Download PDF by Andrew E. Teschendorff: Computational and Statistical Epigenomics

By Andrew E. Teschendorff

ISBN-10: 9401799261

ISBN-13: 9789401799263

ISBN-10: 940179927X

ISBN-13: 9789401799270

This booklet introduces the reader to fashionable computational and statistical instruments for translational epigenomics learn. during the last decade, epigenomics has emerged as a key quarter of molecular biology, epidemiology and genome medication. Epigenomics not just bargains us a deeper figuring out of primary mobile biology, but additionally offers us with the root for a better realizing and administration of advanced ailments. From novel biomarkers for hazard prediction, early detection, analysis and diagnosis of universal ailments, to novel healing techniques, epigenomics is determined to play a key function within the customized drugs of the long run. during this ebook we introduce the reader to a couple of crucial computational and statistical tools for studying epigenomic information, with a unique concentrate on DNA methylation. issues comprise normalization, correction for mobile heterogeneity, batch results, clustering, supervised research and integrative equipment for platforms epigenomics. This booklet can be of curiosity to scholars and researchers in bioinformatics, biostatistics, biologists and clinicians alike.

Dr. Andrew E. Teschendorff is Head of the Computational structures Genomics Lab on the CAS-MPG associate Institute for Computational Biology, Shanghai, China, in addition to an Honorary examine Fellow on the UCL melanoma Institute, collage university London, UK.

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Additional resources for Computational and Statistical Epigenomics

Sample text

Genome Biol. 2008;9(9):R137. Zhuang J, Widschwendter M, Teschendorff AE. A comparison of feature selection and classification methods in DNA methylation studies using the Illumina Infinium platform. BMC Bioinformatics. 2012;13(1):59. Ziller MJ, et al. Charting a dynamic DNA methylation landscape of the human genome. Nature. 2013;500(7463):477–81. Zou J, et al. Epigenome-wide association studies without the need for cell-type composition. Nat Methods. 2014;11(3):309–11. Chapter 2 DNA Methylation and Cell-Type Distribution E.

Quantitative comparison of genome-wide DNA methylation mapping technologies. Nat Biotechnol. 2010;28(10):1106–14. Butcher LM, Beck S. Probe Lasso: A novel method to rope in differentially methylated regions with 450K DNA methylation data. Methods. 2015;72:21–8. 1 Introduction to Data Types in Epigenomics 29 Calo E, Wysocka J. Modification of enhancer chromatin: what, how, and why? Mol Cell. 2013;49(5):825–37. Carroll T, et al. tracktables: build IGV tracks and HTML reports. 0; 2014a. Carroll TS, et al.

Genome Biol. 2012;13(6):R44. 32 F. Marabita et al. Marabita F, et al. An evaluation of analysis pipelines for DNA methylation profiling using the illumina HumanMethylation450 BeadChip platform. Epigenetics. 2013;8(3):333–46. Matys V, et al. TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 2006;34(Database issue):D108–10. Maunakea AK, et al. Conserved role of intragenic DNA methylation in regulating alternative promoters. Nature. 2010;466(7303):253–7.

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Computational and Statistical Epigenomics by Andrew E. Teschendorff

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