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

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.