Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses
Dual threshold optimization and network inference reveal convergent evidence from TF binding locations and TF perturbation responses
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DOI：
10.1101/gr.259655.119
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发表时间：
2020-03-01
期刊：
影响因子：
7
通讯作者：
Brent, Michael R.
中科院分区：
文献类型：
--
作者：
Kang, Yiming;Patel, Nikhil R.;Brent, Michael R.
每个转录因子（TF）的直接功能靶标的高置信度图谱需要来自独立来源的收敛证据。两个重要的证据来源是TF结合的位置和直接TF扰动的转录反应。这两种类型的系统数据集都存在于酵母和人类中，但它们很少集中在TF的一组共同的直接功能靶标上。即使是少数既结合又有反应的基因也可能不是直接的功能靶点。我们的分析表明，当有许多非功能性结合位点和许多间接靶标时，预期非功能性位点偶然地出现在间接靶标的顺式调节DNA中。为了解决这个问题，我们引入了双阈值优化（DTO），一种新的方法，用于设置绑定和扰动响应数据的显着阈值，并表明它提高了收敛性。它还能够将绑定数据与已经由网络推理算法处理的扰动响应数据进行比较，这进一步提高了收敛性。双阈值优化和网络推理的结合极大地扩展了酵母和人类的高置信度TF网络图。接下来，我们分析了一个全面的新的数据集测量转录反应后不久，诱导过表达的酵母TF。我们还提出了一个新的酵母结合位置的数据集获得转座子呼叫卡，并将其与最近的ChIP-exo数据进行比较。这些新的数据集提高了收敛性，并协同扩展了高置信度网络。
A high-confidence map of the direct, functional targets of each transcription factor (TF) requires convergent evidence from independent sources. Two significant sources of evidence are TF binding locations and the transcriptional responses to direct TF perturbations. Systematic data sets of both types exist for yeast and human, but they rarely converge on a common set of direct, functional targets for a TF. Even the few genes that are both bound and responsive may not be direct functional targets. Our analysis shows that when there are many nonfunctional binding sites and many indirect targets, nonfunctional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. To address this problem, we introduce dual threshold optimization (DTO), a new method for setting significance thresholds on binding and perturbation-response data, and show that it improves convergence. It also enables comparison of binding data to perturbation-response data that have been processed by network inference algorithms, which further improves convergence. The combination of dual threshold optimization and network inference greatly expands the high-confidence TF network map in both yeast and human. Next, we analyze a comprehensive new data set measuring the transcriptional response shortly after inducing over-expression of a yeast TF. We also present a new yeast binding location data set obtained by transposon calling cards and compare it to recent ChIP-exo data. These new data sets improve convergence and expand the high-confidence network synergistically.