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Items: 1 to 20 of 149

1.

Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

Schmidt F, Gasparoni N, Gasparoni G, Gianmoena K, Cadenas C, Polansky JK, Ebert P, Nordström K, Barann M, Sinha A, Fröhler S, Xiong J, Dehghani Amirabad A, Behjati Ardakani F, Hutter B, Zipprich G, Felder B, Eils J, Brors B, Chen W, Hengstler JG, Hamann A, Lengauer T, Rosenstiel P, Walter J, Schulz MH.

Nucleic Acids Res. 2017 Jan 9;45(1):54-66. doi: 10.1093/nar/gkw1061. Epub 2016 Nov 29.

2.

Cell-type specificity of ChIP-predicted transcription factor binding sites.

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

BMC Genomics. 2012 Aug 3;13:372. doi: 10.1186/1471-2164-13-372.

3.

Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features.

Kumar S, Bucher P.

BMC Bioinformatics. 2016 Jan 11;17 Suppl 1:4. doi: 10.1186/s12859-015-0846-z.

4.

BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data.

Kähärä J, Lähdesmäki H.

Bioinformatics. 2015 Sep 1;31(17):2852-9. doi: 10.1093/bioinformatics/btv294. Epub 2015 May 7.

PMID:
25957350
5.

Assessing the model transferability for prediction of transcription factor binding sites based on chromatin accessibility.

Liu S, Zibetti C, Wan J, Wang G, Blackshaw S, Qian J.

BMC Bioinformatics. 2017 Jul 27;18(1):355. doi: 10.1186/s12859-017-1769-7.

6.

Cell-type and transcription factor specific enrichment of transcriptional cofactor motifs in ENCODE ChIP-seq data.

Goi C, Little P, Xie C.

BMC Genomics. 2013;14 Suppl 5:S2. doi: 10.1186/1471-2164-14-S5-S2. Epub 2013 Oct 16.

7.

Sequence and chromatin determinants of cell-type-specific transcription factor binding.

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

Genome Res. 2012 Sep;22(9):1723-34. doi: 10.1101/gr.127712.111.

8.

Modeling co-occupancy of transcription factors using chromatin features.

Liu L, Zhao W, Zhou X.

Nucleic Acids Res. 2016 Mar 18;44(5):e49. doi: 10.1093/nar/gkv1281. Epub 2015 Nov 20.

9.

TICA: Transcriptional Interaction and Coregulation Analyzer.

Perna S, Pinoli P, Ceri S, Wong L.

Genomics Proteomics Bioinformatics. 2018 Oct;16(5):342-353. doi: 10.1016/j.gpb.2018.05.004. Epub 2018 Dec 19.

10.

MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets.

Shao Z, Zhang Y, Yuan GC, Orkin SH, Waxman DJ.

Genome Biol. 2012 Mar 16;13(3):R16. doi: 10.1186/gb-2012-13-3-r16.

11.

Statistics of protein-DNA binding and the total number of binding sites for a transcription factor in the mammalian genome.

Kuznetsov VA, Singh O, Jenjaroenpun P.

BMC Genomics. 2010 Feb 10;11 Suppl 1:S12. doi: 10.1186/1471-2164-11-S1-S12.

12.

Imputation for transcription factor binding predictions based on deep learning.

Qin Q, Feng J.

PLoS Comput Biol. 2017 Feb 24;13(2):e1005403. doi: 10.1371/journal.pcbi.1005403. eCollection 2017 Feb.

13.

MixChIP: a probabilistic method for cell type specific protein-DNA binding analysis.

Rautio S, Lähdesmäki H.

BMC Bioinformatics. 2015 Dec 24;16:413. doi: 10.1186/s12859-015-0834-3.

14.

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.

15.

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.

16.

Enhancer identification in mouse embryonic stem cells using integrative modeling of chromatin and genomic features.

Chen CY, Morris Q, Mitchell JA.

BMC Genomics. 2012 Apr 26;13:152. doi: 10.1186/1471-2164-13-152.

17.

Genome-wide in silico prediction of gene expression.

McLeay RC, Lesluyes T, Cuellar Partida G, Bailey TL.

Bioinformatics. 2012 Nov 1;28(21):2789-96. doi: 10.1093/bioinformatics/bts529. Epub 2012 Sep 6.

18.

Revealing transcription factor and histone modification co-localization and dynamics across cell lines by integrating ChIP-seq and RNA-seq data.

Zhang L, Xue G, Liu J, Li Q, Wang Y.

BMC Genomics. 2018 Dec 31;19(Suppl 10):914. doi: 10.1186/s12864-018-5278-5.

19.

An improved ChIP-seq peak detection system for simultaneously identifying post-translational modified transcription factors by combinatorial fusion, using SUMOylation as an example.

Cheng CY, Chu CH, Hsu HW, Hsu FR, Tang CY, Wang WC, Kung HJ, Chang PC.

BMC Genomics. 2014;15 Suppl 1:S1. doi: 10.1186/1471-2164-15-S1-S1. Epub 2014 Jan 24.

20.

Mocap: large-scale inference of transcription factor binding sites from chromatin accessibility.

Chen X, Yu B, Carriero N, Silva C, Bonneau R.

Nucleic Acids Res. 2017 May 5;45(8):4315-4329. doi: 10.1093/nar/gkx174.

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