Abstract
HOT regions, short for high occupied target regions, bound by many transcription factors (TFs) are considered to be one of the most intriguing findings of the recent large-scale sequencing studies. Recent researches have reported that HOT regions are enriched with so many biological processes and functions, which are related with promoters, enhancers and fraction of motifs. Hence, there are a lot of studies focused on the discovery of HOT regions with TFs datasets. Unfortunately, because of the limited TFs datasets from next generation sequencing (NGS) technology and huge time consuming, the HOT regions in each cell line of human genome can’t be fully marked. Here, unlike the previous jobs, we have made an identification of HOT regions by means of machine learning algorithms in 14 different human cell-lines with chromatin modification datasets. The outperform results of these cell-lines can prove the effectiveness and precision of our assumption enough. In addition, we have discovered the cell-type specific HOT regions (CSHRs) of each cell line, which is used to elucidate the associations with cell-type specific regulatory functions.
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Acknowledgements
This work was supported by the grants of the National Science Foundation of China, Nos. 61133010, 61520106006, 31571364, 61532008, 61572364, 61373105, 61303111, 61411140249, 61402334, 61472282, 61472280, 61472173, 61572447, and 61373098, China Postdoctoral Science Foundation Grant, Nos. 2014M561513 and 2015M580352.
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He, F., Li, N. (2016). Identification of HOT Regions in the Human Genome Using Differential Chromatin Modifications. In: Huang, DS., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2016. Lecture Notes in Computer Science(), vol 9773. Springer, Cham. https://doi.org/10.1007/978-3-319-42297-8_79
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