In search of true reads: A classification approach to next generation sequencing data selection

Next generation sequencing (NGS) technology has increasingly become the backbone of transcriptomics analysis, but sequencer error causes biases in the read counts. In this paper we establish a framework for predicting true sequences from NGS data. We formulate this task as a classification problem. We define several features, such as log likelihood ratio of estimated true counts, error probability and observed count of the reads. Using a Support Vector Machine (SVM) classifier, we show that on simulated reads these features can achieve 96.35% classification accuracy in discriminating true sequences. Using this framework we provide a way for users to select sequences with a desired precision and recall for their analysis. The feature generation software and the simulated data set can be obtained from (http://seq.cbrc.jp/NGSFeatGen).