Fabio Gómez-Cano

The paper introduces methods to estimate and remove ambient contamination in scifi-ATAC data, improving the accuracy of chromatin profiles and biological discovery.

The paper studies dedifferentiation in Arabidopsis thaliana using single-cell RNA sequencing, revealing cell-type-specific transcriptional changes and the role of phytohormones and chromatin remodeling.

This study identifies cell-type-specific regulatory variation in maize hybrids, showing how chromatin accessibility changes contribute to heterosis by reducing cell-type specificity.

The paper corrects distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions using multi-component power-law penalties informed by Gaussian mixture modeling and polymer-physics scaling.

The paper identifies cell-type-specific cis-regulatory variants in maize using single-cell genomics and population genetics, revealing their role in local adaptation and flowering.

This paper develops a framework to prioritize maize metabolic gene regulators by integrating multi-omic data, identifying over 3,000 TFs and predicting new regulators of hormone, developmental, and metabolic processes.

The paper shows that TF-TF interactions improve co-expression signals for predicting regulatory complexes in Arabidopsis, highlighting the role of combinatorial gene regulation.