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Items: 1 to 20 of 107

1.

Exhaustive search for over-represented DNA sequence motifs with CisFinder.

Sharov AA, Ko MS.

DNA Res. 2009 Oct;16(5):261-73. doi: 10.1093/dnares/dsp014. Epub 2009 Sep 9.

2.

A new exhaustive method and strategy for finding motifs in ChIP-enriched regions.

Jia C, Carson MB, Wang Y, Lin Y, Lu H.

PLoS One. 2014 Jan 24;9(1):e86044. doi: 10.1371/journal.pone.0086044. eCollection 2014.

3.

FISim: a new similarity measure between transcription factor binding sites based on the fuzzy integral.

Garcia F, Lopez FJ, Cano C, Blanco A.

BMC Bioinformatics. 2009 Jul 20;10:224. doi: 10.1186/1471-2105-10-224.

4.

Identification of Predictive Cis-Regulatory Elements Using a Discriminative Objective Function and a Dynamic Search Space.

Karnik R, Beer MA.

PLoS One. 2015 Oct 14;10(10):e0140557. doi: 10.1371/journal.pone.0140557. eCollection 2015.

5.

Simultaneously learning DNA motif along with its position and sequence rank preferences through expectation maximization algorithm.

Zhang Z, Chang CW, Hugo W, Cheung E, Sung WK.

J Comput Biol. 2013 Mar;20(3):237-48. doi: 10.1089/cmb.2012.0233.

PMID:
23461573
6.

MATLIGN: a motif clustering, comparison and matching tool.

Kankainen M, Löytynoja A.

BMC Bioinformatics. 2007 Jun 8;8:189.

7.

PscanChIP: Finding over-represented transcription factor-binding site motifs and their correlations in sequences from ChIP-Seq experiments.

Zambelli F, Pesole G, Pavesi G.

Nucleic Acids Res. 2013 Jul;41(Web Server issue):W535-43. doi: 10.1093/nar/gkt448. Epub 2013 Jun 7.

8.

Predicting DNA-binding specificities of eukaryotic transcription factors.

Schröder A, Eichner J, Supper J, Eichner J, Wanke D, Henneges C, Zell A.

PLoS One. 2010 Nov 30;5(11):e13876. doi: 10.1371/journal.pone.0013876.

9.

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.

10.

Comprehensive human transcription factor binding site map for combinatory binding motifs discovery.

Müller-Molina AJ, Schöler HR, Araúzo-Bravo MJ.

PLoS One. 2012;7(11):e49086. doi: 10.1371/journal.pone.0049086. Epub 2012 Nov 28.

11.

Identification of context-dependent motifs by contrasting ChIP binding data.

Mason MJ, Plath K, Zhou Q.

Bioinformatics. 2010 Nov 15;26(22):2826-32. doi: 10.1093/bioinformatics/btq546. Epub 2010 Sep 23.

12.

A Monte Carlo-based framework enhances the discovery and interpretation of regulatory sequence motifs.

Seitzer P, Wilbanks EG, Larsen DJ, Facciotti MT.

BMC Bioinformatics. 2012 Nov 27;13:317. doi: 10.1186/1471-2105-13-317.

13.

Identification of co-occurring transcription factor binding sites from DNA sequence using clustered position weight matrices.

Oh YM, Kim JK, Choi S, Yoo JY.

Nucleic Acids Res. 2012 Mar;40(5):e38. doi: 10.1093/nar/gkr1252. Epub 2011 Dec 19.

14.

An intuitionistic approach to scoring DNA sequences against transcription factor binding site motifs.

Garcia-Alcalde F, Blanco A, Shepherd AJ.

BMC Bioinformatics. 2010 Nov 8;11:551. doi: 10.1186/1471-2105-11-551.

15.

Statistical detection of cooperative transcription factors with similarity adjustment.

Pape UJ, Klein H, Vingron M.

Bioinformatics. 2009 Aug 15;25(16):2103-9. doi: 10.1093/bioinformatics/btp143. Epub 2009 Mar 13.

16.

Identification of Pou5f1, Sox2, and Nanog downstream target genes with statistical confidence by applying a novel algorithm to time course microarray and genome-wide chromatin immunoprecipitation data.

Sharov AA, Masui S, Sharova LV, Piao Y, Aiba K, Matoba R, Xin L, Niwa H, Ko MS.

BMC Genomics. 2008 Jun 3;9:269. doi: 10.1186/1471-2164-9-269.

17.

Ab initio identification of putative human transcription factor binding sites by comparative genomics.

Corà D, Herrmann C, Dieterich C, Di Cunto F, Provero P, Caselle M.

BMC Bioinformatics. 2005 May 2;6:110.

18.

Disclosing the crosstalk among DNA methylation, transcription factors, and histone marks in human pluripotent cells through discovery of DNA methylation motifs.

Luu PL, Schöler HR, Araúzo-Bravo MJ.

Genome Res. 2013 Dec;23(12):2013-29. doi: 10.1101/gr.155960.113. Epub 2013 Oct 22.

19.

High resolution models of transcription factor-DNA affinities improve in vitro and in vivo binding predictions.

Agius P, Arvey A, Chang W, Noble WS, Leslie C.

PLoS Comput Biol. 2010 Sep 9;6(9). pii: e1000916. doi: 10.1371/journal.pcbi.1000916.

20.

Theoretical and empirical quality assessment of transcription factor-binding motifs.

Medina-Rivera A, Abreu-Goodger C, Thomas-Chollier M, Salgado H, Collado-Vides J, van Helden J.

Nucleic Acids Res. 2011 Feb;39(3):808-24. doi: 10.1093/nar/gkq710. Epub 2010 Oct 4.

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