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 *

159 related articles for article (PubMed ID: 30791920)

  • 1. 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]  

  • 2. Identification of transcription factor binding sites using ATAC-seq.
    Li Z; Schulz MH; Look T; Begemann M; Zenke M; Costa IG
    Genome Biol; 2019 Feb; 20(1):45. PubMed ID: 30808370
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Explicit DNase sequence bias modeling enables high-resolution transcription factor footprint detection.
    Yardımcı GG; Frank CL; Crawford GE; Ohler U
    Nucleic Acids Res; 2014 Oct; 42(19):11865-78. PubMed ID: 25294828
    [TBL] [Abstract][Full Text] [Related]  

  • 4. XL-DNase-Seq: Footprinting Analysis of Dynamic Transcription Factors.
    Oh KS; Aqdas M; Sung MH
    Methods Mol Biol; 2024; 2846():243-261. PubMed ID: 39141240
    [TBL] [Abstract][Full Text] [Related]  

  • 5. ATAC-seq with unique molecular identifiers improves quantification and footprinting.
    Zhu T; Liao K; Zhou R; Xia C; Xie W
    Commun Biol; 2020 Nov; 3(1):675. PubMed ID: 33188264
    [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. Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data across 27 Tissue Types.
    Funk CC; Casella AM; Jung S; Richards MA; Rodriguez A; Shannon P; Donovan-Maiye R; Heavner B; Chard K; Xiao Y; Glusman G; Ertekin-Taner N; Golde TE; Toga A; Hood L; Van Horn JD; Kesselman C; Foster I; Madduri R; Price ND; Ament SA
    Cell Rep; 2020 Aug; 32(7):108029. PubMed ID: 32814038
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Refined DNase-seq protocol and data analysis reveals intrinsic bias in transcription factor footprint identification.
    He HH; Meyer CA; Hu SS; Chen MW; Zang C; Liu Y; Rao PK; Fei T; Xu H; Long H; Liu XS; Brown M
    Nat Methods; 2014 Jan; 11(1):73-78. PubMed ID: 24317252
    [TBL] [Abstract][Full Text] [Related]  

  • 9. maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks.
    Cazares TA; Rizvi FW; Iyer B; Chen X; Kotliar M; Bejjani AT; Wayman JA; Donmez O; Wronowski B; Parameswaran S; Kottyan LC; Barski A; Weirauch MT; Prasath VBS; Miraldi ER
    PLoS Comput Biol; 2023 Jan; 19(1):e1010863. PubMed ID: 36719906
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Genome-wide MNase hypersensitivity assay unveils distinct classes of open chromatin associated with H3K27me3 and DNA methylation in Arabidopsis thaliana.
    Zhao H; Zhang W; Zhang T; Lin Y; Hu Y; Fang C; Jiang J
    Genome Biol; 2020 Feb; 21(1):24. PubMed ID: 32014062
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data.
    Schultheis H; Bentsen M; Heger V; Looso M
    Sci Rep; 2024 Apr; 14(1):9275. PubMed ID: 38654130
    [TBL] [Abstract][Full Text] [Related]  

  • 12. MMGAT: a graph attention network framework for ATAC-seq motifs finding.
    Wu X; Hou W; Zhao Z; Huang L; Sheng N; Yang Q; Zhang S; Wang Y
    BMC Bioinformatics; 2024 Apr; 25(1):158. PubMed ID: 38643066
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Protein-DNA binding in high-resolution.
    Mahony S; Pugh BF
    Crit Rev Biochem Mol Biol; 2015; 50(4):269-83. PubMed ID: 26038153
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Optimization of ATAC-seq in wheat seedling roots using INTACT-isolated nuclei.
    Debernardi JM; Burguener G; Bubb K; Liu Q; Queitsch C; Dubcovsky J
    BMC Plant Biol; 2023 May; 23(1):270. PubMed ID: 37211599
    [TBL] [Abstract][Full Text] [Related]  

  • 15. An expansive human regulatory lexicon encoded in transcription factor footprints.
    Neph S; Vierstra J; Stergachis AB; Reynolds AP; Haugen E; Vernot B; Thurman RE; John S; Sandstrom R; Johnson AK; Maurano MT; Humbert R; Rynes E; Wang H; Vong S; Lee K; Bates D; Diegel M; Roach V; Dunn D; Neri J; Schafer A; Hansen RS; Kutyavin T; Giste E; Weaver M; Canfield T; Sabo P; Zhang M; Balasundaram G; Byron R; MacCoss MJ; Akey JM; Bender MA; Groudine M; Kaul R; Stamatoyannopoulos JA
    Nature; 2012 Sep; 489(7414):83-90. PubMed ID: 22955618
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Correction of transposase sequence bias in ATAC-seq data with rule ensemble modeling.
    Wolpe JB; Martins AL; Guertin MJ
    NAR Genom Bioinform; 2023 Jun; 5(2):lqad054. PubMed ID: 37274120
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Cistrome Data Browser: expanded datasets and new tools for gene regulatory analysis.
    Zheng R; Wan C; Mei S; Qin Q; Wu Q; Sun H; Chen CH; Brown M; Zhang X; Meyer CA; Liu XS
    Nucleic Acids Res; 2019 Jan; 47(D1):D729-D735. PubMed ID: 30462313
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Global reference mapping of human transcription factor footprints.
    Vierstra J; Lazar J; Sandstrom R; Halow J; Lee K; Bates D; Diegel M; Dunn D; Neri F; Haugen E; Rynes E; Reynolds A; Nelson J; Johnson A; Frerker M; Buckley M; Kaul R; Meuleman W; Stamatoyannopoulos JA
    Nature; 2020 Jul; 583(7818):729-736. PubMed ID: 32728250
    [TBL] [Abstract][Full Text] [Related]  

  • 19. scATAC-seq preprocessing and imputation evaluation system for visualization, clustering and digital footprinting.
    Akhtyamov P; Shaheen L; Raevskiy M; Stupnikov A; Medvedeva YA
    Brief Bioinform; 2023 Nov; 25(1):. PubMed ID: 38084919
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Normalization benchmark of ATAC-seq datasets shows the importance of accounting for GC-content effects.
    Van den Berge K; Chou HJ; Roux de Bézieux H; Street K; Risso D; Ngai J; Dudoit S
    Cell Rep Methods; 2022 Nov; 2(11):100321. PubMed ID: 36452861
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.