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Items: 1 to 20 of 67

1.
2.

SIOMICS: a novel approach for systematic identification of motifs in ChIP-seq data.

Ding J, Hu H, Li X.

Nucleic Acids Res. 2014 Mar;42(5):e35. doi: 10.1093/nar/gkt1288.

3.

A blind deconvolution approach to high-resolution mapping of transcription factor binding sites from ChIP-seq data.

Lun DS, Sherrid A, Weiner B, Sherman DR, Galagan JE.

Genome Biol. 2009;10(12):R142. doi: 10.1186/gb-2009-10-12-r142.

4.

Cell-type specificity of ChIP-predicted transcription factor binding sites.

Håndstad T, Rye M, Močnik R, Drabløs F, Sætrom P.

BMC Genomics. 2012 Aug 3;13:372. doi: 10.1186/1471-2164-13-372.

5.

Systematic discovery of cofactor motifs from ChIP-seq data by SIOMICS.

Ding J, Dhillon V, Li X, Hu H.

Methods. 2015 Jun;79-80:47-51. doi: 10.1016/j.ymeth.2014.08.006.

PMID:
25171961
6.

Computational analysis of ChIP-seq data.

Ji H.

Methods Mol Biol. 2010;674:143-59. doi: 10.1007/978-1-60761-854-6_9.

PMID:
20827590
7.

A practical comparison of methods for detecting transcription factor binding sites in ChIP-seq experiments.

Laajala TD, Raghav S, Tuomela S, Lahesmaa R, Aittokallio T, Elo LL.

BMC Genomics. 2009 Dec 18;10:618. doi: 10.1186/1471-2164-10-618.

8.
9.

ChIPBase: a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data.

Yang JH, Li JH, Jiang S, Zhou H, Qu LH.

Nucleic Acids Res. 2013 Jan;41(Database issue):D177-87. doi: 10.1093/nar/gks1060.

10.

An improved ChIP-seq peak detection system for simultaneously identifying post-translational modified transcription factors by combinatorial fusion, using SUMOylation as an example.

Cheng CY, Chu CH, Hsu HW, Hsu FR, Tang CY, Wang WC, Kung HJ, Chang PC.

BMC Genomics. 2014;15 Suppl 1:S1. doi: 10.1186/1471-2164-15-S1-S1.

11.

A biophysical model for analysis of transcription factor interaction and binding site arrangement from genome-wide binding data.

He X, Chen CC, Hong F, Fang F, Sinha S, Ng HH, Zhong S.

PLoS One. 2009 Dec 1;4(12):e8155. doi: 10.1371/journal.pone.0008155.

12.

A fully Bayesian hidden Ising model for ChIP-seq data analysis.

Mo Q.

Biostatistics. 2012 Jan;13(1):113-28. doi: 10.1093/biostatistics/kxr029.

PMID:
21914728
13.

ChIP-PaM: an algorithm to identify protein-DNA interaction using ChIP-Seq data.

Wu S, Wang J, Zhao W, Pounds S, Cheng C.

Theor Biol Med Model. 2010 Jun 3;7:18. doi: 10.1186/1742-4682-7-18.

14.

Extracting transcription factor targets from ChIP-Seq data.

Tuteja G, White P, Schug J, Kaestner KH.

Nucleic Acids Res. 2009 Sep;37(17):e113. doi: 10.1093/nar/gkp536.

15.
16.

The Triform algorithm: improved sensitivity and specificity in ChIP-Seq peak finding.

Kornacker K, Rye MB, Håndstad T, Drabløs F.

BMC Bioinformatics. 2012 Jul 24;13:176. doi: 10.1186/1471-2105-13-176.

17.

Epigenetic analysis: ChIP-chip and ChIP-seq.

Pellegrini M, Ferrari R.

Methods Mol Biol. 2012;802:377-87. doi: 10.1007/978-1-61779-400-1_25.

PMID:
22130894
18.

Peak Finder Metaserver - a novel application for finding peaks in ChIP-seq data.

Kruczyk M, Umer HM, Enroth S, Komorowski J.

BMC Bioinformatics. 2013 Sep 23;14:280. doi: 10.1186/1471-2105-14-280.

19.

[Genome-wide analysis of chromatin and transcription factor by ChIP-seq].

Kurata T.

Tanpakushitsu Kakusan Koso. 2009 Aug;54(10):1248-55. Review. Japanese. No abstract available.

PMID:
19663251
20.

Searching ChIP-seq genomic islands for combinatorial regulatory codes in mouse embryonic stem cells.

Chen G, Zhou Q.

BMC Genomics. 2011 Oct 20;12:515. doi: 10.1186/1471-2164-12-515.

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