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Items: 1 to 20 of 112

1.

Comparison of four ChIP-Seq analytical algorithms using rice endosperm H3K27 trimethylation profiling data.

Malone BM, Tan F, Bridges SM, Peng Z.

PLoS One. 2011;6(9):e25260. doi: 10.1371/journal.pone.0025260. Epub 2011 Sep 30.

2.

Evaluation of algorithm performance in ChIP-seq peak detection.

Wilbanks EG, Facciotti MT.

PLoS One. 2010 Jul 8;5(7):e11471. doi: 10.1371/journal.pone.0011471.

3.

Identifying ChIP-seq enrichment using MACS.

Feng J, Liu T, Qin B, Zhang Y, Liu XS.

Nat Protoc. 2012 Sep;7(9):1728-40. doi: 10.1038/nprot.2012.101. Epub 2012 Aug 30.

4.

ChIP-chip versus ChIP-seq: lessons for experimental design and data analysis.

Ho JW, Bishop E, Karchenko PV, Nègre N, White KP, Park PJ.

BMC Genomics. 2011 Feb 28;12:134. doi: 10.1186/1471-2164-12-134.

5.

Transcriptomic analysis of rice (Oryza sativa) endosperm using the RNA-Seq technique.

Gao Y, Xu H, Shen Y, Wang J.

Plant Mol Biol. 2013 Mar;81(4-5):363-78. doi: 10.1007/s11103-013-0009-4. Epub 2013 Jan 16.

PMID:
23322175
6.

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.

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.

Shape-based peak identification for ChIP-Seq.

Hower V, Evans SN, Pachter L.

BMC Bioinformatics. 2011 Jan 12;12:15. doi: 10.1186/1471-2105-12-15.

9.

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.

10.

Combining multiple ChIP-seq peak detection systems using combinatorial fusion.

Schweikert C, Brown S, Tang Z, Smith PR, Hsu DF.

BMC Genomics. 2012;13 Suppl 8:S12. doi: 10.1186/1471-2164-13-S8-S12. Epub 2012 Dec 17.

11.
12.

ChIPseqR: analysis of ChIP-seq experiments.

Humburg P, Helliwell CA, Bulger D, Stone G.

BMC Bioinformatics. 2011 Jan 31;12:39. doi: 10.1186/1471-2105-12-39.

13.

Sequential ChIP-bisulfite sequencing enables direct genome-scale investigation of chromatin and DNA methylation cross-talk.

Brinkman AB, Gu H, Bartels SJ, Zhang Y, Matarese F, Simmer F, Marks H, Bock C, Gnirke A, Meissner A, Stunnenberg HG.

Genome Res. 2012 Jun;22(6):1128-38. doi: 10.1101/gr.133728.111. Epub 2012 Mar 30.

14.
15.

CMT: a constrained multi-level thresholding approach for ChIP-Seq data analysis.

Rezaeian I, Rueda L.

PLoS One. 2014 Apr 15;9(4):e93873. doi: 10.1371/journal.pone.0093873. eCollection 2014.

16.

Genome-wide analysis of histone modifications: H3K4me2, H3K4me3, H3K9ac, and H3K27ac in Oryza sativa L. Japonica.

Du Z, Li H, Wei Q, Zhao X, Wang C, Zhu Q, Yi X, Xu W, Liu XS, Jin W, Su Z.

Mol Plant. 2013 Sep;6(5):1463-72. doi: 10.1093/mp/sst018. Epub 2013 Jan 25.

17.

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.

18.

A signal processing approach for enriched region detection in RNA polymerase II ChIP-seq data.

Han Z, Tian L, Pécot T, Huang T, Machiraju R, Huang K.

BMC Bioinformatics. 2012 Mar 13;13 Suppl 2:S2. doi: 10.1186/1471-2105-13-S2-S2.

19.

Is this the right normalization? A diagnostic tool for ChIP-seq normalization.

Angelini C, Heller R, Volkinshtein R, Yekutieli D.

BMC Bioinformatics. 2015 May 9;16:150. doi: 10.1186/s12859-015-0579-z.

20.

Genome-wide ChIP-seq mapping and analysis reveal butyrate-induced acetylation of H3K9 and H3K27 correlated with transcription activity in bovine cells.

Shin JH, Li RW, Gao Y, Baldwin R 6th, Li CJ.

Funct Integr Genomics. 2012 Mar;12(1):119-30. doi: 10.1007/s10142-012-0263-6. Epub 2012 Jan 17.

PMID:
22249597
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