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Bootstrapping01:24

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The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
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Each human somatic cell contains 6 billion base-pairs of DNA. Each base-pair is 0.34 nm long, which means that each diploid cell contains a staggering 2 meters of DNA. How is such a long DNA strand packed inside a nucleus measuring only 10 - 20 microns in diameter? 
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The histone proteins in the nucleosomes are post-translationally modified (PTM) to increase or decrease access to DNA. The commonly observed PTMs are methylation, acetylation, phosphorylation, and ubiquitination of lysine amino acids in the histone H3 tail region. These histone modifications have specific meaning for the cell. Hence, they are called "histone code". The protein complex involved in histone modification is termed as "reader-writer" complex.
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Epigenetics is the study of inherited changes in a cell's phenotype without changing the DNA sequences. It provides a form of memory for the differential gene expression pattern to maintain cell lineage, position-effect variegation, dosage compensation, and maintenance of chromatin structures such as telomeres and centromeres. For example, the structure and location of the centromere on chromosomes are epigenetically inherited. Its functionality is not dictated or ensured by the underlying...
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Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
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DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning.

Wenran Li1,2,3, Wing Hung Wong2,3, Rui Jiang1

  • 1MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic and Systems Biology, BNRist, Department of Automation, Tsinghua University, Beijing 100084, China.

Nucleic Acids Research
|March 15, 2019
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Summary
This summary is machine-generated.

DeepTACT, a new deep learning model, predicts gene regulatory element interactions using genome sequence and accessibility data. This method enhances understanding of gene regulation and disease mechanisms, outperforming existing techniques.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Understanding transcriptional regulation and disease mechanisms requires knowledge of regulatory element interactions.
  • The Hi-C technique detects genome-wide chromatin contacts but lacks resolution for specific regulatory element interactions without deep sequencing.
  • Current limitations in Hi-C technology necessitate advanced computational methods for precise contact prediction.

Purpose of the Study:

  • To develop DeepTACT, a deep learning model for predicting chromatin contacts between regulatory elements.
  • To integrate genome sequences and chromatin accessibility data for improved contact prediction.
  • To enable the inference of promoter-enhancer and promoter-promoter interactions.

Main Methods:

  • Developed DeepTACT, a bootstrapping deep learning model.
  • Integrated genome sequence and chromatin accessibility data.
  • Validated predictions using promoter capture Hi-C data.

Main Results:

  • DeepTACT accurately predicts chromatin contacts between regulatory elements, including promoter-enhancer and promoter-promoter interactions.
  • The model demonstrates superior performance compared to existing methods in promoter capture Hi-C data analysis.
  • Identified 'hub promoters' associated with transcriptional activation, housekeeping genes, and fundamental biological processes.
  • Demonstrated the utility of predicted chromatin contacts in disease association studies, linking IFNA2 to coronary artery disease via GWAS data integration.

Conclusions:

  • DeepTACT offers a powerful computational approach to predict regulatory element interactions, overcoming limitations of experimental methods.
  • The model enhances the understanding of gene regulation and provides insights into disease mechanisms.
  • Hub promoters identified by DeepTACT play significant roles in cellular functions and can serve as biomarkers.
  • Integrative analysis of predicted chromatin contacts with genomic data aids in identifying disease associations.