A comprehensive analysis workflow for genome-wide screening data from ChIP-sequencing experiments

Hatice Gulcin Ozer; Doruk Bozdaǧ; Terry Camerlengo; Jiejun Wu; Yi Wen Huang; Tim Hartley; Jeffrey D. Parvin; Tim Huang; Umit V. Catalyurek; Kun Huang

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

A comprehensive analysis workflow for genome-wide screening data from ChIP-sequencing experiments

Hatice Gulcin Ozer, Doruk Bozdaǧ, Terry Camerlengo, Jiejun Wu, Yi Wen Huang, Tim Hartley, Jeffrey D. Parvin, Tim Huang, Umit V. Catalyurek, Kun Huang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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) supprt.

Original language	English (US)
Title of host publication	Bioinformatics and Computational Biology - First International Conference, BICoB 2009, Proceedings
Pages	320-330
Number of pages	11
DOIs	https://doi.org/10.1007/978-3-642-00727-9_30
State	Published - 2009
Externally published	Yes
Event	1st International Conference on Bioinformatics and Computational Biology, BICoB 2009 - New Orleans, LA, United States Duration: Apr 8 2009 → Apr 10 2009

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5462 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	1st International Conference on Bioinformatics and Computational Biology, BICoB 2009
Country/Territory	United States
City	New Orleans, LA
Period	4/8/09 → 4/10/09

Keywords

ChIP-seq
Normalization
Parallelization
Short sequence mapping
Visualization
Workflow

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

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

Cite this

Ozer, H. G., Bozdaǧ, D., Camerlengo, T., Wu, J., Huang, Y. W., Hartley, T., Parvin, J. D., Huang, T., Catalyurek, U. V., & Huang, K. (2009). A comprehensive analysis workflow for genome-wide screening data from ChIP-sequencing experiments. In Bioinformatics and Computational Biology - First International Conference, BICoB 2009, Proceedings (pp. 320-330). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5462 LNBI). https://doi.org/10.1007/978-3-642-00727-9_30

A comprehensive analysis workflow for genome-wide screening data from ChIP-sequencing experiments. / Ozer, Hatice Gulcin; Bozdaǧ, Doruk; Camerlengo, Terry et al.
Bioinformatics and Computational Biology - First International Conference, BICoB 2009, Proceedings. 2009. p. 320-330 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5462 LNBI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ozer, HG, Bozdaǧ, D, Camerlengo, T, Wu, J, Huang, YW, Hartley, T, Parvin, JD, Huang, T, Catalyurek, UV & Huang, K 2009, A comprehensive analysis workflow for genome-wide screening data from ChIP-sequencing experiments. in Bioinformatics and Computational Biology - First International Conference, BICoB 2009, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5462 LNBI, pp. 320-330, 1st International Conference on Bioinformatics and Computational Biology, BICoB 2009, New Orleans, LA, United States, 4/8/09. https://doi.org/10.1007/978-3-642-00727-9_30

Ozer HG, Bozdaǧ D, Camerlengo T, Wu J, Huang YW, Hartley T et al. A comprehensive analysis workflow for genome-wide screening data from ChIP-sequencing experiments. In Bioinformatics and Computational Biology - First International Conference, BICoB 2009, Proceedings. 2009. p. 320-330. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-00727-9_30

Ozer, Hatice Gulcin ; Bozdaǧ, Doruk ; Camerlengo, Terry et al. / A comprehensive analysis workflow for genome-wide screening data from ChIP-sequencing experiments. Bioinformatics and Computational Biology - First International Conference, BICoB 2009, Proceedings. 2009. pp. 320-330 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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AB - 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) supprt.

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A comprehensive analysis workflow for genome-wide screening data from ChIP-sequencing experiments

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Keywords

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