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@kobor-lab @korthauer-lab

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ErickNavarroD/README.md

Hola 👋

My name is Erick (he/him), and I am a Bioinformatician currently doing his PhD at the University of British Columbia.

Born and raised in Mexico, I completed a B.Sc. in Biology before transitioning to full-time computational work. I have expertise in high-dimensional statistics, software development and omic analyses. Most of my work is developed with R, but I am proficient in Python and SQL too if needed 🐍.

I'm a big fan of open-source code, community-based research and accesible capacity building.

Which kind of work do I do?

I have been conducting biomedical research since 2015, learning wet and dry lab techniques in a wide variety of fields such as immunology, aging, cancer, and epigenomics. I am currently interested in developing computational tools to integrate genome, exposome and DNA methylome data to model the factors that contribute to DNA methylation variability across early life. I am approaching this with a multidisciplinary lens that connects biology, psychosocial disciplines and computer science, with the aim of improving our understanding of how and under which conditions individual genetic susceptibility and environmental exposures may work together to influence DNA methylation in a highly sensitive human developmental period.

Another passion of mine is science outreach. I have ongoing projects to build coding skills (mainly R) in students through free workshops, specially targeted at the Latinx community. If you have any idea or want to collaborate, please send me an email! I am always excited to participate in activities that make scientific knowledge more accessible.

Wanna know more about me?

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  1. hum_progranulin_RNAseq_Leavitt hum_progranulin_RNAseq_Leavitt Public

    RNA-seq analysis conducted for the paper FTD-associated behavioral and transcriptomic abnormalities in ‘humanized’ progranulin-deficient mice: a novel model for progranulin-associated FTD

    R

  2. RAMEN RAMEN Public

    Regional Association of Methylation variability with the Exposome and geNome (RAMEN) is an R package whose goal is to identify Variable Methylated Regions (VMRs) in microarray DNA methylation data.…

    R 4

  3. MachineLearningPCOS MachineLearningPCOS Public

    Development of a predictive Machine Learning model for PCOS screening