Department of Pharmacology, New York University School of Medicine, New York, NY, USA.
Biol Direct. 2011 Feb 28;6:15. doi: 10.1186/1745-6150-6-15.
Pathway databases are becoming increasingly important and almost omnipresent in most types of biological and translational research. However, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors: MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA. The employed benchmarking methodology first involves integrating genome-wide binding with functional gene expression data to derive direct targets of transcription factors. Then the lists of experimentally obtained direct targets are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases.
The results of this study show that for the majority of pathway databases, the overlap between experimentally obtained target genes and targets reported in transcriptional regulatory pathway databases is surprisingly small and often is not statistically significant. The only exception is MetaCore pathway database which yields statistically significant intersection with experimental results in 84% cases. Additionally, we suggest that the lists of experimentally derived direct targets obtained in this study can be used to reveal new biological insight in transcriptional regulation and suggest novel putative therapeutic targets in cancer.
Our study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by solid scientific evidence and rigorous empirical evaluation.
This article was reviewed by Prof. Wing Hung Wong, Dr. Thiago Motta Venancio (nominated by Dr. L Aravind), and Prof. Geoff J McLachlan.
通路数据库在大多数类型的生物和转化研究中变得越来越重要，几乎无处不在。然而，对于这些数据库中存储的通路的质量和完整性知之甚少。本研究对七种研究充分的转录因子（MYC、NOTCH1、BCL6、TP53、AR、STAT1 和 RELA）的人类转录调控通路进行了全面评估。所采用的基准测试方法首先涉及将全基因组结合与功能基因表达数据集成，以得出转录因子的直接靶标。然后，将实验获得的直接靶标列表与来自 10 个常用通路数据库的相关转录靶标列表进行比较。
本研究的结果表明，对于大多数通路数据库，实验获得的靶基因与转录调控通路数据库中报告的靶基因之间的重叠非常小，而且通常不具有统计学意义。唯一的例外是 MetaCore 通路数据库，它在 84%的情况下与实验结果具有统计学上显著的交集。此外，我们建议可以使用本研究中获得的实验衍生直接靶标列表来揭示转录调控中的新生物学见解，并提出癌症中的新潜在治疗靶标。
我们的研究引发了关于使用许多流行的通路数据库来获得转录调控靶标的有效性的争论。我们得出的结论是，应根据可靠的科学证据和严格的实证评估来选择通路数据库。
本文由 Wing Hung Wong 教授、Thiago Motta Venancio 博士（由 L Aravind 博士提名）和 Geoff J McLachlan 教授评审。
