DEET

DEET reanalyzes RNA sequencing (RNA-seq) differential expression results to recompute and compare differentially expressed genes (DEGs) across public human studies for systematic enrichment and cross-study comparison.

Key Features:

• Recomputation of DEGs: Systematically recomputes 3,162 human DEG lists derived from The Cancer Genome Atlas, the Genotype-Tissue Expression Consortium, and 142 studies from the Sequence Read Archive.
• Accuracy assessment: Evaluates the accuracy of recomputed DEG lists prior to downstream use.
• Standardized reanalysis: Applies standardized protocols to reanalyze RNA-seq data across multiple large-scale genomic studies to ensure consistency and comparability.
• Systematic querying of overlaps: Identifies recomputed DEG lists that share similar genes, pathways, or transcription factor (TF) targets with user-provided gene lists.
• Support for hypothesis generation: Enables identification of relevant studies based on shared DEGs, pathways, or TF targets to inform data-driven hypotheses and literature review.

Scientific Applications:

• Cross-study comparison and validation: Compare DEG patterns across studies to validate differential expression findings.
• Pathway and regulatory inference: Identify shared pathways and transcription factor targets to investigate regulatory mechanisms.
• Hypothesis generation and literature linking: Find studies with overlapping results to generate hypotheses and support literature-based follow-up.
• Insights into gene function and disease biology: Use recomputed DEGs to explore gene function and disease-associated expression patterns.

Methodology:

Systematic reanalysis of RNA-seq data by recomputing DEGs using standardized protocols from public datasets (TCGA, GTEx, and 142 SRA studies), evaluation of recomputed DEG list accuracy, and systematic querying for shared genes, pathways, or TF targets with user gene lists.