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¹H NMR Signal Multiplicity: Splitting Patterns

¹H NMR Signal Multiplicity: Splitting Patterns

When protons A and X are coupled, their nuclear spin energy levels are slightly modified. This is because the energy required to excite proton A to a spin state parallel to proton X is slightly different from the energy required for it to become anti-parallel to spin X. Consequently, there are two possible excitation frequencies for A (A1 and A2), depending on the spin state of X, and vice versa. The mutual nature of coupling implies that the difference between frequencies A1 and A2, indicated...

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Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

SignalSpider: probabilistic pattern discovery on multiple normalized ChIP-Seq signal profiles.

Ka-Chun Wong¹, Yue Li¹, Chengbin Peng²

¹Department of Computer Science and Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Jeddah, K.S.A., Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada Department of Computer Science and Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada, CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Jeddah, K.S.A., Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.

Bioinformatics (Oxford, England)

|September 7, 2014

Summary

This summary is machine-generated.

SignalSpider is a new probabilistic model that deciphers transcription factor binding events. It outperforms existing methods in clustering regulatory regions and reveals higher-order combinatorial patterns for gene regulation.

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Chromatin immunoprecipitation sequencing (ChIP-Seq) is crucial for measuring genome-wide transcription factor occupancy.
Understanding gene function requires deciphering combinatorial regulatory mechanisms involving multiple transcription factors.
Probabilistic models are essential for analyzing multiple ChIP-Seq profiles to reveal complex gene regulation.

Purpose of the Study:

To introduce SignalSpider, a novel probabilistic model for deciphering combinatorial transcription factor binding events.
To evaluate SignalSpider's performance against existing methods in clustering regulatory regions.
To identify higher-order combinatorial patterns from multiple ChIP-Seq profiles and visualize genome-wide transcription factor interactions.

Main Methods:

Development of a probabilistic model named SignalSpider.
Application of SignalSpider to normalized ChIP-Seq profiles from the ENCODE consortium.
Utilizing matrix-algebra-optimized executables and source codes for analysis.

Main Results:

SignalSpider demonstrates superior performance in clustering promoter and enhancer regions compared to existing methods.
The model successfully identifies higher-order combinatorial patterns from multiple ChIP-Seq profiles.
Enrichment patterns observed are supported by Gene Ontology, evolutionary conservation, and chromatin interaction data.
A novel enrichment map visualization method reveals genome-wide transcription factor combinatorial patterns.

Conclusions:

SignalSpider provides a powerful tool for understanding complex gene regulation through combinatorial transcription factor binding.
The model enhances our ability to decipher regulatory mechanisms at a genome-wide scale.
The findings offer biological insights and support further focused studies in gene regulation.