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DOE OSTI · 2588293

Characterizing Integrase-Attachment Site Pairs: A Machine Learning Approach

Patel, Ved Bhavesh [Sandia National Laboratories (SNL-CA), Livermore, CA (United States)] (ORCID:0009000107632604)·Torrance, Ellis Lynn [Sandia National Laboratories (SNL-CA), Livermore, CA (United States)] (ORCID:0009000994407875)
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Abstract

Genomic islands (GIs) are mobile genetic elements that integrate into host genomes via self-encoded integrases at specific DNA sequences known as attachment (att) sites. The ability to predict the target att site from an integrase's protein sequence is a central challenge in genomics due to the sequence diversity of integrases and the subtlety of their DNA recognition motifs.

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Patel, Ved Bhavesh [Sandia National Laboratories (SNL-CA), Livermore, CA (United States)] (ORCID:0009000107632604), Torrance, Ellis Lynn [Sandia National Laboratories (SNL-CA), Livermore, CA (United States)] (ORCID:0009000994407875). 2025-09-01. Characterizing Integrase-Attachment Site Pairs: A Machine Learning Approach. https://doi.org/10.2172/2588293

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