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Related Concept Videos

RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...

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DNA-affinity-purified Chip (DAP-chip) Method to Determine Gene Targets for Bacterial Two component Regulatory Systems
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A highly efficient and effective motif discovery method for ChIP-seq/ChIP-chip data using positional information.

Xiaotu Ma1, Ashwinikumar Kulkarni, Zhihua Zhang

  • 1Department of Molecular and Cell Biology, Center for Systems Biology, University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080, USA.

Nucleic Acids Research
|January 10, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient k-mer occurrence model for DNA motif discovery in chromatin immunoprecipitation (ChIP) data. The new method offers robust, sensitive, and specific motif identification for large datasets, outperforming existing approaches.

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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • DNA motif identification is crucial for understanding transcriptional regulation.
  • Existing methods for motif discovery from chromatin immunoprecipitation (ChIP) data are often inefficient and not robust for large sample sizes.

Purpose of the Study:

  • To develop a novel, efficient, and robust method for DNA motif discovery using ChIP-seq and ChIP-chip data.
  • To improve the analysis of transcriptional regulatory networks by addressing limitations of current motif discovery tools.

Main Methods:

  • A new k-mer occurrence model was developed, focusing on k-mer clustering around ChIP peak summits.
  • A novel word clustering method was implemented to group similar k-mers into position weight matrices (PWMs).
  • The method's performance was evaluated using simulations and diverse ChIP experiment datasets.

Main Results:

  • The proposed method demonstrated higher robustness against noise in ChIP data compared to existing methods.
  • High sensitivity and specificity were achieved in motif discovery across various ChIP experiments.
  • The new method significantly outperforms other methods in terms of speed, especially for large sample sizes.

Conclusions:

  • The developed k-mer occurrence model provides an efficient and effective solution for DNA motif discovery in ChIP experiments.
  • This advancement facilitates a deeper understanding of transcriptional regulatory networks through improved motif identification.
  • The method is particularly valuable for analyzing large-scale ChIP data, offering a significant speed advantage.