Evaluating the Quality of Human Transcriptional Pathways
Author Information
Author(s): Evgeny Shmelkov, Zuojian Tang, Iannis Aifantis, Alexander Statnikov
Primary Institution: New York University School of Medicine
Hypothesis
How complete and accurate are the transcriptional regulatory pathways in popular biological databases?
Conclusion
The study reveals that most pathway databases contain incomplete and often inaccurate information about transcriptional regulatory targets.
Supporting Evidence
- MetaCore showed statistically significant overlap with experimental results in 84% of cases.
- Other pathway databases had low Jaccard indices, indicating poor overlap with experimental data.
- Only 5.33% of gene targets overlapped between pathway databases on average.
Takeaway
This study looked at how well different databases track the genes controlled by important proteins in our cells, and found that many of them don't match up well with real experimental data.
Methodology
The study integrated genome-wide binding data with functional gene expression data to assess the overlap of transcriptional targets from various pathway databases.
Potential Biases
Potential biases may arise from the reliance on existing databases that may not accurately reflect the current understanding of transcriptional regulation.
Limitations
The study only evaluated seven transcription factors and the results may not generalize to all transcription factors and pathways.
Statistical Information
P-Value
0.0005
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website