoS Gen. 2018

  3. Genome-wide profiles of UV lesion susceptibility, repair, and mutagenic potential in melanoma. I worked on understanding how different types of UV induce DNA damage in the human genome. Perez BS, Wong KM, Schwartz EK, Herrera RE, King DA, García-Nieto PE, Morrison AJ. Mutat Res. 2021

  4. Identification of a simple metric that underlies global variance in RNA-seq data. I showed that that transcriptome diversity — a simple metric based on Shannon entropy — explains a large portion of variability in both gene expression measurements as well as the confounding factors detected by a leading method (PEER). This prevalent factor provides a simple explanation for a primary source of variation in gene expression estimates. Garcia-Nieto PE et. al. PLoS Comput Biol. 2022