Classification of nucleotide sequences for quality assessment using logistic regression and decision tree approaches

Knowledge of DNA sequences is indispensable for basic biological research. Many researchers use DNA sequencing for various purposes including molecular biology research and sequence comparison for individual identification. Automated DNA sequencing devices use four colored chromatograms or base-calling signals to indicate strength of hybridization for each base channel. Typically, relative strengths of peaks at each base location are used to quantify the quality and/or reliability of individual readings. However, assessment of overall quality of whole DNA trace files remains to be an open problem. Therefore, classification of raw DNA trace files as high or low quality is an important issue for efficient utilization of resources. In this study, we have used several supervised machine learning approaches, including logistic regression and ensemble decision trees, to identify high- or acceptable-quality chromatogram files and compared their prediction performances. In order to test and develop our ideas, we have used a public DNA trace repository consisting of 1626 high- and 631 low-quality files marked by our expert molecular biologist. Our results indicate that, although all of the methods tried offer comparable and acceptable performances, random forest decision tree algorithm with adapting boosting ensemble learning shows slightly higher prediction accuracy with as few as four features.