Marsico Lab - Computational RNA Biology

Unraveling the secrets of RNA and complex biological networks with the power of machine learning - for a healthier future

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Unraveling the secrets of RNA and complex biological networks with the power of machine learning - for a healthier future

Follow us on X!

Go to our Github!

In our lab we investigate machine learning methods for RNA biology, genomics and biomedicine. We are passionate about unravelling new functions of RNA molecules in the cell, and their role in complex gene regulatory networks involving combinatorial interactions between proteins and nucleic acids.

Our machine learning research focuses on deep learning models for biological sequences, interpretable AI and graph-based heterogeneous data modelling. The problems we investigate are motivated by collaborations with wet-lab biologists and clinicians and have the ultimate goal to better understand complex diseases and explore the diagnostic and therapeutic potential of in silico identified biomarkers.

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Dr. Annalisa Marsico

Research Group Head

Lambert Moyon

Postdoc

Ettore Gran

Research Intern

Rodrigo González Laiz

PhD candidate

Svitlana Oleshko

PhD candidate

Stephen Jiang

Undergraduate Researcher

Tobias Schmidt

PhD candidate

Lilly May

Undergraduate Researcher

Paul Pommer

Research Engineer

Hyesu Lim

Visiting PhD Candidate

2023-02-22_MarsicoLab-website_SC2-project-illustration

A computational map of the Human-SARS-CoV-2 Protein-RNA Interactome predicted at single-nucleotide resolution

We adapted our deep learning Pysster classifier to predict binding sites of hundreds of human RBPs on the viral RNA of SARS-CoV-2, 7 related coronaviruses and different variants of concerns. “In silico approaches are very powerful and deeply needed to quickly screen the thousands of viral mutations that potentially drive the fitness of the virus, in order to be prepared for the next pandemics” points out Marc Horlacher, the PhD student leading the project.

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2023-02-24_MarsicoLab-website_graph-project-illustration_2

Network Embedding Across Multiple Tissues and Data Modalities Elucidates the Context of Host Factors Important for COVID-19 Infection

COVID-19 is a heterogeneous disease caused by SARS-CoV-2. Aside from infections of the lungs, the highly variable symptom severity is influenced by genetic predispositions and pre existing diseases. We developed a holistic framework, based on graph inference and graph embedding, to understand molecular pathways affected by SARS-CoV-2 in the context of pre-pandemic data, such as gene expression data, polygenetic predispositions and disease phenotypes across over 900 patients and 50 tissues.

“Node embeddings capture the topological structure of the highly complex biological graph. They allow for efficient investigation of the relationship between the different data and allow us to find important associations between COVID-19 genes and diseases such as ischemic heart disease, cerebrovascular disease, and hypertension. ” says Emy Yue Hu, the PhD student who led the project.

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Horlacher M., Wagner N., Moyon L., Kuret K., Goedert N., Salvatore M., Ule J., Gagneur J., Winther O., Marsico A.

Towards In-Silico CLIP-seq: Predicting Protein-RNA Interaction via Sequence-to-Signal Learning

Ito-Kureha T., Leoni C., Borland K., Bataclan M., Metzger R., Ammann G., Krug A., Marsico A., Kellner S., Canzar S., Feske S., Monticelli S., König J., Heissmeyer V.

N6-adenosine methylation of mRNAs requires Wtap expression and controls T cell receptor signaling and survival

Hu Y., Rehawi G., Moyon L., Gerstner N., Ogris C., Knauer-Arloth J., Bittner F., Marsico A.*, Mueller N. S.*, * equal contributions

Network Embedding Across Multiple Tissues and Data Modalities Elucidates the Context of Host Factors Important for COVID-19 Infection

Schulte-Sasse R., Budach S., Hnisz D., Marsico A.

Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms

Horlacher M., Oleshko S., Hu Y., Ghanbari M., Elorduy Vergara E., Mueller N., Ohler U., Moyon L., Marsico A.

Computational Mapping of the Human-SARS-CoV-2 Protein-RNA Interactome

Krakau S, Richard H*, Marsico A*. * co-corresponding author

PureCLIP: capturing target-specific protein-RNA interaction footprints from single-nucleotide CLIP-seq data