ArticleBioinformatics (Oxford, England)2014
A wavelet-based method to exploit epigenomic language in the regulatory region.
Article in Bioinformatics (Oxford, England), 2014. 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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Who cites it
5 citing papers in PubMed.
- FUNCTION-ON-FUNCTION REGRESSION FOR THE IDENTIFICATION OF EPIGENETIC REGIONS EXHIBITING WINDOWS OF SUSCEPTIBILITY TO ENVIRONMENTAL EXPOSURES.The annals of applied statistics · 2021Article
- Dynamic epigenetic mode analysis using spatial temporal clustering.BMC bioinformatics · 2016Article
- Identification of differentially methylated loci using wavelet-based functional mixed models.Bioinformatics (Oxford, England) · 2016Article
- A stationary wavelet entropy-based clustering approach accurately predicts gene expression.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
- A wavelet approach to detect enriched regions and explore epigenomic landscapes.Journal of computational biology : a journal of computational molecular cell biology · 2014Article
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Authors and funding
3 authors.
Funding
Abstract
motivationEpigenetic landscapes in the regulatory regions reflect binding condition of transcription factors and their co-factors. Identifying epigenetic condition and its variation is important in understanding condition-specific gene regulation. Computational approaches to explore complex multi-dimensional landscapes are needed.
resultsTo study epigenomic condition for gene regulation, we developed a method, AWNFR, to classify epigenomic landscapes based on the detected epigenomic landscapes. Assuming mixture of Gaussians for a nucleosome, the proposed method captures the shape of histone modification and identifies potential regulatory regions in the wavelet domain. For accuracy estimation as well as enhanced computational speed, we developed a novel algorithm based on down-sampling operation and footprint in wavelet. We showed the algorithmic advantages of AWNFR using the simulated data. AWNFR identified regulatory regions more effectively and accurately than the previous approaches with the epigenome data in mouse embryonic stem cells and human lung fibroblast cells (IMR90). Based on the detected epigenomic landscapes, AWNFR classified epigenomic status and studied epigenomic codes. We studied co-occurring histone marks and showed that AWNFR captures the epigenomic variation across time. AVAILABILITY AND IMPLEMENTATION: The source code and supplemental document of AWNFR are available at http://wonk.med.upenn.edu/AWNFR.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.