Chapter 13 Bibliography | The csaw Book
Description: ChIP-seq Analysis with Windows. But it works on Mac and Linux too!

# Chapter 13 Bibliography

Ballman, K. V., D. E. Grill, A. L. Oberg, and T. M. Therneau. 2004. “Faster cyclic loess: normalizing RNA arrays via linear models.” Bioinformatics 20 (16): 2778–86.

Benjamini, Y., and Y. Hochberg. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” J. R. Stat. Soc. Series B 57: 289–300.

———. 1997. “Multiple Hypotheses Testing with Weights.” Scand. J. Stat. 24: 407–18.

Berger, R. L., and J. C. Hsu. 1996. “Bioequivalence Trials, Intersection-Union Tests and Equivalence Confidence Sets.” Statist. Sci. 11 (4): 283–319.

Bonhoure, N., G. Bounova, D. Bernasconi, V. Praz, F. Lammers, D. Canella, I. M. Willis, et al. 2014. “Quantifying ChIP-seq data: a spiking method providing an internal reference for sample-to-sample normalization.” Genome Res. 24 (7): 1157–68.

Bourgon, R., R. Gentleman, and W. Huber. 2010. “Independent filtering increases detection power for high-throughput experiments.” Proc. Natl. Acad. Sci. U.S.A. 107 (21): 9546–51.

Chen, Y., A. T. L. Lun, and G. K. Smyth. 2014. “Differential Expression Analysis of Complex RNA-Seq Experiments Using EdgeR.” In Statistical Analysis of Next Generation Sequence Data, edited by S. Datta and D. S. Nettleton. New York: Springer.

Consortium, ENCODE Project. 2012. “An integrated encyclopedia of DNA elements in the human genome.” Nature 489 (7414): 57–74.

F., Hahne, and Ivanek R. 2016. “Visualizing Genomic Data Using Gviz and Bioconductor.” In Statistical Genomics: Methods and Protocols, edited by Ewy Mathé and Sean Davis, 335–51. New York, NY: Springer New York. https://doi.org/10.1007/978-1-4939-3578-9_16.

Galvis, L. A., A. Z. Holik, K. M. Short, J. Pasquet, A. T. Lun, M. E. Blewitt, I. M. Smyth, M. E. Ritchie, and M. L. Asselin-Labat. 2015. “Repression of Igf1 expression by Ezh2 prevents basal cell differentiation in the developing lung.” Development 142 (8): 1458–69.

Hoffmann, R., T. Seidl, M. Neeb, A. Rolink, and F. Melchers. 2002. “Changes in gene expression profiles in developing B cells of murine bone marrow.” Genome Res. 12 (1): 98–111.

Holik, A. Z., L. A. Galvis, A. T. Lun, M. E. Ritchie, and M. L. Asselin-Labat. 2015. “Transcriptome and H3K27 tri-methylation profiling of Ezh2-deficient lung epithelium.” Genom Data 5 (September): 346–51.

Humburg, P., C. A. Helliwell, D. Bulger, and G. Stone. 2011. “ChIPseqR: analysis of ChIP-seq experiments.” BMC Bioinformatics 12: 39.

Kasper, L. H., C. Qu, J. C. Obenauer, D. J. McGoldrick, and P. K. Brindle. 2014. “Genome-wide and single-cell analyses reveal a context dependent relationship between CBP recruitment and gene expression.” Nucleic Acids Res. 42 (18): 11363–82.

Kharchenko, P. V., M. Y. Tolstorukov, and P. J. Park. 2008. “Design and analysis of ChIP-seq experiments for DNA-binding proteins.” Nat. Biotechnol. 26 (12): 1351–9.

Landt, S. G., G. K. Marinov, A. Kundaje, P. Kheradpour, F. Pauli, S. Batzoglou, B. E. Bernstein, et al. 2012. “ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia.” Genome Res. 22 (9): 1813–31.

Law, C. W., Y. Chen, W. Shi, and G. K. Smyth. 2014. “Voom: precision weights unlock linear model analysis tools for RNA-seq read counts.” Genome Biol. 15 (2): R29.

Liang, K., and S. Keles. 2012. “Detecting differential binding of transcription factors with ChIP-seq.” Bioinformatics 28 (1): 121–22.

