Salomé Catalina Vilchez Larrea

This paper develops a neural network-based pipeline to automate the quantification of intracellular Trypanosoma cruzi infection using fluorescently stained images, showing high accuracy and robustness compared to manual methods.

The paper maps H3K4me3 in T. cruzi and finds it correlates with transcriptional activity and genomic span of initiation regions.

The paper adapts and improves a nucleosome mapping method for Trypanosoma cruzi, emphasizing the importance of using a comprehensive genome reference and evaluating aligners for complex genomes.