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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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Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.

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Related Experiment Video

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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AREM: aligning short reads from ChIP-sequencing by expectation maximization.

Daniel Newkirk1, Jacob Biesinger, Alvin Chon

  • 1Department of Biological Chemistry, University of California, Irvine, California 92697, USA.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|November 1, 2011
PubMed
Summary

This study introduces AREM, a new ChIP-Seq analysis method that uses all sequencing reads for a more complete genome-wide view. AREM improves peak detection, especially in repeat regions, enhancing transcription factor and protein binding pattern analysis.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Chromatin immunoprecipitation sequencing (ChIP-Seq) is crucial for mapping DNA-binding protein patterns genome-wide.
  • Existing ChIP-Seq analysis methods often exclude reads mapping to repetitive regions, limiting detection power.
  • Identifying binding sites in repeat sequences is essential for a comprehensive understanding of protein function.

Purpose of the Study:

  • To develop a novel probabilistic approach for ChIP-Seq data analysis that incorporates all sequencing reads.
  • To enhance the detection of genome-wide binding patterns, particularly in previously challenging repeat regions.
  • To provide a more accurate and complete genome-wide view of protein-DNA interactions.

Main Methods:

  • A probabilistic mixture model was employed to represent enriched regions and genomic background.
  • An expectation-maximization (E-M) algorithm, termed AREM (aligning reads by expectation maximization), was implemented.
  • Maximum likelihood estimation was used to determine the locations of enriched regions.

Main Results:

  • The AREM algorithm successfully identified 19,935 Rad21 and 1,748 Srebp-1 binding peaks in the mouse genome.
  • AREM detected 7.6% of Rad21 peaks and 13% of Srebp-1 peaks that were missed by methods using only uniquely mapped reads.
  • This demonstrates AREM's superior capability in identifying binding events within repeat sequences.

Conclusions:

  • The AREM algorithm offers a significant advancement in ChIP-Seq data analysis by utilizing all reads.
  • This approach provides a more comprehensive and accurate genome-wide characterization of protein-DNA binding patterns.
  • AREM enhances the discovery of binding sites, especially in complex genomic regions like repeats.