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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...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...

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Comparative study on ChIP-seq data: normalization and binding pattern characterization.

Cenny Taslim1, Jiejun Wu, Pearlly Yan

  • 1Department of Molecular Virology, Immunology & Medical Genetics, Ohio State University, Columbus, OH 43210, USA. taslim.2@osu.edu

Bioinformatics (Oxford, England)
|June 30, 2009
PubMed
Summary
This summary is machine-generated.

We developed a new algorithm to compare Chromatin Immunoprecipitation sequencing (ChIP-seq) data across multiple samples. This method identifies differential RNA polymerase II binding patterns, revealing potential cancer-related gene dysregulation.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Chromatin Immunoprecipitation sequencing (ChIP-seq) is a powerful technique for genome-wide protein binding analysis.
  • Existing methods require improvements for efficient multi-sample comparisons and gene characterization.
  • RNA polymerase II (Pol II) binding patterns are crucial for understanding gene regulation.

Purpose of the Study:

  • To present a novel non-linear normalization algorithm for comparing ChIP-seq data.
  • To introduce a mixture modeling approach for characterizing genes based on Pol II binding.
  • To enable more comprehensive analysis of ChIP-seq data across multiple samples.

Main Methods:

  • A two-step non-linear normalization using locally weighted regression (LOESS).
  • An Exponential-Normal(K) mixture model for data comparison.
  • Identification of differential binding sites using local false discovery rate (fdr).
  • Hierarchical clustering for characterizing Pol II binding patterns.

Main Results:

  • The algorithm successfully normalized and compared ChIP-seq data from different samples.
  • Differential binding sites were identified, with enriched regions associated with cancer (P < 0.0001).
  • Analysis of tamoxifen-resistant cells suggested dysregulation in cell cycle and gene expression pathways.

Conclusions:

  • The developed non-linear normalization method is effective for multi-sample ChIP-seq data analysis.
  • The approach can characterize genes by their Pol II binding patterns.
  • This methodology aids in understanding gene regulation in various biological contexts, including cancer.