Welcome to my portfolio. My work falls within the field of bioinformatics—the intersection of biology, statistics, and computer science. My current research focuses on quantitative genetics and evolutionary biology, with special interest in missing data methodologies and stickleback fish evolution (detailed here). Below is a selection of projects that showcase my understanding of:

  • Genomic analysis pipelines
  • Statistical methods for biological data
  • Microbiome data processing
  • Evolutionary modeling and simulation
  • Programming in R and Python I invite you to explore these projects and discover how computational biology can transform raw data into scientific insights.

5 items under this folder.

Gut Microbiome Analysis

A pipeline focused on processing 16S rRNA amplicon sequencing data using the DADA2 methodology.

LongReads - Analyzing Bacterial 16S Variation

Project Overview LongReads is a specialized bioinformatics pipeline I developed to analyze and compare the variation of 16S rRNA regions both within (intragenomic) and between (intergenomic) strains of bacterial species.

Missing Data Handling in Quantitative Genetics - CCA vs MI

Project Overview This project investigates how different missing data handling methods affect the analysis of evolutionary potential in biological populations.

Testing Fisher's Geometric Model in Stickleback Evolution

Project Overview This research project investigated the evolutionary dynamics of threespine stickleback fish (Gasterosteus aculeatus) through the lens of Fisher’s Geometric Model of Adaptation.

Viral Genome Analysis Pipeline - HCMV Assembly and Characterization

Project Overview This project involved the development of a comprehensive bioinformatics pipeline for the detection, assembly, and characterization of Human Cytomegalovirus (HCMV) genomes from next-generation sequencing data.