Evidence map›Paper›PMID 41241906›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Bioinformatic Tools for Non-bioinformaticians: A Roadmap to Study Unknown Microprotein Sequences.

Patricia Gimeno Le Paih, Iker Valle Aramburu

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Patricia Gimeno Le PaihDepartment of Molecular Biology, Umeå University, Umeå, Sweden.
Iker Valle AramburuDepartment of Molecular Biology, Umeå University, Umeå, Sweden. Iker.aramburu@umu.se.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The identification of unknown microprotein sequences is often presented as a FASTA file with hundreds or thousands of unknown RNA or protein sequences. Then, the researcher needs to gain an overall view of all the sequences and identify which ones may be relevant to address their biological question of interest. In the absence of a bioinformatician or certain computational knowledge, this task becomes increasingly challenging proportionally to the number of sequences identified. In this chapter, we provide a guideline of different publicly available online resources that can be used without any computational background to analyze your sequences' structural and physicochemical properties and identify functional domains and motifs. Moreover, we have developed MicrOracle, a publicly available Shiny app that will integrate the different output files from the online prediction tools and generate interactive plots that can contribute to easily visualizing, understanding, and segmenting your data based on structural and functional criteria.

Indexed as

Computational BiologyProteinsSequence Analysis, ProteinSoftwareDatabases, ProteinProteinsDisordered proteinsDomainMicroproteinsMotifProtein bioinformaticsShiny app

Identifiers

What OpenQuestion holds

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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 OpenQuestion graph.