Liao, Y., G. K. Smyth, and W. Shi. 2013. “The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote.” Nucleic Acids Res. 41 (10): e108.

Lun, A. T. L., and G. K. Smyth. 2015. “From Reads to Regions: A Bioconductor Workflow to Detect Differential Binding in ChIP-Seq Data.” F1000Research 4.

Lun, A. T., and G. K. Smyth. 2014. “De novo detection of differentially bound regions for ChIP-seq data using peaks and windows: controlling error rates correctly.” Nucleic Acids Res. 42 (11): e95.

———. 2016. “csaw: a Bioconductor package for differential binding analysis of ChIP-seq data using sliding windows.” Nucleic Acids Res. 44 (5): e45.

Lund, S. P., D. Nettleton, D. J. McCarthy, and G. K. Smyth. 2012. “Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates.” Stat. Appl. Genet. Mol. Biol. 11 (5).

McCarthy, D. J., Y. Chen, and G. K. Smyth. 2012. “Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.” Nucleic Acids Res. 40 (10): 4288–97.

Orlando, D. A., M. W. Chen, V. E. Brown, S. Solanki, Y. J. Choi, E. R. Olson, C. C. Fritz, J. E. Bradner, and M. G. Guenther. 2014. “Quantitative ChIP-Seq normalization reveals global modulation of the epigenome.” Cell Rep. 9 (3): 1163–70.

Pal, B., T. Bouras, W. Shi, F. Vaillant, J. M. Sheridan, N. Fu, K. Breslin, et al. 2013. “Global changes in the mammary epigenome are induced by hormonal cues and coordinated by Ezh2.” Cell Rep. 3 (2): 411–26.

Phipson, B., S. Lee, I. J. Majewski, W. S. Alexander, and G. K. Smyth. 2016. “Robust Hyperparameter Estimation Protects Against Hypervariable Genes and Improves Power to Detect Differential Expression.” Ann. Appl. Stat. 10 (2): 946–63.

Podojil, J. R., and V. M. Sanders. 2003. “Selective regulation of mature IgG1 transcription by CD86 and beta 2-adrenergic receptor stimulation.” J. Immunol. 170 (10): 5143–51.

Revilla-I-Domingo, R., I. Bilic, B. Vilagos, H. Tagoh, A. Ebert, I. M. Tamir, L. Smeenk, et al. 2012. “The B-cell identity factor Pax5 regulates distinct transcriptional programmes in early and late B lymphopoiesis.” EMBO J. 31 (14): 3130–46.

Robinson, M. D., D. J. McCarthy, and G. K. Smyth. 2010. “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.” Bioinformatics 26 (1): 139–40.

Robinson, M. D., and A. Oshlack. 2010. “A scaling normalization method for differential expression analysis of RNA-seq data.” Genome Biol. 11 (3): R25.

Robinson, M. D., and G. K. Smyth. 2008. “Small-sample estimation of negative binomial dispersion, with applications to SAGE data.” Biostatistics 9 (2): 321–32.

Ross-Innes, C. S., R. Stark, A. E. Teschendorff, K. A. Holmes, H. R. Ali, M. J. Dunning, G. D. Brown, et al. 2012. “Differential oestrogen receptor binding is associated with clinical outcome in breast cancer.” Nature 481 (7381): 389–93.

Simes, R. J. 1986. “An Improved Bonferroni Procedure for Multiple Tests of Significance.” Biometrika 73 (3): 751–54.

Smyth, G. K. 2004. “Linear models and empirical bayes methods for assessing differential expression in microarray experiments.” Stat. Appl. Genet. Mol. Biol. 3: Article 3.

Tiwari, V. K., M. B. Stadler, C. Wirbelauer, R. Paro, D. Schubeler, and C. Beisel. 2012. “A chromatin-modifying function of JNK during stem cell differentiation.” Nat. Genet. 44 (1): 94–100.

Zhang, Y., T. Liu, C. A. Meyer, J. Eeckhoute, D. S. Johnson, B. E. Bernstein, C. Nusbaum, et al. 2008. “Model-based analysis of ChIP-Seq (MACS).” Genome Biol. 9 (9): R137.

Zhu, L. J., C. Gazin, N. D. Lawson, H. Pages, S. M. Lin, D. S. Lapointe, and M. R. Green. 2010. “ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data.” BMC Bioinformatics 11: 237.