GlycoGenius: Advancing Automated Glycomics Data Analysis

Mass spectrometry-based glycomics produces highly complex datasets that require specialized computational tools for reliable glycan identification, structural annotation, and quantification. Traditionally, much of this processing has depended on manual interpretation, limiting throughput and reproducibility. To address these challenges, Hector developed GlycoGenius, an open-source platform that automates major steps in glycomics data processing.

The software provides functionality for:

• Automated construction of glycan search spaces.

• Identification and annotation of glycan compositions.

• Scoring, filtering, and quantification of detected features.

• Interpretation and annotation of MS/MS fragmentation spectra.

• Visualization and quality assessment of glycomics datasets.

• Analysis of multiple glycan classes, including N-glycans, O-glycans, and glycosaminoglycans.

Expanding GlycoGenius

His current postdoctoral research focuses on extending the software into a more flexible and broadly applicable computational platform. This includes further improving analytical efficiency, supporting different mass spectrometry technologies and experimental workflows, and addressing computational challenges associated with increasingly complex glycomics datasets. A particular emphasis is placed on making automated data interpretation robust, reproducible, and accessible to researchers with different levels of computational expertise.

Software: GlycoGenius on GitHub

PerMel-AI: Artificial Intelligence and Digital Pathology for Personalized Melanoma Research

Hector is involved in PerMel-AI – Utilizing AI and Large-scale Biobanking for Drug Target and Biomarker Validation in Personalized Melanoma Treatment. This international research consortium aims to improve melanoma patient stratification and identify molecular biomarkers and potential therapeutic targets by integrating large-scale proteomics, artificial intelligence, and digital pathology. The project builds on the Human Melanoma Proteome Atlas and combines molecular information with histopathological and clinical data.

Hector's work contributes computational and data-analysis expertise to this interdisciplinary research environment, including the integration and interpretation of complex molecular datasets alongside AI-assisted digital pathology approaches. An important objective is to explore how molecular profiles and tissue-level information can complement each other in characterizing melanoma heterogeneity and identifying potentially clinically relevant disease subtypes. By combining bioinformatics, computational analysis, and biomedical research, this project seeks to contribute to more precise and personalized approaches to melanoma treatment.

Glycosylation, Metabolism and Cancer

Hector's previous research investigated how metabolic conditions, particularly elevated glucose availability, influence protein glycosylation and cancer cell behaviour. His work contributed to understanding how hyperglycemia can alter glycan biosynthesis and the molecular composition of glycans expressed by cancer cells. By combining experimental glycobiology, mass spectrometry and computational analysis, these investigations highlighted the importance of studying glycosylation as a molecular connection between cellular metabolism and disease progression. This background continues to inform his computational research and the development of biologically meaningful analytical tools.