γ-TRIS: A graph-algorithm for comprehensive identification of vector genomic insertion sites

Andrea Calabria, Stefano Beretta, Ivan Merelli, Giulio Spinozzi, Stefano Brasca, Yuri Pirola, Fabrizio Benedicenti, Erika Tenderini, Paola Bonizzoni, Luciano Milanesi, Eugenio Montini

Research output: Contribution to journalArticlepeer-review

Abstract

Summary: Retroviruses and their vector derivatives integrate semi-randomly in the genome of host cells and are inherited by their progeny as stable genetic marks. The retrieval and mapping of the sequences flanking the virus-host DNA junctions allows the identification of insertion sites in gene therapy or virally infected patients, essential for monitoring the evolution of genetically modified cells in vivo. However, since ∼30% of insertions land in low complexity or repetitive regions of the host cell genome, they cannot be correctly assigned and are currently discarded, limiting the accuracy and predictive power of clonal tracking studies. Here, we present γ-TRIS, a new graph-based genome-free alignment tool for identifying insertion sites even if embedded in low complexity regions. By using γ-TRIS to reanalyze clinical studies, we observed improvements in clonal quantification and tracking.

Original languageEnglish
Pages (from-to)1622-1624
Number of pages3
JournalBioinformatics
Volume36
Issue number5
DOIs
Publication statusPublished - Mar 1 2020

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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