ArticleBioinformatics (Oxford, England)2010
A varying threshold method for ChIP peak-calling using multiple sources of information.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Integrative analysis of epigenetics data identifies gene-specific regulatory elements.Nucleic acids research · 2021Article
- Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning.Bioinformatics (Oxford, England) · 2017Article
- Article
- Dynamics of the epigenetic landscape during erythroid differentiation after GATA1 restoration.Genome research · 2011Article
- Fast and Accurate Approximation to Significance Tests in Genome-Wide Association Studies.Journal of the American Statistical Association · 2011Article
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Authors and funding
2 authors.
Funding
Abstract
motivationGene regulation commonly involves interaction among DNA, proteins and biochemical conditions. Using chromatin immunoprecipitation (ChIP) technologies, protein-DNA interactions are routinely detected in the genome scale. Computational methods that detect weak protein-binding signals and simultaneously maintain a high specificity yet remain to be challenging. An attractive approach is to incorporate biologically relevant data, such as protein co-occupancy, to improve the power of protein-binding detection. We call the additional data related with the target protein binding as supporting tracks.
resultsWe propose a novel but rigorous statistical method to identify protein occupancy in ChIP data using multiple supporting tracks (PASS2). We demonstrate that utilizing biologically related information can significantly increase the discovery of true protein-binding sites, while still maintaining a desired level of false positive calls. Applying the method to GATA1 restoration in mouse erythroid cell line, we detected many new GATA1-binding sites using GATA1 co-occupancy data. AVAILABILITY: http://stat.psu.edu/ approximately yuzhang/pass2.tar.
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