Subtopic Deep Dive

Molecular Identification of Yeasts and Rusts
Research Guide

What is Molecular Identification of Yeasts and Rusts?

Molecular Identification of Yeasts and Rusts uses DNA barcodes like ITS and LSU regions with specific primers for species-level identification of yeasts and rust fungi from environmental samples.

Studies develop and validate high-coverage ITS primers for Ascomycetes and Basidiomycetes, including yeasts and rusts (Toju et al., 2012, 1207 citations). Improved ITS-specific primers enable ultra-high-throughput profiling of fungal communities (Bokulich and Mills, 2013, 566 citations). Linking molecular data to reference specimens addresses cryptic diversity in fungi (Schoch et al., 2014, 473 citations).

Curated Papers

Key Challenges

Why It Matters

ITS primers from Toju et al. (2012) enable metabarcoding surveys of fungal pathogens in agriculture, identifying rust species causing crop losses. Bokulich and Mills (2013) primers support quantitative microbiome analysis in food fermentation using yeasts. Schoch et al. (2014) database links improve diagnostics for yeast infections by tying sequences to verified specimens, aiding clinical mycology.

Key Research Challenges

Primer Bias in HTS

Primer amplification biases limit ultra-high-throughput sequencing of fungal communities. Bokulich and Mills (2013) improved ITS primers to reduce this bias. Challenges persist for rusts with variable ITS regions.

Cryptic Species Detection

Morphology underestimates diversity in yeasts and rusts, requiring linked molecular data. Schoch et al. (2014) highlight needs for reference specimens and accurate naming. Gene genealogies reveal recombination in cryptic yeast species (Matute et al., 2005).

Taxon-Specific Marker Efficacy

ITS coverage varies across Ascomycetes and Basidiomycetes like rusts. Toju et al. (2012) primers achieve high coverage but need validation for polyploid yeasts. Synteny tools like YGOB aid comparative genomics (Byrne and Wolfe, 2005).

Essential Papers

High-Coverage ITS Primers for the DNA-Based Identification of Ascomycetes and Basidiomycetes in Environmental Samples

Hirokazu Toju, Akifumi S. Tanabe, Satoshi Yamamoto et al. · 2012 · PLoS ONE · 1.2K citations

The kingdom Fungi is estimated to include 1.5 million or more species, playing key roles as decomposers, mutualists, and parasites in every biome on the earth. To comprehensively understand the div...

The Yeast Gene Order Browser: Combining curated homology and syntenic context reveals gene fate in polyploid species

Kevin P. Byrne, Kenneth H. Wolfe · 2005 · Genome Research · 748 citations

We developed the Yeast Gene Order Browser (YGOB; http://wolfe.gen.tcd.ie/ygob) to facilitate visual comparisons and computational analysis of synteny relationships in yeasts. The data presented in ...

Improved Selection of Internal Transcribed Spacer-Specific Primers Enables Quantitative, Ultra-High-Throughput Profiling of Fungal Communities

Nicholas A. Bokulich, David A. Mills · 2013 · Applied and Environmental Microbiology · 566 citations

ABSTRACT Ultra-high-throughput sequencing (HTS) of fungal communities has been restricted by short read lengths and primer amplification bias, slowing the adoption of newer sequencing technologies ...

A genome triplication associated with early diversification of the core eudicots

Yuannian Jiao, Jim Leebens‐Mack, Saravanaraj Ayyampalayam et al. · 2012 · Genome biology · 520 citations

Finding needles in haystacks: linking scientific names, reference specimens and molecular data for Fungi

Conrad L. Schoch, Barbara Robbertse, Vincent Robert et al. · 2014 · Database · 473 citations

DNA phylogenetic comparisons have shown that morphology-based species recognition often underestimates fungal diversity. Therefore, the need for accurate DNA sequence data, tied to both correct tax...

Ionizing Radiation Changes the Electronic Properties of Melanin and Enhances the Growth of Melanized Fungi

Ekaterina Dadachova, Ruth Bryan, Xianchun Huang et al. · 2007 · PLoS ONE · 452 citations

Exposure of melanin to ionizing radiation, and possibly other forms of electromagnetic radiation, changes its electronic properties. Melanized fungal cells manifested increased growth relative to n...

