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Machine Learning in Base-Calling for Next-Generation Sequencing Methods

The development of next-generation sequencing (NGS) technologies has made a significant contribution to the trend of reducing costs and obtaining massive sequencing data. The Institute for Analytical Instrumentation of the Russian Academy of Sciences is developing a hardware-software complex for deciphering nucleic acid sequences by the method of mass parallel sequencing (Nanofor SPS). Image processing algorithms play an essential role in solving the problems of genome deciphering. The final part of this preliminary analysis of raw data is the base-calling process. Base-calling is the process of determining a nucleotide base that generates the corresponding intensity value in the fluorescence channels for different wavelengths in the flow cell image frames for different synthesis sequencing runs. An extensive analysis of various base-calling approaches and a summary of the common procedures available for the Illumina platform are provided. Various chemical processes included in the synthesis sequencing technology, which cause shifts in the values of recorded intensities, are considered, including the effects of phasing / prephasing, signal decay, and crosstalk. A generalized model is defined, within which possible implementations are considered. Possible machine learning (ML) approaches for creating and evaluating models that implement the base-calling processing stage are considered. ML approaches take many forms, including unsupervised learning, semi-supervised learning, and supervised learning. The paper shows the possibility of using various machine learning algorithms based on the Scikit-learn platform. A separate important task is the optimal selection of features identified in the detected clusters on a flow cell for machine learning. Finally, a number of sequencing data for the MiSeq Illumina and Nanofor SPS devices show the promise of the machine learning method for solving the base-calling problem.