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Power Spectrum-Based Genomic Feature Extraction from High-Throughput ChIP-seq Sequences

  • Binhua TangEmail author
  • Yufan Zhou
  • Victor X. Jin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)

Abstract

Due to its enhanced accuracy and high-throughput capability in capturing genetic activities, recently Next Generation Sequencing technology is being applied prevalently in biomedical study for tackling diverse topics. Within the work, we propose a computational method for answering such questions as deciding optimal argument pairs (peak number, p-value threshold, selected bin size and false discovery rate) from estrogen receptor α ChIP-seq data, and detecting corresponding transcription factor binding sites. We employ a signal processing-based approach to extract inherent genomic features from the identified transcription factor binding sites, which illuminates novel evidence for further analysis and experimental validation. Thus eventually we attempt to exploit the potentiality of ChIP-seq for deep comprehension of inherent biological meanings from the high-throughput genomic sequences.

Keywords

ChIP-seq Genomic feature Optimal argument pair Transcription factor binding site Comprehensive analysis 

Notes

Acknowledgments

This work was supported by the Fundamental Research Funds for China Central Universities [grant number 2016B08914 to BHT] and Changzhou Science & Technology Program [grant number CE20155050 to BHT]. This work made use of the resources supported by the NSFC-Guangdong Mutual Funds for Super Computing Program (China), and the Open Cloud Consortium (OCC)-sponsored project resource, which supported in part by grants from Gordon and Betty Moore Foundation and the National Science Foundation (USA) and major contributions from OCC members.

References

  1. 1.
    Mardis, E.R.: ChIP-seq: welcome to the new frontier. Nat. Methods 4(8), 613–614 (2007)CrossRefGoogle Scholar
  2. 2.
    Martinez, G.J., Rao, A.: Cooperative transcription factor complexes in control. Science 338(6109), 891–892 (2012)CrossRefGoogle Scholar
  3. 3.
    Kilpinen, H., Barrett, J.C.: How next-generation sequencing is transforming complex disease genetics. Trends Genetics (TIG) 29(1), 23–30 (2013)CrossRefGoogle Scholar
  4. 4.
    Chikina, D.M., Troyanskaya, O.G.: An effective statistical evaluation of ChIP-seq dataset similarity. Bioinformatics 28(5), 607–613 (2012)CrossRefGoogle Scholar
  5. 5.
    Furey, T.S.: ChIP-seq and beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Nat. Rev. Genet. 13, 840–852 (2012)CrossRefGoogle Scholar
  6. 6.
    Oppenheim, A.V., Schafer, R.W.: Discrete-Time Signal Processing. Prentice Hall Signal Processing Series, 3rd edn. Prentice Hall, Upper Saddle River (2010). Ed. by A.V. OppenheimzbMATHGoogle Scholar
  7. 7.
    Tang, B., Hsu, H.K., Hsu, P.Y.: Hierarchical modularity in ERΑ transcriptional network is associated with distinct functions and implicates clinical outcomes. NPG Sci. Rep. 2, 875 (2012)Google Scholar
  8. 8.
    Wang, S.L., Zhu, Y.H., Jia, W.: Robust classification method of tumor subtype by using correlation filters. IEEE/ACM Trans. Comput. Biol. Bioinform. 9(2), 580–591 (2012)CrossRefGoogle Scholar
  9. 9.
    Zhang, Y., Liu, T., Meyer, C.A., et al.: Model-based Analysis of ChIP-seq (MACS). Genome Biol. 9(9), R137 (2008)CrossRefGoogle Scholar
  10. 10.
    Lan, X., Bonneville, R., Apostolos, J., et al.: W-ChIPeaks: a comprehensive web application tool for processing chip-chip and ChIP-seq data. Bioinformatics 27(3), 428–430 (2011)CrossRefGoogle Scholar
  11. 11.
    Spyrou, C., Stark, R., Lynch, A.G., et al.: BayesPeak: Bayesian analysis of ChIP-seq data. BMC Bioinform. 10(1), 299 (2009)CrossRefGoogle Scholar
  12. 12.
    Fejes, A.P., Robertson, G., Bilenky, M., et al.: FindPeaks 3.1: a tool for identifying areas of enrichment from massively parallel short-read sequencing technology. Bioinformatics 24(15), 1729–1730 (2008)CrossRefGoogle Scholar
  13. 13.
    Zhu, L., Guo, W.L., Deng, S.P., et al.: ChIP-PIT: enhancing the analysis of ChIP-seq data using convex-relaxed pair-wise interaction tensor decomposition. IEEE/ACM Trans. Comput. Biol. Bioinform. 13(1), 55–63 (2016)CrossRefGoogle Scholar
  14. 14.
    Cheng, A.S.L., Jin, V.X., Fanet, M.: Combinatorial analysis of transcription factor partners reveals recruitment of C-MYC to estrogen Receptor-Α responsive promoters. Mol. Cell 21(3), 393–404 (2006)CrossRefGoogle Scholar
  15. 15.
    Ou, Y.Y., Chen, S.-A., Gromiha, M.M.: Classification of transporters using efficient radial basis function networks with position-specific scoring matrices and biochemical properties. Proteins: Struct. Funct. Bioinform. 78(7), 1789–1797 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.College of the Internet of ThingsHohai UniversityJiangsuChina
  2. 2.School of Public HealthShanghai Jiao Tong UniversityShanghaiChina
  3. 3.Department of Molecular Medicine and BiostatisticsUniversity of Texas Health Science CenterSan AntonioUSA

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