Biocontrol yeasts: mechanisms and applications

Florian M. Freimoser, Maria Paula Rueda‐Mejia, Bruno Tilocca et al. · 2019 · World Journal of Microbiology and Biotechnology · 426 citations

Abstract Yeasts occur in all environments and have been described as potent antagonists of various plant pathogens. Due to their antagonistic ability, undemanding cultivation requirements, and limi...

Reading Guide

Foundational Papers

Start with Toju et al. (2012) for ITS primers covering yeasts and rusts; then Bokulich and Mills (2013) for HTS applications; Schoch et al. (2014) for linking data to taxonomy.

Recent Advances

Freimoser et al. (2019) on biocontrol yeasts; Naranjo-Ortíz and Gabaldón (2019) on fungal phylogeny aiding barcoding.

Core Methods

ITS primer design and validation (Toju et al., 2012); HTS optimization (Bokulich and Mills, 2013); synteny browsers for yeast genomes (Byrne and Wolfe, 2005); phylogenetic gene genealogies (Matute et al., 2005).

How PapersFlow Helps You Research Molecular Identification of Yeasts and Rusts

Discover & Search

Research Agent uses searchPapers with 'ITS primers rust fungi identification' to find Toju et al. (2012), then citationGraph reveals 1207 citing papers on primer validation, and findSimilarPapers uncovers Bokulich and Mills (2013) for HTS improvements.

Analyze & Verify

Analysis Agent applies readPaperContent on Toju et al. (2012) to extract primer sequences, verifyResponse with CoVe checks efficacy claims against Schoch et al. (2014), and runPythonAnalysis parses sequence alignments for rust specificity using NumPy, with GRADE scoring evidence strength.

Synthesize & Write

Synthesis Agent detects gaps in rust-specific LSU markers beyond ITS, flags contradictions in primer biases between Toju et al. (2012) and Bokulich and Mills (2013); Writing Agent uses latexEditText for methods section, latexSyncCitations for 10+ papers, latexCompile for PDF, and exportMermaid for primer design flowcharts.

Use Cases

"Analyze ITS primer coverage for Puccinia rust species using Python."

Research Agent → searchPapers('ITS primers Puccinia') → Analysis Agent → readPaperContent(Toju et al. 2012) → runPythonAnalysis(NumPy alignment stats on primer mismatches) → researcher gets CSV of coverage scores and matplotlib degeneracy plots.

"Write LaTeX review on yeast barcode validation."

Synthesis Agent → gap detection(ITS vs LSU for yeasts) → Writing Agent → latexEditText(draft) → latexSyncCitations(Bokulich 2013, Schoch 2014) → latexCompile → researcher gets compiled PDF with synced bibliography and figures.

"Find code for fungal ITS sequence processing."

Research Agent → searchPapers('ITS fungal pipeline') → Code Discovery → paperExtractUrls → paperFindGithubRepo → githubRepoInspect → researcher gets repo links with primer matching scripts from Bokulich-style pipelines.

Automated Workflows

Deep Research workflow scans 50+ papers on ITS barcoding via searchPapers → citationGraph → structured report with GRADE scores on primer efficacy for rusts. DeepScan applies 7-step analysis: readPaperContent(Toju 2012) → verifyResponse(CoVe) → runPythonAnalysis(sequence stats) → gap detection. Theorizer generates hypotheses on LSU+ITS combos for polyploid yeasts from Byrne and Wolfe (2005) synteny data.

Try Doxa for Molecular Identification of Yeasts and Rusts Research

Frequently Asked Questions

What defines molecular identification of yeasts and rusts?

It uses DNA barcodes like ITS and LSU with validated primers for species-level ID from environmental samples, as in Toju et al. (2012).

What are key methods?

High-coverage ITS primers (Toju et al., 2012), bias-reduced primers for HTS (Bokulich and Mills, 2013), and specimen-linked databases (Schoch et al., 2014).

What are key papers?

Toju et al. (2012, 1207 citations) for ITS primers; Bokulich and Mills (2013, 566 citations) for HTS; Schoch et al. (2014, 473 citations) for data linking.