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Items: 1 to 20 of 126

1.

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.

2.

Optimally choosing PWM motif databases and sequence scanning approaches based on ChIP-seq data.

Dabrowski M, Dojer N, Krystkowiak I, Kaminska B, Wilczynski B.

BMC Bioinformatics. 2015 May 1;16:140. doi: 10.1186/s12859-015-0573-5.

3.

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.

4.

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
5.

A general pairwise interaction model provides an accurate description of in vivo transcription factor binding sites.

Santolini M, Mora T, Hakim V.

PLoS One. 2014 Jun 13;9(6):e99015. doi: 10.1371/journal.pone.0099015. eCollection 2014.

6.

The next generation of transcription factor binding site prediction.

Mathelier A, Wasserman WW.

PLoS Comput Biol. 2013;9(9):e1003214. doi: 10.1371/journal.pcbi.1003214. Epub 2013 Sep 5.

7.

Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data.

Eggeling R, Roos T, Myllymäki P, Grosse I.

BMC Bioinformatics. 2015 Nov 9;16:375. doi: 10.1186/s12859-015-0797-4.

8.

Improving analysis of transcription factor binding sites within ChIP-Seq data based on topological motif enrichment.

Worsley Hunt R, Mathelier A, Del Peso L, Wasserman WW.

BMC Genomics. 2014 Jun 13;15:472. doi: 10.1186/1471-2164-15-472.

9.

Optimized position weight matrices in prediction of novel putative binding sites for transcription factors in the Drosophila melanogaster genome.

Morozov VY, Ioshikhes IP.

PLoS One. 2013 Aug 6;8(8):e68712. doi: 10.1371/journal.pone.0068712. Print 2013.

10.

A DNA shape-based regulatory score improves position-weight matrix-based recognition of transcription factor binding sites.

Yang J, Ramsey SA.

Bioinformatics. 2015 Nov 1;31(21):3445-50. doi: 10.1093/bioinformatics/btv391. Epub 2015 Jun 30.

11.

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.

12.

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.

13.

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
14.

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.

15.

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.

16.

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.

17.

LASAGNA: a novel algorithm for transcription factor binding site alignment.

Lee C, Huang CH.

BMC Bioinformatics. 2013 Mar 24;14:108. doi: 10.1186/1471-2105-14-108.

18.

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.

19.

DISPARE: DIScriminative PAttern REfinement for Position Weight Matrices.

da Piedade I, Tang MH, Elemento O.

BMC Bioinformatics. 2009 Nov 26;10:388. doi: 10.1186/1471-2105-10-388.

20.

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.

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