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

Bioinformatics

Volumn 25, Issue 18, 2009, Pages 2334-2340

  Taslim, Cenny ^a   Wu, Jiejun ^a   Yan, Pearlly ^a   Singer, Greg ^a   Parvin, Jeffrey ^a   Huang, Tim ^a   Lin, Shili ^a   Huang, Kun ^a  

^a OHIO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DNA FRAGMENT; RNA POLYMERASE II;

ALGORITHM; ARTICLE; BINDING SITE; BREAST CANCER; CANCER CELL CULTURE; CANCER RESISTANCE; CELL CYCLE REGULATION; CELL STRAIN MCF 7; CHROMATIN IMMUNOPRECIPITATION; CLUSTER ANALYSIS; COMPARATIVE STUDY; CONTROLLED STUDY; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; HIGH THROUGHPUT SCREENING; HUMAN; HUMAN CELL; NONLINEAR SYSTEM; PRIORITY JOURNAL; PROTEIN BINDING; STATISTICAL MODEL;

BINDING SITES; BREAST NEOPLASMS; CELL LINE, TUMOR; CHROMATIN IMMUNOPRECIPITATION; COMPUTATIONAL BIOLOGY; FEMALE; HUMANS; RNA POLYMERASE II; SEQUENCE ANALYSIS, DNA;

EID: 69849103628     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btp384     Document Type: Article

References (25)

1. Akaike,H. (1973) Information theory and an extension of the maximum likelihood principle. In International Symposium on Information Theory, 2nd, Tsahkadsor, Armenian SSR, pp. 267-281.
2. Carroll,J. et al. (2006) Genome-wide analysis of estrogen receptor binding sites. Nat. Genet., 38, 1289-1297.
3. Cleveland,W.S. (1988) Locally-weighted regression: An approach to regression analysis by local fitting. J. Am. Stat. Assoc., 85 596-610.
4. Dean,N. and Raftery,A.E. (2005) Normal uniform mixture differential gene expression detection for cDNA microarrays. BMC Bioinformatics, 6, 173.
5. Fan,M. et al. (2006) Diverse gene expression and DNA methylation profiles correlate with differential adaptation of breast cancer cells to the antiestrogens tamoxifen and fulvestrant. Cancer Res., 66 11954-11966.
6. Feng,W. et al. (2008) A poisson mixture model to identify changes in RNA polymerase II binding quantity using high-throughput sequencing technology. BMC Genomics, 9 (Suppl. 2), S23.
7. Horak,C.E. and Snyder,M. (2002) ChIP-chip: A genomic approach for identifying transcription factor binding sites. Methods Enzymol, 350, 469-483.
8. Hulka,B. et al. (2004) Steroid hormones and risk of breast cancer. Cancer, 74 (Suppl. 3), 1111-1124.
9. Ji,H. et al. (2008) An integrated software system for analyzing ChIP-chip and ChIP-seq data. Nat. Biotechnol., 26, 1293-1300.
10. Johnson,D.S. et al. (2007) Genome-wide mapping of in vivo protein-DNA interactions. Science, 316, 1441-1442.
11. Khalili,A. et al. (2009) A robust unified approach to analyzing methylation and gene expression data. Comput. Stat. Data Anal., 53, 1701-1710.
12. Kharchenko,P.V.V. et al. (2008) Design and analysis of ChIP-seq experiments for DNA-binding proteins. Nature Biotechnol., 26 1351-1359.
13. Lin,C.-Y. et al. (2004) Discovery of estrogen receptor alpha target genes and response elements in breast tumor cells. Genome Biol., 5, R66.
14. Lin,C.Y. et al. (2007) Whole-genome cartography of estrogen receptor alpha binding sites. PLoS Genet., 3, e87.
15. Oetken,G. et al. (1975) New results in the design of digital interpolators. IEEE Trans. Acoust. Speech Signal Process. [see also IEEE Trans. Signal Process.], 23, 301-309.
16. Osborne,C.K. and Schiff,R. (2005) Estrogen-receptor biology: Continuing progress and therapeutic implications. J. Clin. Oncol., 23, 1616-1622.
17. Ren,B. et al. (2000) Genome-wide location and function of DNA binding proteins. Science, 290, 2306-2309.
18. Rozowsky,J. et al. (2009) Peakseq enables systematic scoring of ChIP-seq experiments relative to controls. Nat. Biotechnol., 27 66-75.
19. Schena,M. et al. (1996) Parallel human genome analysis: microarray-based expression monitoring of 1000 genes. Proc. Natl Acad. Sci. USA, 93, 10614-10619.
20. Singer,G. et al. (2008) Genome-wide analysis of alternative promoters of human genes using a custom promoter tiling array. BMC Genomics, 9, 349.
21. Teicher,H. (1961) Identifiability of mixtures. Ann. Math. Stat., 32, 244-248.
22. Teicher,H. (1963) Identifiability of finite mixtures. Ann. Math. Stat., 34, 1265-1269.
23. Welboren,W.-J. et al. (2009) ChIP-Seq of ERalpha and RNA polymerase II defines genes differentially responding to ligands. EMBO J., [Epub ahead of print doi = 10.1038/emboj.2009.88, April 2, 2009].
24. Xu,H. et al. (2008) An HMM approach to genome-wide identification of differential histone modification sites from chip-seq data. Bioinformatics, 24, 2344-2349.
25. Zhang,Y. et al. (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol., 9, R137+.