Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learning

Journal article

We are witnessing a steep increase in model development initiatives in genomics that employ high-end machine learning methodologies. Of particular interest are models that predict certain genomic characteristics based solely on DNA sequence. These models, however, treat the DNA as a mere collection of four, A, T, G and C, letters, dismissing the past advancements in science that can enable the use of more intricate information from nucleic acid sequences. Here, we provide a comprehensive database of quantum mechanical (QM) and geometric features for all the permutations of 7-meric DNA in their representative B, A and Z conformations. The database is generated by employing the applicable high-cost and time-consuming QM methodologies. This can thus make it seamless to associate a wealth of novel molecular features to any DNA sequence, by scanning it with a matching k-meric window and pulling the pre-computed values from our database for further use in modelling. We demonstrate the usefulness of our deposited features through their exclusive use in developing a model for A->C mutation rates.

Authors: Masuda, K; Abdullah, AA; Pflughaupt, P; Sahakyan, AB

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Scientific Data
• Volume: 11
• Issue: 1
• Article number: 911
• Publication date: 2024-08-22
• Acceptance date: 2024-08-13
• DOI: 10.1038/s41597-024-03772-5
• EISSN: 2052-4463
• Language: English