Marco Trevisan-Herraz, PhD

Computational biology

I have a physicist mindset, and I like to think in terms of models and first principles from which the rest emerges. This is how I have created computational models for different omics (spanning from genomics to proteomics), and their associated software to allow non-bioinformatics specialised researchers to use them. Some of them have a community of researchers around them that, after twelve years, keep being used, developed and cited. For example, in the period 2014-2018, while I worked in my PhD at CNIC (Madrid), the three publications related to SanXoT, the Systems Biology Triangle model, and the statistical model behind (see the Publications section), have been cited 52 times only in 2025, and have been complemented with new tools after I left, such as iSanXoT and PTM-compass. Today, I apply AI and machine learning to create new models that bring up what is important in messy datasets. Read more Read more

Systems biology

I'm especially interested in making interpretable the incredibly intricate patterns of biology. For example, I created the Systems Biology Triangle, which is a systems biology model created especially for proteomics data structures. The model introduces the concept of protein coordination. In proteomics, information flows from spectra to proteins, protein complexes and functional pathways, following workflows that are very different from genomics or transcriptomics (in which the basis are sequences rather than spectra). When I started to work in proteomics, many researchers recycled models built for genomics and transcriptomics (such as GSEA and over-representation algorithms), which is not wrong, but don't use all the potential that proteomics datasets offer. Building on the WSPP model, I created an entirely new systems biology model that puts proteomics data structures at the centre. After ten years, my 2016 paper has been cited eleven times only in 2026, helping research ranging from embryonic brain angiogenesis to heart failure induced by cancer therapy. Read more Read more

Omics technologies

I'm fascinated by the complexity of life. Since childhood, I have always been interested in the big picture of how life is organised. That's why, after completing my PhD in bioinformatics applied to proteomics, I decided to get out of the comfort zone and move to Newcastle University to work in other areas of computational biology, to broaden my understanding. Today, my models and research are being applied to genomics, epigenomics (such as ChIP-seq and histone marks), and transcriptomics (including both bulk RNA-seq and single cell RNA-seq). On the way, I had the joy of learning and working in the immune system, mitochondrial research, differences between men and women, evolution, kidney disease, ageing, and cardiovascular disease. Read more Read more

Methodological leadership

I co-developed the Systems Biology Triangle (SBT), an established proteomics-native framework for analysing coordinated protein responses in pairwise quantitative experiments. Combined WSPP–SBT workflows retain the hierarchy linking spectra, peptides, proteins and functional categories, rather than beginning with a flattened protein-level table. This data-aware approach also guides the current transcriptomics work described under Projects.

Public indicator: SBT remains an operational model in the current iSanXoT platform, where it is provided as a dedicated module and embedded in reusable compound WSPP–SBT workflows.

Open software and a growing ecosystem

SanXoT has developed beyond the original package into a wider ecosystem of users, developers and connected tools. After my direct involvement ended, researchers at CNIC carried the framework forward through iSanXoT and added resources for database construction, reporting and specialised proteomics workflows. The public code and release history trace how that analytical environment has continued to evolve.

Public record: The CNIC-Proteomics software portfolio contains the iSanXoT successor, a continuing public release history and connected resources such as bioDataHub, alongside a wider family of PTM and workflow tools.

Teaching and knowledge exchange

Teaching and knowledge exchange are part of my research contribution. I have developed and delivered practical bioinformatics training across universities and European programmes, connecting computational methods with biological interpretation. The emphasis is on helping learners move from omics data to reproducible analysis and on producing materials that remain useful beyond a single teaching session.

Public indicator: Institutional course pages and a public teaching repository document these contributions; the Teaching section brings the roles, evidence and materials together.

Computational epigenomics and machine learning approaches to understand chromatin structure, regulatory architecture and disease-associated genome function. A first model of Chromatinsight is on GitHub if you want to have a look. It was originally developed to detect differential epigenomic patterns between men and women, but can be used between any two groups of samples (provided that there are enough samples to have some statistical power). It was developed during my postdoc with Daniel Rico, while we both were at Newcastle University.

Why this matters: It aims to find hidden differential pattens between epigenomic experiments, helping with interpretation.

Exploring the relationship between mitochondrial DNA variants (both healthy or pathogenic), and the epigenomic patterns in the nucleus, using machine learning models (such as Chromatinsight) and statistical physics to characterise the impact of different types of mitochondria in different diseases. Preliminary data for healthy mtDNA variants for this project were analysed during my time at the former Wellcome Trust Centre for Mitochondrial Research, with related researchers and activities now under Newcastle University Mitochondrial Research Group.

Why this matters: I aim to explain how mitochondrial variation influences nuclear regulation and contributes to health or disease.

At Prof Richardson's Cardiovascular Regeneration and Ageing Lab, I'm currently developing what we call the Onsager prominence model, a statistical model (and its associated software) for experiments that include three groups of samples: control, condition, and condition + treatment. In these experiments, understanding how effective a treatment is, or the side effects, can be difficult if only pairwise comparisons are performed: a treatment can rescue some pathways, while making others worse. The Onsager prominence model aims to make it easier to interpret the effect of treatment on each gene set. The model is still in alpha stage, but the viewer and the demo dataset are already available.

Why this matters: It helps reveal which biological processes a treatment restores, and which get worse due to it.

ELTE · Analysis of Omics Data PR

On 14jul-2026, I delivered three lectures for ELTE's practice-oriented Analysis of Omics Data PR summer course, covering ChIP-seq analysis and single-cell RNA-seq. The course trained students to move from high-throughput omics data to reproducible analysis, visualisation and biological interpretation, and I greatly enjoyed their participation and engagement during the sessions.

CHARM-EU · transnational teaching

I previously contributed 12 hours of lectures to the CHARM-EU transnational version of Analysis of Omics Data PR, delivered by ELTE. The course covers high-throughput data handling, processing and visualisation across genomics, ChIP-seq, transcriptomics, functional enrichment, metagenomics and proteomics.

Open course materials

The practical materials are available through the ELTEbioinformatics GitHub repository, supporting reproducible learning with modules on Linux/Bash, next-generation sequencing, genomics, proteomics, transcriptomics, ChIP-seq, functional enrichment and single-cell transcriptomics.