I am a bioinformatician with a background in applied science, with a concentration in biochemistry and molecular biology. Throughout my life, I’ve been tinkering with computers, which naturally led me to support the projects I pursue. Past experiences involved developing computational proteomics tools that enable the discovery and application of novel phosphopeptides and glycopeptides with desired properties. I am currently enrolled as a Master student in Omics Data Analysis at the University of Vic - Central University of Catalonia (UVic-UCC). I enjoy exploring the intersection of different omics fields with the aid of computational tools. The projects listed below highlight some of those adventures.

Here is a list of recent projects I have been toying with on my free time.

1. Glycopeptide Proteoform Generator (https://github.com/RichardDShipman/glycopeptide_proteoform_generator)

• A script to generate proteoforms by reading glycopeptide data from a CSV file, with limits on the number of proteoforms, saving results in both CSV and text file formats for each protein.

2. Glycoproteomics Graph Tool (https://github.com/RichardDShipman/GlycoproteomicsGraphTool_release)

• Neo4j based graph knowledge base for storing glycoproteomics records in context to the central dogma of molecular biology.

Here is a list of past projects I have worked on, with links to related GitHub repositories if they are available.

1. Glycoproteomics Knowledge Database Development

• Developed a Neo4j graph database for glycomics, integrating public and proprietary multi-omics data to enhance biomarker discovery for various disease indications.

2. Glycoproteomics Mass Spectrometry Deep Learning Pipeline (https://github.com/Vennbiosciences/D-Va-GlycoML)

• Optimized a Python-based machine learning pipeline for glycoproteomics mass spectrometry, improving the accuracy of N-glycopeptide identification using deep learning models to analyze fragmentation patterns.

3. N-Linked Glycoform Categorization (10.1016/j.mcpro.2021.100081)

• Developed R scripts for categorizing glycoforms based on mass spectrometry data, contributing to the analysis of glycosylation patterns in diseases.

• Languages: Python, R, Bash
• Data Management: SQL, Neo4j - Cypher
• Data Visualization: R Shiny, Neodash, ggplot2
• Bioinformatics: Omics Data Analysis, Mass Spectrometry, Proteomics, Glycomics, Glycoproteomics