Comparative analysis method for time series data objects represented as sets of strings based on de Bruijn graphs

The paper considers the comparative analysis of string datasets represented as time series of samples. We propose a method to increase the accuracy of determining differences between two samples. Based on this method, a method for analyzing time series of three samples has been developed, which allows for more accurate changes investigation between samples. The use of three samples in the analysis is due to the specific nature of the practical task of processing metagenomic sample sequencing data, obtaining a larger number of which is very resource-intensive. To classify strings from one sample into detected and undetected strings in another sample, a method of comparing two samples using k-mers and the de Bruijn graph is proposed. It implements decision rules based on statistics of the frequency of k-mers occurrence, different values of the parameter k, and information about possible errors in the strings. To analyze time series of three samples (the original and final sample for one object and the modifying sample for another object), a method based on pairwise comparison of samples is developed. It is used to divide the strings of each sample into groups depending on the detection of strings in other samples. The developed method for analyzing time series has been tested on two types of generated metagenomic data, represented as a set of strings. It was shown that the method allows distinguishing organisms that have differences in genomes in at least one symbol for every 10,000 symbols. High (more than 80 %) recall and precision of the results of string classification were demonstrated when analyzing simulated complex data, with properties comparable to real data. The developed method allows comparing metagenomic samples represented as a set of strings using only the data itself and without requiring additional information. This allows for a more accurate analysis compared to existing methods that compare samples based on the results of string classification in taxonomic annotation databases. The developed methods can also be used in other areas of string data processing such as analyzing changes in author style when writing a series of texts.