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Related Experiment Video

Updated: Jun 26, 2026

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA

Collection and Extraction of Occupational Air Samples for Analysis of Fungal DNA

Published on: May 2, 2018

Identification of Rust Fungi Using High-Throughput Sequencing Data from Environmental Samples.

Samuel Holden^1,2, Sang Hu Kim³, Wen Chen⁴

¹Faculty of Land and Food Systems, The University of British Columbia, Vancouver, BC, Canada. Samuel.holden@ubc.ca.

Methods in Molecular Biology (Clifton, N.J.)

|April 8, 2025

Summary

This summary is machine-generated.

This study presents in silico methods for identifying and classifying microbes in sequencing data from environmental samples, crucial for accurate plant-associated microbe research. These quality control steps ensure data integrity for downstream analyses, especially with complex field samples like rust fungi.

Keywords:

Bioinformatics Data processing Genomics Metagenomics NGS Pucciniales RNAseq Sequencing Taxonomy

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Area of Science:

Microbiology
Bioinformatics
Plant Pathology

Background:

Environmental samples harbor diverse microbial communities, posing challenges for targeted research.
Maintaining axenic cultures of plant pathogens like rust fungi requires stringent laboratory protocols.
Next-generation sequencing (NGS) generates large datasets often containing non-target genetic material.

Purpose of the Study:

To detail in silico approaches for identifying and classifying organisms within sequencing data.
To provide essential quality control (QC) steps for analyzing environmental sequencing data.
To address challenges in analyzing plant-associated microbial communities, particularly rust fungi.

Main Methods:

In silico analysis of sequencing data.
Quality control and filtering of next-generation sequencing reads.
Bioinformatic classification of microbial genetic material.

Main Results:

Developed and applied computational methods for sample identification and classification.
Demonstrated the importance of filtering non-target reads in environmental sequencing data.
Highlighted potential pitfalls of assuming target organisms only in sequencing datasets.

Conclusions:

In silico methods are vital for accurate microbial community analysis from environmental samples.
Implementing QC steps ensures the reliability of sequencing data for plant-associated microbe studies.
These approaches are broadly applicable but may require species-specific adjustments.