Pan-Transcriptome Analysis of Pine Wilt Disease in Resistant and Susceptible Pinus Species

A dataset by Bae Young Choi from 2026 contains pan-transcriptome analysis data for pine wilt disease progression across six Pinus species and a hybrid. It includes results from a deep learning-based phenotyping program quantifying disease symptoms over 14 weeks. The data supports findings on gene expression differences related to water conservation and pathogen response between resistant and susceptible trees.

Use Cases

• Train models to identify gene expression markers for disease resistance based on transcriptomic profiles.
• Compare molecular responses to pathogen infection across different pine species as described in the pan-transcriptomic analysis.
• Validate deep learning-based phenotyping programs for quantifying plant disease symptoms mentioned in the description.
• Investigate the role of the elongation factor Tu receptor (EFR) and pathogenesis-related (PR)-3 genes in nematode detection.

Strengths

• Includes data from six distinct Pinus species and a hybrid, allowing for comparative analysis.
• Generated using a novel deep learning-based phenotyping program for precise symptom quantification.
• License is CC-BY-4.0, permitting broad reuse with attribution.

Limitations

• The dataset is very small at 6.4 KB, suggesting limited raw data or highly summarized results.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Bae Young Choi via figshare.

Collection Method

Deep learning-based phenotyping and pan-transcriptome analysis conducted on Pinus species infected with pine wood nematode.

Time Range

The experiment monitored symptom progression for 14 weeks post-infection.

Freshness

Last updated 2026-03-25 22:01:54.