Speaker: Elisa Bonomi
3 Novembre 2026 | 15:00
DEIB, Sala Riunioni PT2 (Ed. 20A)
Per maggiori informazioni: Silvia Cascianelli | silvia.cascianelli@polimi.it
Sommario
Tuesday, November 3rd, 2026 at 3:00 pm a new appointment of Data Science Seminars: Bioinformatics focus will take place in DEIB PT2 Meeting Room (Building 20A) organized by the Data Science for Bioinformatics group.The seminar will be held by Elisa Bonomi, PhD candidate in Data Analytics and Decision Sciences, on the following subject: "CHIM-CAT: A Bioinformatic Framework for Reconstructing and Characterizing Viral–Host Chimeric Transcripts".
Integration of viral or therapeutic vector DNA into the host genome can alter gene expression and generate chimeric transcripts containing both host and vector-derived sequences. However, these complex transcriptional events are often difficult to detect and characterize using conventional RNA-seq analysis pipelines, which are primarily designed to identify transcripts originating from the host genome.
To address this challenge, we developed Chim-seq, a targeted RNA sequencing approach designed to enrich for vector–host chimeric transcripts, and CHIM-CAT, a dedicated bioinformatic framework for their reconstruction and characterization. The pipeline combines alignment to a hybrid host–vector reference genome with custom algorithms that interpret CIGAR strings and soft-clipped alignments to reconstruct transcript architectures. By integrating genomic annotations, exon structures, and splice-site analysis, CHIM-CAT enables the identification and classification of complex chimeric transcriptional events, including canonical and cryptic splicing. We applied this framework to a liver-directed gene editing model for Wilson disease, where it revealed diverse transcriptional outcomes associated with AAV vector integration, including chimeric transcripts originating from both targeted and unintended integration events. We further extended its application to a gene therapy-treated patient with metachromatic leukodystrophy (MLD), investigating vector–host transcriptional interactions in a distinct clinical setting. Together, these applications illustrate how dedicated sequencing strategies and computational methods can reveal transcriptional consequences of vector integration that are difficult to resolve through conventional approaches, providing a framework for investigating vector–host interactions in gene therapy.