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Journal Abstract Search

332 related items for PubMed ID: 25192742

  • 1. SignalSpider: probabilistic pattern discovery on multiple normalized ChIP-Seq signal profiles.
    Wong KC, Li Y, Peng C, Zhang Z.
    Bioinformatics; 2015 Jan 01; 31(1):17-24. PubMed ID: 25192742
    [Abstract] [Full Text] [Related]

  • 2. Probabilistic Inference on Multiple Normalized Signal Profiles from Next Generation Sequencing: Transcription Factor Binding Sites.
    Wong KC, Peng C, Li Y.
    IEEE/ACM Trans Comput Biol Bioinform; 2015 Jan 01; 12(6):1416-28. PubMed ID: 26671811
    [Abstract] [Full Text] [Related]

  • 3. iTAR: a web server for identifying target genes of transcription factors using ChIP-seq or ChIP-chip data.
    Yang CC, Andrews EH, Chen MH, Wang WY, Chen JJ, Gerstein M, Liu CC, Cheng C.
    BMC Genomics; 2016 Aug 12; 17(1):632. PubMed ID: 27519564
    [Abstract] [Full Text] [Related]

  • 4. A novel statistical method for quantitative comparison of multiple ChIP-seq datasets.
    Chen L, Wang C, Qin ZS, Wu H.
    Bioinformatics; 2015 Jun 15; 31(12):1889-96. PubMed ID: 25682068
    [Abstract] [Full Text] [Related]

  • 5. Using combined evidence from replicates to evaluate ChIP-seq peaks.
    Jalili V, Matteucci M, Masseroli M, Morelli MJ.
    Bioinformatics; 2015 Sep 01; 31(17):2761-9. PubMed ID: 25957351
    [Abstract] [Full Text] [Related]

  • 6. Seten: a tool for systematic identification and comparison of processes, phenotypes, and diseases associated with RNA-binding proteins from condition-specific CLIP-seq profiles.
    Budak G, Srivastava R, Janga SC.
    RNA; 2017 Jun 01; 23(6):836-846. PubMed ID: 28336542
    [Abstract] [Full Text] [Related]

  • 7. Chromatin analyses of Zymoseptoria tritici: Methods for chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq).
    Soyer JL, Möller M, Schotanus K, Connolly LR, Galazka JM, Freitag M, Stukenbrock EH.
    Fungal Genet Biol; 2015 Jun 01; 79():63-70. PubMed ID: 25857259
    [Abstract] [Full Text] [Related]

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  • 9. ChIPulate: A comprehensive ChIP-seq simulation pipeline.
    Datta V, Hannenhalli S, Siddharthan R.
    PLoS Comput Biol; 2019 Mar 01; 15(3):e1006921. PubMed ID: 30897079
    [Abstract] [Full Text] [Related]

  • 10. Application of topic models to a compendium of ChIP-Seq datasets uncovers recurrent transcriptional regulatory modules.
    Yang G, Ma A, Qin ZS, Chen L.
    Bioinformatics; 2020 Apr 15; 36(8):2352-2358. PubMed ID: 31899481
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  • 12. ChIP-BIT: Bayesian inference of target genes using a novel joint probabilistic model of ChIP-seq profiles.
    Chen X, Jung JG, Shajahan-Haq AN, Clarke R, Shih IeM, Wang Y, Magnani L, Wang TL, Xuan J.
    Nucleic Acids Res; 2016 Apr 20; 44(7):e65. PubMed ID: 26704972
    [Abstract] [Full Text] [Related]

  • 13. De novo prediction of cis-regulatory elements and modules through integrative analysis of a large number of ChIP datasets.
    Niu M, Tabari ES, Su Z.
    BMC Genomics; 2014 Dec 02; 15():1047. PubMed ID: 25442502
    [Abstract] [Full Text] [Related]

  • 14. Identification of Candidate Functional Elements in the Genome from ChIP-seq Data.
    Marinov GK.
    Methods Mol Biol; 2017 Dec 02; 1543():19-43. PubMed ID: 28349420
    [Abstract] [Full Text] [Related]

  • 15. Defining bacterial regulons using ChIP-seq.
    Myers KS, Park DM, Beauchene NA, Kiley PJ.
    Methods; 2015 Sep 15; 86():80-8. PubMed ID: 26032817
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  • 17. RSAT::Plants: Motif Discovery in ChIP-Seq Peaks of Plant Genomes.
    Castro-Mondragon JA, Rioualen C, Contreras-Moreira B, van Helden J.
    Methods Mol Biol; 2016 Sep 15; 1482():297-322. PubMed ID: 27557775
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  • 19. Cell-type specificity of ChIP-predicted transcription factor binding sites.
    Håndstad T, Rye M, Močnik R, Drabløs F, Sætrom P.
    BMC Genomics; 2012 Aug 03; 13():372. PubMed ID: 22863112
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