ArticleBMC bioinformatics2016
Dynamic epigenetic mode analysis using spatial temporal clustering.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Genome-wide analysis of epigenetic dynamics across human developmental stages and tissues.BMC genomics · 2019Article
- PDRLGB: precise DNA-binding residue prediction using a light gradient boosting machine.BMC bioinformatics · 2018Article
- A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.BMC bioinformatics · 2017Article
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Authors and funding
5 authors.
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Abstract
backgroundDifferentiation of human embryonic stem cells requires precise control of gene expression that depends on specific spatial and temporal epigenetic regulation. Recently available temporal epigenomic data derived from cellular differentiation processes provides an unprecedented opportunity for characterizing fundamental properties of epigenomic dynamics and revealing regulatory roles of epigenetic modifications.
resultsThis paper presents a spatial temporal clustering approach, named STCluster, which exploits the temporal variation information of epigenomes to characterize dynamic epigenetic mode during cellular differentiation. This approach identifies significant spatial temporal patterns of epigenetic modifications along human embryonic stem cell differentiation and cluster regulatory sequences by their spatial temporal epigenetic patterns.
conclusionsThe results show that this approach is effective in capturing epigenetic modification patterns associated with specific cell types. In addition, STCluster allows straightforward identification of coherent epigenetic modes in multiple cell types, indicating the ability in the establishment of the most conserved epigenetic signatures during cellular differentiation process.
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