Evidence map›Paper›PMID 40000933›Full record

ArticleBMC bioinformatics2025

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

Matheus Henrique Pimenta-Zanon, André Yoshiaki Kashiwabara, André Luís Laforga Vanzela, Fabricio Martins Lopes

Abstract read
In one paragraph

Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. CAMELLIA: an alignment-free pipeline integratingNAR genomics and bioinformatics · 2026
    Article
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Matheus Henrique Pimenta-ZanonComputer Science Department, Universidade Tecnológica Federal do Paraná (UTFPR), Alberto Carazzai, 1640, Cornélio Procópio, Paraná, 86300-000, Brazil.
André Yoshiaki KashiwabaraComputer Science Department, Universidade Tecnológica Federal do Paraná (UTFPR), Alberto Carazzai, 1640, Cornélio Procópio, Paraná, 86300-000, Brazil.
André Luís Laforga VanzelaLaboratory of Cytogenetics and Plant Diversity, Department of General Biology, Universidade Estadual de Londrina (UEL), Rodovia Celso Garcia Cid, PR-445, Km 380, Londrina, Paraná, 86057-970, Brazil.
Fabricio Martins LopesComputer Science Department, Universidade Tecnológica Federal do Paraná (UTFPR), Alberto Carazzai, 1640, Cornélio Procópio, Paraná, 86300-000, Brazil. fabricio@utfpr.edu.br.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico 440412/2022-6Coordenação de Aperfeiçoamento de Pessoal de Nível Superior 001Fundação Araucária 035/2019NAPI - Bioinformática 0001
6 · The paper itself

Abstract

backgroundAdvances in high throughput sequencing technologies provide a huge number of genomes to be analyzed. Thus, computational methods 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 restrictive in scenarios with large datasets.

resultsWe present a novel method for identifying single nucleotide polymorphisms (SNPs) in DNA sequences from assembled genomes. This study proposes GRAMEP, an alignment-free approach that adopts the principle of maximum entropy to discover the most informative k-mers specific to a genome or set of sequences under investigation. The informative k-mers enable the detection of variant-specific mutations in comparison to a reference genome or other set of sequences. 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 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 methods with the same purpose.

conclusionsGRAMEP is an open and user-friendly software based on maximum entropy that provides an efficient alignment-free approach to identifying and classifying unique genomic subsequences and SNPs with high accuracy, offering advantages over comparative methods. The instructions for use, applicability, and usability of GRAMEP are open access at https://github.com/omatheuspimenta/GRAMEP .

Indexed as

Polymorphism, Single NucleotideSoftwareAlgorithmsComputational BiologyCOVID-19EntropyGenome, ViralHigh-Throughput Nucleotide SequencingHumansSARS-CoV-2Sequence Analysis, DNAAlignment-free methodsClassification of biological sequencesGenomic data analysisPrinciple of maximum entropySNP mutation identification

Identifiers

PMID40000933
PMCPMC11863517

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.