BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

213 related articles for article (PubMed ID: 24936875)

  • 1. OccuPeak: ChIP-Seq peak calling based on internal background modelling.
    de Boer BA; van Duijvenboden K; van den Boogaard M; Christoffels VM; Barnett P; Ruijter JM
    PLoS One; 2014; 9(6):e99844. PubMed ID: 24936875
    [TBL] [Abstract][Full Text] [Related]  

  • 2. RECAP reveals the true statistical significance of ChIP-seq peak calls.
    Chitpin JG; Awdeh A; Perkins TJ
    Bioinformatics; 2019 Oct; 35(19):3592-3598. PubMed ID: 30824903
    [TBL] [Abstract][Full Text] [Related]  

  • 3. WACS: improving ChIP-seq peak calling by optimally weighting controls.
    Awdeh A; Turcotte M; Perkins TJ
    BMC Bioinformatics; 2021 Feb; 22(1):69. PubMed ID: 33588754
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Is this the right normalization? A diagnostic tool for ChIP-seq normalization.
    Angelini C; Heller R; Volkinshtein R; Yekutieli D
    BMC Bioinformatics; 2015 May; 16():150. PubMed ID: 25957089
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data.
    Teng M; Irizarry RA
    Genome Res; 2017 Nov; 27(11):1930-1938. PubMed ID: 29025895
    [TBL] [Abstract][Full Text] [Related]  

  • 6. A non-parametric peak calling algorithm for DamID-Seq.
    Li R; Hempel LU; Jiang T
    PLoS One; 2015; 10(3):e0117415. PubMed ID: 25785608
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Comparative analysis of commonly used peak calling programs for ChIP-Seq analysis.
    Jeon H; Lee H; Kang B; Jang I; Roh TY
    Genomics Inform; 2020 Dec; 18(4):e42. PubMed ID: 33412758
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Identifying ChIP-seq enrichment using MACS.
    Feng J; Liu T; Qin B; Zhang Y; Liu XS
    Nat Protoc; 2012 Sep; 7(9):1728-40. PubMed ID: 22936215
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Use model-based Analysis of ChIP-Seq (MACS) to analyze short reads generated by sequencing protein-DNA interactions in embryonic stem cells.
    Liu T
    Methods Mol Biol; 2014; 1150():81-95. PubMed ID: 24743991
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A comparison of peak callers used for DNase-Seq data.
    Koohy H; Down TA; Spivakov M; Hubbard T
    PLoS One; 2014; 9(5):e96303. PubMed ID: 24810143
    [TBL] [Abstract][Full Text] [Related]  

  • 11. PePr: a peak-calling prioritization pipeline to identify consistent or differential peaks from replicated ChIP-Seq data.
    Zhang Y; Lin YH; Johnson TD; Rozek LS; Sartor MA
    Bioinformatics; 2014 Sep; 30(18):2568-75. PubMed ID: 24894502
    [TBL] [Abstract][Full Text] [Related]  

  • 12. PeaKDEck: a kernel density estimator-based peak calling program for DNaseI-seq data.
    McCarthy MT; O'Callaghan CA
    Bioinformatics; 2014 May; 30(9):1302-4. PubMed ID: 24407222
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A generalized linear model for peak calling in ChIP-Seq data.
    Xu J; Zhang Y
    J Comput Biol; 2012 Jun; 19(6):826-38. PubMed ID: 22533622
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Theoretical characterisation of strand cross-correlation in ChIP-seq.
    Anzawa H; Yamagata H; Kinoshita K
    BMC Bioinformatics; 2020 Sep; 21(1):417. PubMed ID: 32962634
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Shape-based peak identification for ChIP-Seq.
    Hower V; Evans SN; Pachter L
    BMC Bioinformatics; 2011 Jan; 12():15. PubMed ID: 21226895
    [TBL] [Abstract][Full Text] [Related]  

  • 16. CIPHER: a flexible and extensive workflow platform for integrative next-generation sequencing data analysis and genomic regulatory element prediction.
    Guzman C; D'Orso I
    BMC Bioinformatics; 2017 Aug; 18(1):363. PubMed ID: 28789639
    [TBL] [Abstract][Full Text] [Related]  

  • 17. AREM: aligning short reads from ChIP-sequencing by expectation maximization.
    Newkirk D; Biesinger J; Chon A; Yokomori K; Xie X
    J Comput Biol; 2011 Nov; 18(11):1495-505. PubMed ID: 22035330
    [TBL] [Abstract][Full Text] [Related]  

  • 18. ChIP-chip versus ChIP-seq: lessons for experimental design and data analysis.
    Ho JW; Bishop E; Karchenko PV; Nègre N; White KP; Park PJ
    BMC Genomics; 2011 Feb; 12():134. PubMed ID: 21356108
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Sensitive and robust assessment of ChIP-seq read distribution using a strand-shift profile.
    Nakato R; Shirahige K
    Bioinformatics; 2018 Jul; 34(14):2356-2363. PubMed ID: 29528371
    [TBL] [Abstract][Full Text] [Related]  

  • 20. The Triform algorithm: improved sensitivity and specificity in ChIP-Seq peak finding.
    Kornacker K; Rye MB; Håndstad T; Drabløs F
    BMC Bioinformatics; 2012 Jul; 13():176. PubMed ID: 22827163
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.