Identification of SNPs in genomes using GRAMEP, an alignment-free method based on the Principle of Maximum Entropy
<|reference_start|>Identification of SNPs in genomes using GRAMEP, an alignment-free method based on the Principle of Maximum Entropy: Advances in high throughput sequencing technologies provide a large number of genomes to be analyzed, so computational methodologies play a crucial role in analyzing and extracting knowledge from the data generated. Investigating genomic mutations is critical because of their impact on chromosomal evolution, genetic disorders, and diseases. It is common to adopt aligning sequences for analyzing genomic variations, however, this approach can be computationally expensive and potentially arbitrary in scenarios with large datasets. Here, we present a novel method for identifying single nucleotide polymorphisms (SNPs) in DNA sequences from assembled genomes. This method uses the principle of maximum entropy to select the most informative k-mers specific to the variant under investigation. The use of this informative k-mer set enables the detection of variant-specific mutations in comparison to a reference sequence. In addition, our method offers the possibility of classifying novel sequences with no need for organism-specific information. GRAMEP demonstrated high accuracy in both in silico simulations and analyses of real viral genomes, including Dengue, HIV, and SARS-CoV-2. Our approach maintained accurate SARS-CoV-2 variant identification while demonstrating a lower computational cost compared to the gold-standard statistical tools. The source code for this proof-of-concept implementation is freely available at https://github.com/omatheuspimenta/GRAMEP.<|reference_end|>
@article{pimenta-zanon2024identification,
    title={Identification of SNPs in genomes using GRAMEP, an alignment-free method
  based on the Principle of Maximum Entropy},
    author={Matheus Henrique Pimenta-Zanon and Andr'e Yoshiaki Kashiwabara and
  Andr'e Lu'is Laforga Vanzela and Fabricio Martins Lopes},
    journal={arXiv preprint arXiv:2405.01715},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01715},
    primaryClass={q-bio.GN cs.IT math.IT stat.AP}
}