Evidence map›Paper›PMID 40828281›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2025

High-Throughput Amplicon Sequencing for Analyzing Microbial Communities of Insects.

Juan Sebastian Enciso Garcia, Marta Chignola, Lapo Ragionieri, Filippo Rey, Maja Fluch, Luigimaria Borruso, Erika Corretto, Hannes Schuler

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2025. 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

8 authors.

Juan Sebastian Enciso GarciaFaculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy.
Marta ChignolaFaculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy.
Lapo RagionieriCompetence Centre for Plant Health, Free University of Bozen-Bolzano, Bolzano-Bozen, Italy.
Filippo ReyFaculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy.
Maja FluchFaculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy.
Luigimaria BorrusoFaculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy.
Erika CorrettoCompetence Centre for Plant Health, Free University of Bozen-Bolzano, Bolzano-Bozen, Italy.
Hannes SchulerFaculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy. hannes.schuler@unibz.it.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Insects represent more than 80% of all described species on the planet. This diversity is a result of millions of years of evolution, during which insects have colonized nearly every habitat. Their success is partly due to their ability to form symbiotic relationships with a wide variety of other organisms, especially microorganisms. Identifying and characterizing associated microorganisms are crucial to understanding the complexity and dynamics of these symbiotic relationships. To date, advancements in sequencing technologies that provide large sequence data sets have become ideal tools for characterizing insect microbiomes, including information about non-cultivable microorganisms commonly found in insects. Despite the growing number of studies focused on insect microbiome characterization, there are few protocols detailing methodological procedures for fieldwork, DNA extraction, and data processing. Here, we present an overview of the characterization of insect-associated bacterial communities. We cover best practices for data interpretation and visualization, including alpha and beta diversity analyses, community composition profiling, and statistical testing to identify microbial associations of insects.

Indexed as

BacteriaHigh-Throughput Nucleotide SequencingInsectaMicrobiotaAnimalsDNA, BacterialMetagenomicsRNA, Ribosomal, 16SSequence Analysis, DNASymbiosisDNA, BacterialRNA, Ribosomal, 16S16S rRNA geneBacterial communityBioinformaticsInsectsMetabarcodingMicrobiome

Identifiers

What Socratic holds

Textmetadata
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 Socratic graph.