Virtual ChIP-seq Predictions for 36 Transcription Factors in Roadmap Epigenomics Tissues

36 transcription factor binding predictions generated by Virtual ChIP-seq for tissues in the Roadmap Epigenomics Project with matched DNase-seq and RNA-seq data. The dataset, created by Mehran Karimzadeh at the University of Toronto, includes BED files with posterior probability scores for each prediction. Predictions are included for transcription factors where the median Matthews correlation coefficient exceeded 0.3 in validation cell types.

Use Cases

• Predicting transcription factor binding sites in epigenomic data based on posterior probability scores.
• Analyzing the regulatory landscape across different tissues and ages from the Roadmap Epigenomics Project.
• Integrating predicted binding sites with matched DNase-seq and RNA-seq data for regulatory network inference.
• Validating and benchmarking computational models for transcription factor binding prediction.

Strengths

• Includes predictions for 36 distinct transcription factors.
• Provides posterior probability scores with defined cutoffs for validation.
• Focuses on predictions with a median Matthews correlation coefficient greater than 0.3 in validation.
• Data is associated with the well-characterized Roadmap Epigenomics Project tissues.

Limitations

• Row count and total dataset size are unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

University of Toronto, Mehran Karimzadeh

Collection Method

Computational predictions (Virtual ChIP-seq) applied to Roadmap Epigenomics Project data.