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Related Concept Videos

DNA Packaging00:58

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Each human somatic cell contains 6 billion base pairs of DNA. Each base pair is 0.34 nm long, meaning each diploid cell contains a staggering 2 meters of DNA. This long DNA strand is packed inside a nucleus measuring only 10-20 microns in diameter with the help of specialized DNA-binding proteins called histones. Together they form a compact DNA-protein complex called chromatin. The chromatin is further compacted into higher-order structures. The highest level of compaction is achieved during...
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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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Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
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The Statistical Package for the Social Sciences, or SPSS, is a data management and analysis software suite. Developed by SPSS Inc. in 1968 and acquired by IBM in 2009, this tool was initially designed for social science data analysis, evolving to serve a wider range of disciplines. It was later renamed to Statistical Product and Service Solutions.
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BPRMeth: a flexible Bioconductor package for modelling methylation profiles.

Chantriolnt-Andreas Kapourani1, Guido Sanguinetti1,2

  • 1School of Informatics, University of Edinburgh, Edinburgh, UK.

Bioinformatics (Oxford, England)
|March 10, 2018
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Summary

BPRMeth quantifies DNA methylation profiles using generalized linear models. This enhanced Bioconductor package offers faster analysis with Bayesian confidence measures and supports diverse platforms like single-cell and methylation arrays.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput DNA methylation measurements are crucial in biomedical research.
  • Analyzing spatial variation of methylation across genomic regions offers functional insights.

Purpose of the Study:

  • Introduce BPRMeth, an enhanced Bioconductor package for DNA methylation profiling.
  • Improve quantification of methylation profiles using advanced statistical methods.

Main Methods:

  • Utilizes generalized linear model regression for methylation quantification.
  • Incorporates a fast, variational inference approach for Bayesian posterior confidence measures.
  • Adapts observation models for compatibility with diverse platforms.

Main Results:

  • Provides enhanced DNA methylation profile quantification.
  • Enables Bayesian posterior confidence measures for model reliability.
  • Supports single-cell analyses and methylation arrays.

Conclusions:

  • BPRMeth offers a robust and versatile tool for DNA methylation analysis.
  • The enhancements improve accuracy and applicability across various biological contexts.
  • Facilitates deeper understanding of DNA methylation's functional role.