Abstract
The exponential growth of genome sequences available has spurred research on pattern detection with the aim of extracting evolutionary signal. Traditional approaches, such as multiple sequence alignment, rely on positional homology in order to reconstruct the phylogenetic history of taxa. Yet, mining information from the plethora of biological data and delineating species on a genetic basis, still proves to be an extremely difficult problem to consider. Multiple algorithms and techniques have been developed in order to approach the problem multidimensionally. Here, we propose a computational framework for identifying potentially meaningful features based on k-mers retrieved from unaligned sequence data. Specifically, we have developed a process which makes use of unsupervised learning techniques in order to identify characteristic k-mers of the input dataset across a range of different k-values and within a reasonable time frame. We use these k-mers as features for clustering the input sequences and identifying differences between the distributions of k-mers across the dataset. The developed algorithm is part of an innovative and much promising approach both to the problem of grouping sequence data based on their inherent characteristic features, as well as for the study of changes in the distributions of k-mers, as the k-value is fluctuating within a range of values. Our framework is fully developed in Python language as an open source software licensed under the MIT License, and is freely available at https://github.com/BiodataAnalysisGroup/kmerAnalyzer.
Highlights
During the last decade DNA sequencing technology has been revolutionized, as the advent of Generation Sequencing (NGS) (Castro et al, 2020) led to the production of great amounts of biological data
A total of 12,474 sequences of SARS-CoV-2 were retrieved from NCBI2, along with a metadata JSON file containing chronological information, demographic information and information related to the collection source
In order to do that, we firstly investigated lower k-mer sizes and we started with a range of k ∈ [4, 5]
Summary
During the last decade DNA sequencing technology has been revolutionized, as the advent of Generation Sequencing (NGS) (Castro et al, 2020) led to the production of great amounts of biological data. A Computational-Framework for Sequence-Feature Identification in several studies for the comparison and analysis of DNA sequences (Murray et al, 2017; Sievers et al, 2017) Alignmentbased methods, such as the well-known Basic Local Alignment Search Tool (BLAST), consider the exact position and quality of similarity of every part of the sequence within the dataset. Most of the time, prior knowledge of the underlying genome sequences is not a requirement To this end, Murray et al (2017) have proposed a new method for a k-mer-based sequence comparison to estimate genetic relatedness from sequence data. Given the overwhelming quantities of available sequence data, a question that arises is how to identify key features across sequences that they would serve as proxies for significant phenotypic differences, aiding in this way the inference of the underlying evolutionary relationships
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