Bioinformatician

BioBam Bioinformatics S.L.

November 2021-Present

  • Deployment: Deployed 3 R environments with scRNA-Seq data analysis toolkits like Seurat, Monocle3 and SingleCellExperimnet using Docker on AWS Cloud.
  • Development: Contributed in the development of OmicsBox (BioBam’s flagship product based on the JAVA-OSGi framework) in 3 major release cycles.
  • Design: Implemented 2 modular and user-friendly interfaces for the scRNA-Seq Module in OmicsBox, improving user experience and efficiency.
  • Documentation: Publish blog posts and user manuals on BioBam’s website to educate users on the latest features and updates in OmicsBox regarding scRNA-Seq Data Analysis.

            View Blog     View User Manual


Bioinformatician

  • Workflow Development: Designed Snakemake workflow for analysing CUT&RUN data with highly abstracted rules.
  • Pipeline Implementation: Optimised the workflow to ensure accurate and efficient data processing by integrating command-line tools and containerised R/Python-environment for reproducible analysis.
  • Results Communication: Communicated and formulated bioinformatics results, including complex visualisations, to clinicians with non-computational backgrounds, facilitating understanding and informed decision-making.

           


Bioinformatics Data Analyst (Master’s Project)

Ryan Institute, University of Galway

June 2021 - August 2021

  • Metagenomic Analysis: Analysed solid samples from Chernobyl Red Forest for the assessment of microbial co-occurrence networks in a radiation-affected site to better understand the microbial community interactions.
  • Pipeline Development: Developed an end-to-end Nextflow pipeline, from raw reads to inferring co-occurrence networks using Cytoscape command line plugins, ensuring a seamless analysis

           


Bioinformatics Data Analyst (Bachelor’s Project)

  • Designed a Python and shell script-based pipeline for the quantification of raw bulk-RNA seq reads.
  • Performed differential expression analysis and functional enrichment analysis on bulk-RNA seq data from tuberculosis samples over a time series of infections.
  • Applied Random Forest-based binary classifier to differentiate between progressive and non-progressive tuberculosis samples infection over the time series.

            Pre-print on BioArchive