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Related Concept Videos

Phase II Reactions: Methylation Reactions01:17

Phase II Reactions: Methylation Reactions

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Methylation is a phase II biotransformation process involving the attachment of a methyl group to a substrate. Enzymes known as methyltransferases orchestrate this reaction.
The mechanism of methylation unfolds in two stages. The first stage sees a methyltransferase enzyme facilitating the transfer of a methyl group from S-adenosylmethionine (SAM) to the substrate, forming S-adenosylhomocysteine (SAH). The second stage involves further metabolism of SAH into homocysteine, which can be recycled...
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Multiple Halogenation of Methyl Ketones: Haloform Reaction01:28

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A method involving the transformation of methyl ketones to carboxylic acids using excess base and halogen is called the haloform reaction. It begins with the deprotonation of α hydrogen to form an enolate ion which reacts with the electrophilic halogen to give an α-halo ketone. The step continues until all the α protons are substituted to form a trihalomethyl ketone. The resulting molecule is unstable, and in the presence of a hydroxide base, it readily undergoes nucleophilic...
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Classifying Matter by Composition03:35

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Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
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Inertial Frames of Reference01:03

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Newton’s first law is usually considered to be a statement about reference frames. It provides a method for identifying a special type of reference frame: the inertial reference frame. In principle, we can make the net force on a body zero. If its velocity relative to a given frame is constant, then that frame is said to be inertial. So, by definition, an inertial reference frame is a reference frame where Newton's first law holds valid. Newton's first law applies to objects with...
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A reference frame accelerating or decelerating relative to an inertial frame is a non-inertial frame. To help understand this, consider what taking off in an airplane, turning a corner in a car, riding a merry-go-round, and the circular motion of a tropical cyclone all have in common. All these systems are accelerating, decelerating, or rotating relative to the Earth; hence, they all are non-inertial frames. All these systems exhibit inertial forces, which merely seem to arise from motion,...
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What are Estimates?01:06

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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Methylated DNA Immunoprecipitation
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BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for

Elior Rahmani1, Regev Schweiger2, Liat Shenhav1

  • 1Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.

Genome Biology
|September 23, 2018
PubMed
Summary

This study presents a new Bayesian method to accurately estimate cell counts from DNA methylation data. It overcomes limitations of existing methods, enabling better cell composition analysis in genomic studies.

Keywords:
Bayesian modelCell countsCell-type compositionDNA methylationEpigeneticsEpigenome-wide association studiesTissue heterogeneity

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Estimating cell-type composition is crucial for understanding tissue heterogeneity in genomic studies.
  • Current methods for inferring cell counts from DNA methylation data have limitations.
  • Lack of accurate cell count estimation hinders detailed analysis of tissue samples.

Purpose of the Study:

  • To introduce a novel Bayesian semi-supervised method for precise cell count estimation from DNA methylation.
  • To address the limitations of existing methods that fail to isolate individual cell type contributions.
  • To enable cell composition analysis in genomic studies for previously intractable tissues.

Main Methods:

  • Developed a Bayesian semi-supervised approach for DNA methylation-based cell count estimation.
  • Incorporated prior knowledge of cell-type distribution within tissues.
  • Mathematically and empirically demonstrated the method's superiority over alternatives.

Main Results:

  • The proposed method successfully estimates cell counts, with each component corresponding to a single cell type.
  • Demonstrated that alternative methods capture only linear combinations of cell counts, not distinct cell types.
  • Validated the effectiveness of the Bayesian approach through mathematical proofs and empirical testing.

Conclusions:

  • The new Bayesian method accurately deconvolutes cell types from DNA methylation data.
  • This advancement overcomes limitations of previous computational approaches.
  • Opens new avenues for investigating tissue cell compositions in diverse genomic research.