Evidence map›Paper›PMID 41034705›Full record

ArticleBMC bioinformatics2025

HolomiRA: a reproducible pipeline for miRNA binding site prediction in microbial genomes.

Jennifer Jessica Bruscadin, Tainã Figueiredo Cardoso, Liliane Costa Conteville, Juliana Virginio da Silva, Adriana Mércia Guaratini Ibelli, Gabriel Alexander Colmenarez Pena, Thanny Porto, Priscila Silva Neubern de Oliveira, Bruno Gabriel Nascimento Andrade, Adhemar Zerlotini and 1 more

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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

2 citing papers in PubMed.

  1. Review
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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

11 authors.

Jennifer Jessica Bruscadin *Center of Biological and Health Sciences, Federal University of São Carlos, São Carlos, São Paulo, Brazil.
Tainã Figueiredo Cardoso *Embrapa Southeastern Livestock, São Carlos, São Paulo, Brazil. taina.cardoso@colaborador.embrapa.br.
Liliane Costa ContevilleEmbrapa Southeastern Livestock, São Carlos, São Paulo, Brazil.
Juliana Virginio da SilvaCenter of Biological and Health Sciences, Federal University of São Carlos, São Carlos, São Paulo, Brazil.
Adriana Mércia Guaratini IbelliEmbrapa Southeastern Livestock, São Carlos, São Paulo, Brazil.
Gabriel Alexander Colmenarez PenaEmbrapa Southeastern Livestock, São Carlos, São Paulo, Brazil.
Thanny PortoCenter of Biological and Health Sciences, Federal University of São Carlos, São Carlos, São Paulo, Brazil.
Priscila Silva Neubern de OliveiraCenter of Biological and Health Sciences, Federal University of São Carlos, São Carlos, São Paulo, Brazil.
Bruno Gabriel Nascimento AndradeMunster Technological University, Cork, Ireland.
Adhemar ZerlotiniEmbrapa Digital Agriculture, Campinas, São Paulo, Brazil.
Luciana Correia de Almeida RegitanoEmbrapa Southeastern Livestock, São Carlos, São Paulo, Brazil. luciana.regitano@embrapa.br.

Funding

Conselho Nacional de Desenvolvimento Científico e Tecnológico 456191/2014-3Fundação de Amparo à Pesquisa do Estado de São Paulo 2019/04089-2Fundação de Amparo à Pesquisa do Estado de São Paulo 2022/06281-0
6 · The paper itself

Abstract

backgroundSmall RNAs, such as microRNAs (miRNAs), are candidates for mediating communication between the host and its microbiota, regulating bacterial gene expression and influencing microbiome functions and dynamics. Here, we introduce HolomiRA (Holobiome miRNA Affinity Predictor), a computational pipeline developed to predict target sites for host miRNAs in microbiome genomes. HolomiRA operates within a Snakemake workflow, processes microbial genomic sequences in FASTA format using freely available bioinformatics software and incorporates built-in data processing methods. The pipeline begins by annotating protein-coding sequences from microbial genomes using Prokka. It then identifies candidate regions, evaluates them for potential host miRNA binding sites and the accessibility of these target sites using RNAHybrid and RNAup software. The predicted results that meet the quality filter parameters are further summarized and used to perform a functional analysis of the affected genes using SUPER-FOCUS software.

resultsIn this paper, we demonstrate the use of the HolomiRA pipeline by applying it to publicly available metagenome-assembled genomes obtained from human feces, as well as from bovine feces and ruminal content. This approach enables the prediction of bacterial genes and biological pathways within microbiomes that could be influenced by host miRNAs. It also allows for the identification of shared or unique miRNAs, target genes, and taxonomies across phenotypes, environments, or host species.

conclusionsHolomiRA is a practical and user-friendly pipeline designed as a hypothesis-generating tool to support the prediction of host miRNA binding sites in prokaryotic genomes, providing insights into host-microbiota communication mediated by miRNA regulation. HolomiRA is publicly available on GitHub: https://github.com/JBruscadin/HolomiRA .

Indexed as

Computational BiologyGenome, BacterialGenome, MicrobialMicroRNAsSoftwareAnimalsBinding SitesCattleHumansMicroRNAsGene regulationHolobiomeHost–pathogen interaction

Identifiers

PMID41034705
PMCPMC12487068

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.