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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Updated: May 3, 2026

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GRAMEP: an alignment-free method based on the maximum entropy principle for identifying SNPs.

Matheus Henrique Pimenta-Zanon1, André Yoshiaki Kashiwabara1, André Luís Laforga Vanzela2

  • 1Computer Science Department, Universidade Tecnológica Federal do Paraná (UTFPR), Alberto Carazzai, 1640, Cornélio Procópio, Paraná, 86300-000, Brazil.

BMC Bioinformatics
|February 25, 2025
PubMed
Summary
This summary is machine-generated.

GRAMEP is a novel alignment-free method for identifying genomic mutations and classifying DNA sequences. This maximum entropy-based approach accurately detects single nucleotide polymorphisms (SNPs) with reduced computational cost.

Keywords:
Alignment-free methodsClassification of biological sequencesGenomic data analysisPrinciple of maximum entropySNP mutation identification

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput sequencing generates vast genomic data requiring efficient analysis.
  • Investigating genomic mutations is crucial for understanding evolution, genetic disorders, and diseases.
  • Traditional sequence alignment methods for variation analysis are computationally intensive and restrictive for large datasets.

Purpose of the Study:

  • To present GRAMEP, a novel alignment-free method for identifying single nucleotide polymorphisms (SNPs) in assembled genomes.
  • To leverage the principle of maximum entropy for discovering informative k-mers specific to genomes.
  • To enable accurate variant detection and classification of novel sequences without organism-specific data.

Main Methods:

  • Developed GRAMEP, an alignment-free computational approach.
  • Utilized the principle of maximum entropy to identify informative k-mers.
  • Applied the method to in silico simulations and viral genome analyses (Dengue, HIV, SARS-CoV-2).

Main Results:

  • GRAMEP accurately identifies single nucleotide polymorphisms (SNPs) and classifies genomic sequences.
  • Demonstrated high accuracy in in silico simulations and analyses of viral genomes.
  • Achieved accurate SARS-CoV-2 variant identification with lower computational cost compared to existing methods.

Conclusions:

  • GRAMEP is an open-source, user-friendly software providing an efficient alignment-free method for genomic analysis.
  • The software accurately identifies and classifies unique genomic subsequences and SNPs.
  • GRAMEP offers significant advantages in efficiency and accuracy over comparative methods.