Evidence map›Paper›PMID 34789306›Full record

ArticleGenome biology2021

Promotech: a general tool for bacterial promoter recognition.

Ruben Chevez-Guardado, Lourdes Peña-Castillo

Abstract read
In one paragraph

Article in Genome biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

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  14. ProPr54 web server: predicting σNAR genomics and bioinformatics · 2025
    Article
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  17. Microbiology spectrum · 2025
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  18. Identification of Small RNAs inInternational journal of molecular sciences · 2024
    Article
  19. Predicting Promoters in Multiple Prokaryotes with Prompt.Interdisciplinary sciences, computational life sciences · 2024
    Article
  20. 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

2 authors.

Ruben Chevez-GuardadoDepartment of Computer Science, Memorial University of Newfoundland, 230 Elizabeth Ave, St. John's, Newfoundland, A1C 5S7, Canada.
Lourdes Peña-CastilloDepartment of Computer Science, Memorial University of Newfoundland, 230 Elizabeth Ave, St. John's, Newfoundland, A1C 5S7, Canada. lourdes@mun.ca.ORCID 0000-0002-0643-2547

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Promoters are genomic regions where the transcription machinery binds to initiate the transcription of specific genes. Computational tools for identifying bacterial promoters have been around for decades. However, most of these tools were designed to recognize promoters in one or few bacterial species. Here, we present Promotech, a machine-learning-based method for promoter recognition in a wide range of bacterial species. We compare Promotech's performance with the performance of five other promoter prediction methods. Promotech outperforms these other programs in terms of area under the precision-recall curve (AUPRC) or precision at the same level of recall. Promotech is available at https://github.com/BioinformaticsLabAtMUN/PromoTech .

Indexed as

Promoter Regions, GeneticBacteriaComputational BiologyGenomicsMachine LearningSoftwareBacterial promoterBioinformaticsMachine learningMicrobiologyPromoter predictionPromoter recognition

Identifiers

PMID34789306
PMCPMC8597233

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.