Gino Prasad

This paper introduces ecEvol, a method to infer ecDNA emergence time and other parameters from bulk whole-genome sequencing data, finding that ecDNA typically emerges early and is associated with poorer survival.

Cycle-Extractor accurately reconstructs ecDNA structures from both short- and long-read sequencing data, outperforming existing methods in speed and completeness.

The paper introduces interSeg, a deep learning tool that accurately predicts oncogene amplification status from interphase FISH images, achieving high accuracy in detecting ecDNA and chromosomal amplifications.

The paper introduces OM2BFB, an algorithm detecting BFB amplifications using OGMs, showing high precision and recall, and finds BFB events are common in certain cancers but not linked to intratumoral heterogeneity or rapid genome change.

AmpliconSuite identifies focal amplifications in cancer genomes, showing ecDNA has higher copy numbers and stronger oncogene enrichment compared to other amplification types.

Bento enables subcellular analysis of spatial transcriptomics data by defining domains, annotating localization patterns, and quantifying gene colocalization, demonstrating its use on multiple datasets.