Machine learning applications to DNA subsequence and restriction site analysis

• URL: http://arxiv.org/abs/2011.03544v5
• Date: Fri, 11 Dec 2020 16:03:26 GMT
• Title: Machine learning applications to DNA subsequence and restriction site analysis
• Authors: Ethan J. Moyer (1) and Anup Das (PhD) (2) ((1) School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, Pennsylvania, USA, (2) College of Engineering, Drexel University, Philadelphia, Pennsylvania, USA)
• Abstract summary: restriction synthesis is a novel iterative DNA synthesis method that utilizes endonucleases to synthesize a query sequence from a reference sequence. In this work, the reference sequence is built from shorter subsequences by classifying them as applicable or inapplicable for the synthesis method using three different machine learning methods. The sensitivity using SVMs, random forest, and CNNs are 94.9%, 92.7%, 91.4%, respectively.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Based on the BioBricks standard, restriction synthesis is a novel catabolic iterative DNA synthesis method that utilizes endonucleases to synthesize a query sequence from a reference sequence. In this work, the reference sequence is built from shorter subsequences by classifying them as applicable or inapplicable for the synthesis method using three different machine learning methods: Support Vector Machines (SVMs), random forest, and Convolution Neural Networks (CNNs). Before applying these methods to the data, a series of feature selection, curation, and reduction steps are applied to create an accurate and representative feature space. Following these preprocessing steps, three different pipelines are proposed to classify subsequences based on their nucleotide sequence and other relevant features corresponding to the restriction sites of over 200 endonucleases. The sensitivity using SVMs, random forest, and CNNs are 94.9%, 92.7%, 91.4%, respectively. Moreover, each method scores lower in specificity with SVMs, random forest, and CNNs resulting in 77.4%, 85.7%, and 82.4%, respectively. In addition to analyzing these results, the misclassifications in SVMs and CNNs are investigated. Across these two models, different features with a derived nucleotide specificity visually contribute more to classification compared to other features. This observation is an important factor when considering new nucleotide sensitivity features for future studies.

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