These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

142 related articles for article (PubMed ID: 28704389)

  • 1. Differential chromatin profiles partially determine transcription factor binding.
    Chen R; Gifford DK
    PLoS One; 2017; 12(7):e0179411. PubMed ID: 28704389
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Analysis of computational footprinting methods for DNase sequencing experiments.
    Gusmao EG; Allhoff M; Zenke M; Costa IG
    Nat Methods; 2016 Apr; 13(4):303-9. PubMed ID: 26901649
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. DNase I sensitivity QTLs are a major determinant of human expression variation.
    Degner JF; Pai AA; Pique-Regi R; Veyrieras JB; Gaffney DJ; Pickrell JK; De Leon S; Michelini K; Lewellen N; Crawford GE; Stephens M; Gilad Y; Pritchard JK
    Nature; 2012 Feb; 482(7385):390-4. PubMed ID: 22307276
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. Most brain disease-associated and eQTL haplotypes are not located within transcription factor DNase-seq footprints in brain.
    Handel AE; Gallone G; Zameel Cader M; Ponting CP
    Hum Mol Genet; 2017 Jan; 26(1):79-89. PubMed ID: 27798116
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding.
    Zeng H; Hashimoto T; Kang DD; Gifford DK
    Bioinformatics; 2016 Feb; 32(4):490-6. PubMed ID: 26476779
    [TBL] [Abstract][Full Text] [Related]  

  • 9. TRACE: transcription factor footprinting using chromatin accessibility data and DNA sequence.
    Ouyang N; Boyle AP
    Genome Res; 2020 Jul; 30(7):1040-1046. PubMed ID: 32660981
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Genomic methods in profiling DNA accessibility and factor localization.
    Klein DC; Hainer SJ
    Chromosome Res; 2020 Mar; 28(1):69-85. PubMed ID: 31776829
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. DeFCoM: analysis and modeling of transcription factor binding sites using a motif-centric genomic footprinter.
    Quach B; Furey TS
    Bioinformatics; 2017 Apr; 33(7):956-963. PubMed ID: 27993786
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Genome-Scale Analysis of Cell-Specific Regulatory Codes Using Nuclear Enzymes.
    Baek S; Sung MH
    Methods Mol Biol; 2016; 1418():225-40. PubMed ID: 27008018
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Romulus: robust multi-state identification of transcription factor binding sites from DNase-seq data.
    Jankowski A; Tiuryn J; Prabhakar S
    Bioinformatics; 2016 Aug; 32(16):2419-26. PubMed ID: 27153645
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Intrinsic bias estimation for improved analysis of bulk and single-cell chromatin accessibility profiles using SELMA.
    Hu SS; Liu L; Li Q; Ma W; Guertin MJ; Meyer CA; Deng K; Zhang T; Zang C
    Nat Commun; 2022 Sep; 13(1):5533. PubMed ID: 36130957
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Differential analysis of chromatin accessibility and histone modifications for predicting mouse developmental enhancers.
    Fu S; Wang Q; Moore JE; Purcaro MJ; Pratt HE; Fan K; Gu C; Jiang C; Zhu R; Kundaje A; Lu A; Weng Z
    Nucleic Acids Res; 2018 Nov; 46(21):11184-11201. PubMed ID: 30137428
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. MEDEA: analysis of transcription factor binding motifs in accessible chromatin.
    Mariani L; Weinand K; Gisselbrecht SS; Bulyk ML
    Genome Res; 2020 May; 30(5):736-748. PubMed ID: 32424069
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. Reproducible inference of transcription factor footprints in ATAC-seq and DNase-seq datasets using protocol-specific bias modeling.
    Karabacak Calviello A; Hirsekorn A; Wurmus R; Yusuf D; Ohler U
    Genome Biol; 2019 Feb; 20(1):42. PubMed ID: 30791920
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.