183 related articles for article (PubMed ID: 35609992)
1. Profiling the quantitative occupancy of myriad transcription factors across conditions by modeling chromatin accessibility data.
Luo K; Zhong J; Safi A; Hong LK; Tewari AK; Song L; Reddy TE; Ma L; Crawford GE; Hartemink AJ
Genome Res; 2022 Jun; 32(6):1183-1198. PubMed ID: 35609992
[TBL] [Abstract][Full Text] [Related]
2. Predicting transcription factor site occupancy using DNA sequence intrinsic and cell-type specific chromatin features.
Kumar S; Bucher P
BMC Bioinformatics; 2016 Jan; 17 Suppl 1(Suppl 1):4. PubMed ID: 26818008
[TBL] [Abstract][Full Text] [Related]
3. RoboCOP: jointly computing chromatin occupancy profiles for numerous factors from chromatin accessibility data.
Mitra S; Zhong J; Tran TQ; MacAlpine DM; Hartemink AJ
Nucleic Acids Res; 2021 Aug; 49(14):7925-7938. PubMed ID: 34255854
[TBL] [Abstract][Full Text] [Related]
4. 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; 18(1):355. PubMed ID: 28750606
[TBL] [Abstract][Full Text] [Related]
5. Modeling co-occupancy of transcription factors using chromatin features.
Liu L; Zhao W; Zhou X
Nucleic Acids Res; 2016 Mar; 44(5):e49. PubMed ID: 26590261
[TBL] [Abstract][Full Text] [Related]
6. RoboCOP: Multivariate State Space Model Integrating Epigenomic Accessibility Data to Elucidate Genome-Wide Chromatin Occupancy.
Mitra S; Zhong J; MacAlpine DM; Hartemink AJ
Res Comput Mol Biol; 2020 May; 12074():136-151. PubMed ID: 34386808
[TBL] [Abstract][Full Text] [Related]
7. Effects of sequence variation on differential allelic transcription factor occupancy and gene expression.
Reddy TE; Gertz J; Pauli F; Kucera KS; Varley KE; Newberry KM; Marinov GK; Mortazavi A; Williams BA; Song L; Crawford GE; Wold B; Willard HF; Myers RM
Genome Res; 2012 May; 22(5):860-9. PubMed ID: 22300769
[TBL] [Abstract][Full Text] [Related]
8. Cooperative binding of transcription factors in the human genome.
Nie Y; Shu C; Sun X
Genomics; 2020 Sep; 112(5):3427-3434. PubMed ID: 32574834
[TBL] [Abstract][Full Text] [Related]
9. Identification of mammalian transcription factors that bind to inaccessible chromatin.
Pop RT; Pisante A; Nagy D; Martin PCN; Mikheeva LA; Hayat A; Ficz G; Zabet NR
Nucleic Acids Res; 2023 Sep; 51(16):8480-8495. PubMed ID: 37486787
[TBL] [Abstract][Full Text] [Related]
10. Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape.
Sherwood RI; Hashimoto T; O'Donnell CW; Lewis S; Barkal AA; van Hoff JP; Karun V; Jaakkola T; Gifford DK
Nat Biotechnol; 2014 Feb; 32(2):171-178. PubMed ID: 24441470
[TBL] [Abstract][Full Text] [Related]
11. 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(Suppl 1):S1. PubMed ID: 24564277
[TBL] [Abstract][Full Text] [Related]
12. BinDNase: a discriminatory approach for transcription factor binding prediction using DNase I hypersensitivity data.
Kähärä J; Lähdesmäki H
Bioinformatics; 2015 Sep; 31(17):2852-9. PubMed ID: 25957350
[TBL] [Abstract][Full Text] [Related]
13. Exploiting genetic variation to uncover rules of transcription factor binding and chromatin accessibility.
Behera V; Evans P; Face CJ; Hamagami N; Sankaranarayanan L; Keller CA; Giardine B; Tan K; Hardison RC; Shi J; Blobel GA
Nat Commun; 2018 Feb; 9(1):782. PubMed ID: 29472540
[TBL] [Abstract][Full Text] [Related]
14. Dynamic motif occupancy (DynaMO) analysis identifies transcription factors and their binding sites driving dynamic biological processes.
Kuang Z; Ji Z; Boeke JD; Ji H
Nucleic Acids Res; 2018 Jan; 46(1):e2. PubMed ID: 29325176
[TBL] [Abstract][Full Text] [Related]
15. A map of direct TF-DNA interactions in the human genome.
Gheorghe M; Sandve GK; Khan A; Chèneby J; Ballester B; Mathelier A
Nucleic Acids Res; 2019 Feb; 47(4):e21. PubMed ID: 30517703
[TBL] [Abstract][Full Text] [Related]
16. XL-DNase-seq: improved footprinting of dynamic transcription factors.
Oh KS; Ha J; Baek S; Sung MH
Epigenetics Chromatin; 2019 Jun; 12(1):30. PubMed ID: 31164146
[TBL] [Abstract][Full Text] [Related]
17. Profiling of chromatin accessibility identifies transcription factor binding sites across the genome of Aspergillus species.
Huang L; Li X; Dong L; Wang B; Pan L
BMC Biol; 2021 Sep; 19(1):189. PubMed ID: 34488759
[TBL] [Abstract][Full Text] [Related]
18. ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements.
Chen X; Neuwald AF; Hilakivi-Clarke L; Clarke R; Xuan J
PLoS Comput Biol; 2021 Jul; 17(7):e1009203. PubMed ID: 34292930
[TBL] [Abstract][Full Text] [Related]
19. 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. PubMed ID: 22955984
[TBL] [Abstract][Full Text] [Related]
20. Contribution of Sequence Motif, Chromatin State, and DNA Structure Features to Predictive Models of Transcription Factor Binding in Yeast.
Tsai ZT; Shiu SH; Tsai HK
PLoS Comput Biol; 2015 Aug; 11(8):e1004418. PubMed ID: 26291518
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]