BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

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]
    of 10.