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Items: 1 to 20 of 172

1.

A clustering approach for identification of enriched domains from histone modification ChIP-Seq data.

Zang C, Schones DE, Zeng C, Cui K, Zhao K, Peng W.

Bioinformatics. 2009 Aug 1;25(15):1952-8. doi: 10.1093/bioinformatics/btp340. Epub 2009 Jun 8.

2.
3.

QChIPat: a quantitative method to identify distinct binding patterns for two biological ChIP-seq samples in different experimental conditions.

Liu B, Yi J, Sv A, Lan X, Ma Y, Huang TH, Leone G, Jin VX.

BMC Genomics. 2013;14 Suppl 8:S3. doi: 10.1186/1471-2164-14-S8-S3. Epub 2013 Dec 9.

4.

SignalSpider: probabilistic pattern discovery on multiple normalized ChIP-Seq signal profiles.

Wong KC, Li Y, Peng C, Zhang Z.

Bioinformatics. 2015 Jan 1;31(1):17-24. doi: 10.1093/bioinformatics/btu604. Epub 2014 Sep 5.

5.

A novel statistical method for quantitative comparison of multiple ChIP-seq datasets.

Chen L, Wang C, Qin ZS, Wu H.

Bioinformatics. 2015 Jun 15;31(12):1889-96. doi: 10.1093/bioinformatics/btv094. Epub 2015 Feb 13.

PMID:
25682068
6.

Identifying differential histone modification sites from ChIP-seq data.

Xu H, Sung WK.

Methods Mol Biol. 2012;802:293-303. doi: 10.1007/978-1-61779-400-1_19.

PMID:
22130888
7.

HPeak: an HMM-based algorithm for defining read-enriched regions in ChIP-Seq data.

Qin ZS, Yu J, Shen J, Maher CA, Hu M, Kalyana-Sundaram S, Yu J, Chinnaiyan AM.

BMC Bioinformatics. 2010 Jul 2;11:369. doi: 10.1186/1471-2105-11-369.

8.

A signal-noise model for significance analysis of ChIP-seq with negative control.

Xu H, Handoko L, Wei X, Ye C, Sheng J, Wei CL, Lin F, Sung WK.

Bioinformatics. 2010 May 1;26(9):1199-204. doi: 10.1093/bioinformatics/btq128. Epub 2010 Apr 5.

9.

Identifying positioned nucleosomes with epigenetic marks in human from ChIP-Seq.

Zhang Y, Shin H, Song JS, Lei Y, Liu XS.

BMC Genomics. 2008 Nov 13;9:537. doi: 10.1186/1471-2164-9-537.

10.

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.

11.

Clustered ChIP-Seq-defined transcription factor binding sites and histone modifications map distinct classes of regulatory elements.

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

BMC Biol. 2011 Nov 24;9:80. doi: 10.1186/1741-7007-9-80.

12.

Using combined evidence from replicates to evaluate ChIP-seq peaks.

Jalili V, Matteucci M, Masseroli M, Morelli MJ.

Bioinformatics. 2015 Sep 1;31(17):2761-9. doi: 10.1093/bioinformatics/btv293. Epub 2015 May 7.

PMID:
25957351
13.

Mapping protein-DNA interactions using ChIP-sequencing.

Massie CE, Mills IG.

Methods Mol Biol. 2012;809:157-73. doi: 10.1007/978-1-61779-376-9_11.

PMID:
22113275
14.

MMDiff: quantitative testing for shape changes in ChIP-Seq data sets.

Schweikert G, Cseke B, Clouaire T, Bird A, Sanguinetti G.

BMC Genomics. 2013 Nov 24;14:826. doi: 10.1186/1471-2164-14-826.

15.

Distinct features of lamin A-interacting chromatin domains mapped by ChIP-sequencing from sonicated or micrococcal nuclease-digested chromatin.

Lund EG, Duband-Goulet I, Oldenburg A, Buendia B, Collas P.

Nucleus. 2015;6(1):30-9. doi: 10.4161/19491034.2014.990855.

16.

A global clustering algorithm to identify long intergenic non-coding RNA--with applications in mouse macrophages.

Garmire LX, Garmire DG, Huang W, Yao J, Glass CK, Subramaniam S.

PLoS One. 2011;6(9):e24051. doi: 10.1371/journal.pone.0024051. Epub 2011 Sep 30.

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.

Genome-wide localization of protein-DNA binding and histone modification by a Bayesian change-point method with ChIP-seq data.

Xing H, Mo Y, Liao W, Zhang MQ.

PLoS Comput Biol. 2012;8(7):e1002613. doi: 10.1371/journal.pcbi.1002613. Epub 2012 Jul 26.

19.

Empirical methods for controlling false positives and estimating confidence in ChIP-Seq peaks.

Nix DA, Courdy SJ, Boucher KM.

BMC Bioinformatics. 2008 Dec 5;9:523. doi: 10.1186/1471-2105-9-523.

20.

Broad-Enrich: functional interpretation of large sets of broad genomic regions.

Cavalcante RG, Lee C, Welch RP, Patil S, Weymouth T, Scott LJ, Sartor MA.

Bioinformatics. 2014 Sep 1;30(17):i393-400. doi: 10.1093/bioinformatics/btu444.

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