Evidence map›Paper›PMID 42681285›Full record

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

Transcriptome Analysis of Arbuscular Mycorrhizal Fungi Using Short-Read Sequencing.

Matteo Chialva, Stefano Ghignone

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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.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Matteo ChialvaDepartment of Life Sciences and Systems Biology (DBIOS), University of Turin, Turin, Italy. matteo.chialva@unito.it.ORCID https://orcid.org/0000-0002-6996-6642
Stefano GhignoneInstitute of Sustainable Plant Protection (CNR-IPSP) Turin Unit, National Research Council, Turin, Italy.ORCID https://orcid.org/0000-0002-2033-2286

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Arbuscular mycorrhizal (AM) fungi are obligate biotrophs whose molecular biology has long been difficult to investigate due to limited genomic resources and the occurrence, in experimental samples, of a high quantity of host plant material. High-throughput short-read RNA sequencing has become a key approach for characterizing AM fungal transcriptomes, but requires tailored bioinformatic strategies to overcome challenges such as mixed-species datasets, low fungal RNA abundance, and the absence of high-quality reference AM fungal genomes. This chapter presents a robust pipeline for AM fungal transcriptome analysis based on short-read RNA data, combining de novo assembly, functional annotation, and differential expression analysis. The workflow is applicable both to host-free systems, where fungal reads can be directly assembled, and to symbiotic conditions, where preprocessing steps are necessary to remove plant-derived reads and enrich for fungal transcripts. Methods to check read quality, remove contaminants, including the host, assemble and annotate transcriptome using homology- and domain-based tools are described. Finally, procedures for robust statistical analysis of differential gene expression are outlined, enabling the identification of molecular pathways involved in fungal development, nutrient exchange, and symbiotic function. Together, these methods provide a comprehensive framework for generating reliable and meaningful insights into the transcriptomes of AM fungi.

Indexed as

Gene Expression ProfilingHigh-Throughput Nucleotide SequencingMycorrhizaeTranscriptomeComputational BiologyGene Expression Regulation, FungalMolecular Sequence AnnotationSequence Analysis, RNASymbiosisBioinformaticsDe novo transcriptomeDifferential gene expressionGene expression profilingRNA-seq

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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.