A Comprehensive Analysis Workflow for Genome-Wide Screening Data from ChIP-Sequencing Experiments

  • Hatice Gulcin Ozer
  • Doruk Bozdağ
  • Terry Camerlengo
  • Jiejun Wu
  • Yi-Wen Huang
  • Tim Hartley
  • Jeffrey D. Parvin
  • Tim Huang
  • Umit V. Catalyurek
  • Kun Huang
Conference paper

DOI: 10.1007/978-3-642-00727-9_30

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5462)
Cite this paper as:
Ozer H.G. et al. (2009) A Comprehensive Analysis Workflow for Genome-Wide Screening Data from ChIP-Sequencing Experiments. In: Rajasekaran S. (eds) Bioinformatics and Computational Biology. Lecture Notes in Computer Science, vol 5462. Springer, Berlin, Heidelberg

Abstract

ChIP-sequencing is a new technique for generating short DNA sequences useful in analyzing DNA-protein interactions and carrying out genome-wide studies. Although there are some studies to process and analyze ChIP-sequencing data, a complete workflow has not been reported yet. The size of the data and broad range of biological questions are the main challenges to establish a data analysis workflow for ChIP-sequencing data. In this paper, we present the ChIP-sequencing data analysis workflow that we developed at the Ohio State University Comprehensive Cancer Center Bioinformatics Shared Resources. This pipeline utilizes 1) use of different mapping algorithms such as Eland, MapReads, SeqMap, RMAP to align short sequence reads to the reference genome 2) a novel normalization algorithm to detect significant binding densities and to compare binding densities of different experiments 3) gene database mapping and 3D binding density visualization 4) distributed computing and high performance computing (HPC) support.

Keywords

ChIP-seq workflow short sequence mapping parallelization normalization visualization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hatice Gulcin Ozer
    • 1
    • 2
  • Doruk Bozdağ
    • 1
    • 3
  • Terry Camerlengo
    • 1
  • Jiejun Wu
    • 4
  • Yi-Wen Huang
    • 4
  • Tim Hartley
    • 1
  • Jeffrey D. Parvin
    • 1
    • 2
  • Tim Huang
    • 4
  • Umit V. Catalyurek
    • 1
  • Kun Huang
    • 1
    • 2
  1. 1.Department of Biomedical InformaticsThe Ohio State UniversityUSA
  2. 2.The Ohio State University Comprehensive Cancer Center Biomedical Informatics Shared ResourceUSA
  3. 3.Department of Electrical & Computer EngineeringThe Ohio State UniversityUSA
  4. 4.Department of Molecular Virology, ImmunologyThe Ohio State UniversityColumbusUSA

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