Methods for Bioinformatic Prediction of Genuine sRNAs from Outer Membrane Vesicles.

Ali Ali, Mohamed Salem

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2024. 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

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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

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.

Ali AliDepartment of Animal and Avian Sciences, University of Maryland, College Park, MD, USA.

Mohamed SalemDepartment of Animal and Avian Sciences, University of Maryland, College Park, MD, USA. mosalem@umd.edu.

Funding

United States Department of Agriculture, National Institute of Food and Agriculture 2020-67015-30770

6 · The paper itself

Abstract

The molecular pathogenesis of Gram-negative bacteria remains a complex and incompletely understood phenomenon. Various factors are believed to contribute to the pathogenicity of these bacteria. One key mechanism utilized by Gram-negative bacteria is the production of outer membrane vesicles (OMVs), which are small spherical particles derived from the bacterial outer membrane. These OMVs are crucial in delivering virulence factors to the host, facilitating host-pathogen interactions. Within these OMVs, small regulatory RNAs (sRNAs) have been identified as important players in modulating the host immune response. One of the main challenges in studying OMVs and their cargo of sRNAs is the difficulty in isolating and purifying sufficient quantities of OMVs, as well as accurately predicting genuine sRNAs computationally. In this chapter, we present protocols aimed at overcoming these obstacles.

Indexed as

Bacterial Outer MembraneComputational BiologyRNA, Small UntranslatedGram-Negative BacteriaRNA, BacterialRNA, BacterialRNA, Small UntranslatedBioinformatic predictionComparative genomics-based predictionIsolationMachine learningOuter membrane vesicles (OMVs)PurificationSmall regulatory RNAs (sRNA)Small RNA-SeqWhole bacterial genome

Identifiers

PMID39141293

DOI10.1007/978-3-031-20837-9_6

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.