Dataset Information

A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data.

ABSTRACT:

Motivation

A global map of transcription factor binding sites (TFBSs) is critical to understanding gene regulation and genome function. DNaseI digestion of chromatin coupled with massively parallel sequencing (digital genomic footprinting) enables the identification of protein-binding footprints with high resolution on a genome-wide scale. However, accurately inferring the locations of these footprints remains a challenging computational problem.

Results

We present a dynamic Bayesian network-based approach for the identification and assignment of statistical confidence estimates to protein-binding footprints from digital genomic footprinting data. The method, DBFP, allows footprints to be identified in a probabilistic framework and outperforms our previously described algorithm in terms of precision at a fixed recall. Applied to a digital footprinting data set from Saccharomyces cerevisiae, DBFP identifies 4679 statistically significant footprints within intergenic regions. These footprints are mainly located near transcription start sites and are strongly enriched for known TFBSs. Footprints containing no known motif are preferentially located proximal to other footprints, consistent with cooperative binding of these footprints. DBFP also identifies a set of statistically significant footprints in the yeast coding regions. Many of these footprints coincide with the boundaries of antisense transcripts, and the most significant footprints are enriched for binding sites of the chromatin-associated factors Abf1 and Rap1.

Supplementary information

Supplementary material is available at Bioinformatics online.

SUBMITTER: Chen X

PROVIDER: S-EPMC2881360 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature

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Publications

A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data.

Chen Xiaoyu X Hoffman Michael M MM Bilmes Jeff A JA Hesselberth Jay R JR Noble William S WS

Bioinformatics (Oxford, England) 20100601 12

<h4>Motivation</h4>A global map of transcription factor binding sites (TFBSs) is critical to understanding gene regulation and genome function. DNaseI digestion of chromatin coupled with massively parallel sequencing (digital genomic footprinting) enables the identification of protein-binding footprints with high resolution on a genome-wide scale. However, accurately inferring the locations of these footprints remains a challenging computational problem.<h4>Results</h4>We present a dynamic Bayes ...[more]

PMID: 20529925

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Project description:BackgroundCensorship is the primary challenge in survival modeling, especially in human health studies. The classical methods have been limited by applications like Kaplan-Meier or restricted assumptions like the Cox regression model. On the other hand, Machine learning algorithms commonly rely on the high dimensionality of data and ignore the censorship attribute. In addition, these algorithms are more sophisticated to understand and utilize. We propose a novel approach based on the Bayesian network to address these issues.MethodsWe proposed a two-slice temporal Bayesian network model for the survival data, introducing the survival and censorship status in each observed time as the dynamic states. A score-based algorithm learned the structure of the directed acyclic graph. The likelihood approach conducted parameter learning. We conducted a simulation study to assess the performance of our model in comparison with the Kaplan-Meier and Cox proportional hazard regression. We defined various scenarios according to the sample size, censoring rate, and shapes of survival and censoring distributions across time. Finally, we fit the model on a real-world dataset that includes 760 post gastrectomy surgery due to gastric cancer. The validation of the model was explored using the hold-out technique based on the posterior classification error. Our survival model performance results were compared using the Kaplan-Meier and Cox proportional hazard models.ResultsThe simulation study shows the superiority of DBN in bias reduction for many scenarios compared with Cox regression and Kaplan-Meier, especially in the late survival times. In the real-world data, the structure of the dynamic Bayesian network model satisfied the finding from Kaplan-Meier and Cox regression classical approaches. The posterior classification error found from the validation technique did not exceed 0.04, representing that our network predicted the state variables with more than 96% accuracy.ConclusionsOur proposed dynamic Bayesian network model could be used as a data mining technique in the context of survival data analysis. The advantages of this approach are feature selection ability, straightforward interpretation, handling of high-dimensional data, and few assumptions.

Dataset Information

A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data.

Motivation

Results

Supplementary information

Publications

A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data.

Similar Datasets

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