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Items: 1 to 20 of 86

1.

GAPWM: a genetic algorithm method for optimizing a position weight matrix.

Li L, Liang Y, Bass RL.

Bioinformatics. 2007 May 15;23(10):1188-94. Epub 2007 Mar 6.

PMID:
17341493
2.

fdrMotif: identifying cis-elements by an EM algorithm coupled with false discovery rate control.

Li L, Bass RL, Liang Y.

Bioinformatics. 2008 Mar 1;24(5):629-36. doi: 10.1093/bioinformatics/btn009. Epub 2008 Feb 22.

3.

A Fast Cluster Motif Finding Algorithm for ChIP-Seq Data Sets.

Zhang Y, Wang P.

Biomed Res Int. 2015;2015:218068. doi: 10.1155/2015/218068. Epub 2015 Jul 5.

4.
5.
6.

From binding motifs in ChIP-Seq data to improved models of transcription factor binding sites.

Kulakovskiy I, Levitsky V, Oshchepkov D, Bryzgalov L, Vorontsov I, Makeev V.

J Bioinform Comput Biol. 2013 Feb;11(1):1340004. doi: 10.1142/S0219720013400040. Epub 2013 Jan 16.

PMID:
23427986
7.

Optimizing the GATA-3 position weight matrix to improve the identification of novel binding sites.

Nandi S, Ioshikhes I.

BMC Genomics. 2012 Aug 22;13:416. doi: 10.1186/1471-2164-13-416.

8.

Using ChIPMotifs for de novo motif discovery of OCT4 and ZNF263 based on ChIP-based high-throughput experiments.

Kennedy BA, Lan X, Huang TH, Farnham PJ, Jin VX.

Methods Mol Biol. 2012;802:323-34. doi: 10.1007/978-1-61779-400-1_21.

9.

coMOTIF: a mixture framework for identifying transcription factor and a coregulator motif in ChIP-seq data.

Xu M, Weinberg CR, Umbach DM, Li L.

Bioinformatics. 2011 Oct 1;27(19):2625-32. doi: 10.1093/bioinformatics/btr397. Epub 2011 Jul 19.

10.

Variable structure motifs for transcription factor binding sites.

Reid JE, Evans KJ, Dyer N, Wernisch L, Ott S.

BMC Genomics. 2010 Jan 14;11:30. doi: 10.1186/1471-2164-11-30.

11.

W-AlignACE: an improved Gibbs sampling algorithm based on more accurate position weight matrices learned from sequence and gene expression/ChIP-chip data.

Chen X, Guo L, Fan Z, Jiang T.

Bioinformatics. 2008 May 1;24(9):1121-8. doi: 10.1093/bioinformatics/btn088. Epub 2008 Mar 5.

PMID:
18325926
12.

Application of experimentally verified transcription factor binding sites models for computational analysis of ChIP-Seq data.

Levitsky VG, Kulakovskiy IV, Ershov NI, Oshchepkov DY, Makeev VJ, Hodgman TC, Merkulova TI.

BMC Genomics. 2014 Jan 29;15:80. doi: 10.1186/1471-2164-15-80.

13.

Learning position weight matrices from sequence and expression data.

Chen X, Guo L, Fan Z, Jiang T.

Comput Syst Bioinformatics Conf. 2007;6:249-60.

14.

Differential motif enrichment analysis of paired ChIP-seq experiments.

Lesluyes T, Johnson J, Machanick P, Bailey TL.

BMC Genomics. 2014 Sep 2;15:752. doi: 10.1186/1471-2164-15-752.

15.

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.

16.

Tree-based position weight matrix approach to model transcription factor binding site profiles.

Bi Y, Kim H, Gupta R, Davuluri RV.

PLoS One. 2011;6(9):e24210. doi: 10.1371/journal.pone.0024210. Epub 2011 Sep 2.

17.
18.

MEME-ChIP: motif analysis of large DNA datasets.

Machanick P, Bailey TL.

Bioinformatics. 2011 Jun 15;27(12):1696-7. doi: 10.1093/bioinformatics/btr189. Epub 2011 Apr 12.

19.

DREME: motif discovery in transcription factor ChIP-seq data.

Bailey TL.

Bioinformatics. 2011 Jun 15;27(12):1653-9. doi: 10.1093/bioinformatics/btr261. Epub 2011 May 4.

20.

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