Evidence map›Paper›PMID 39994548›Full record

ArticleBMC genomics2025

An algorithm for annotation and classification of T. cruzi MASP sequences: towards a better understanding of the parasite genetic variability.

Aldana Alexandra Cepeda Dean, Luisa Berná, Carlos Robello, Carlos Andrés Buscaglia, Virginia Balouz

Abstract read
In one paragraph

Article in BMC genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

4 citing papers in PubMed.

  1. Article
  2. An Algorithm for Annotation of Trypanosoma cruzi MASP Sequences.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  3. Article
  4. Article
4 · The record

Corrections and comments

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

5 authors.

Aldana Alexandra Cepeda DeanInstituto de Investigaciones Biotecnológicas (IIBio), Universidad Nacional de San Martín (UNSAM), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. 25 de Mayo y Francia, Campus UNSAM, B1650HMP San Martín, Buenos Aires, Argentina.
Luisa BernáLaboratorio de Interacciones Hospedero-Patógeno, Unidad de Biología Molecular, Institut Pasteur de Montevideo, Montevideo, Uruguay.
Carlos RobelloLaboratorio de Interacciones Hospedero-Patógeno, Unidad de Biología Molecular, Institut Pasteur de Montevideo, Montevideo, Uruguay.
Carlos Andrés Buscaglia *Instituto de Investigaciones Biotecnológicas (IIBio), Universidad Nacional de San Martín (UNSAM), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. 25 de Mayo y Francia, Campus UNSAM, B1650HMP San Martín, Buenos Aires, Argentina. cbuscaglia@iib.unsam.edu.ar.
Virginia Balouz *Instituto de Investigaciones Biotecnológicas (IIBio), Universidad Nacional de San Martín (UNSAM), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Av. 25 de Mayo y Francia, Campus UNSAM, B1650HMP San Martín, Buenos Aires, Argentina. vbalouz@iib.unsam.edu.ar.

Funding

Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación PICT-2017-3908
6 · The paper itself

Abstract

backgroundTrypanosoma cruzi, the protozoan causing Chagas disease, is responsible for a neglected tropical disease affecting millions in Latin America. Its genome contains rapidly evolving multigene families, such as mucins (TcMUC), trans-sialidases (TS), and mucin-associated surface proteins (MASP), which are essential for parasite transmission and disease mechanisms. However, methodological challenges in genome assembly and annotation have limited the characterization of these gene families, particularly MASPs.

resultsWe developed a bioinformatic pipeline for the automatic identification, characterization, and annotation of MASPs directly from T. cruzi genome assemblies. This algorithm, based on a manually curated MASP database and HMM-based identification of MASP diagnostic motifs, enables the robust classification of these molecules into canonical MASPs, MASP-related molecules (mostly pseudogenes), and chimeric sequences combining MASPs and TcMUC/TS genes. Validation against a rigorously annotated dataset demonstrated high accuracy, and allowed us to reclassify misanotated sequences and, more crucially, to accurately identify previously unrecognized canonical MASPs and MASP chimeras. This algorithm was then used to explore the MASP repertoire in the genomes of 13 parasite strains from different evolutionary lineages, revealing patterns of diversity. For instance, TcI and TcII strains exhibited higher ratios of canonical MASP/MASP-related molecules and a greater abundance of MASP chimeras, suggesting that their genomes are under strong selective pressures towards maintaining a broader panel of full-length MASP genes at the expense of pseudogenes. On the contrary, structural features of canonical MASPs, MASP-related sequences, and MASP-chimeras were largely conserved across parasite genomes.

conclusionsThis novel pipeline automates the annotation of MASPs, a key surface protein family unique to T. cruzi, improving genome annotation and enabling robust comparative analyses. It provides an essential tool for exploring the evolutionary dynamics of multigene families in T. cruzi and could be extended to other gene families.

Indexed as

AlgorithmsGenetic VariationMembrane ProteinsMolecular Sequence AnnotationProtozoan ProteinsTrypanosoma cruziComputational BiologyGenome, ProtozoanGlycoproteinsMucinsMultigene FamilyNeuraminidasePhylogenyGlycoproteinsMembrane ProteinsMucinsNeuraminidaseProtozoan Proteinstrans-sialidaseGenome annotationHidden Markov Models (HMM)Molecular signaturesMucin-associated surface proteins (MASP)Trypanosoma cruzi

Identifiers

PMID39994548
PMCPMC11852901

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LicenceCC BY-NC-ND
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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.