  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.
