Epigenomics, methylation

Epigenomics intro

Stephen B. Baylin and Peter A. Jones, “A Decade of Exploring the Cancer Epigenome — Biological and Translational Implications,” Nature Reviews Cancer, (September 23, 2011) - Cancer epigenomics introduction, therapies
Kagohara, Luciane T., Genevieve L. Stein-O’Brien, Dylan Kelley, Emily Flam, Heather C. Wick, Ludmila V. Danilova, Hariharan Easwaran, et al. “Epigenetic Regulation of Gene Expression in Cancer: Techniques, Resources and Analysis.” Briefings in Functional Genomics, August 11, 2017 - Review of epigeneitc modifications, methylation, histones, chromatin states, 3D. Technologies, databases, software. Lots of references
Li, Bing, Michael Carey, and Jerry L. Workman. “The Role of Chromatin during Transcription.” Cell, (February 23, 2007) - Transcription process and the role of chromatin modifications.
Zhou, Vicky W., Alon Goren, and Bradley E. Bernstein. “Charting Histone Modifications and the Functional Organization of Mammalian Genomes.” Nature Reviews. Genetics, (January 2011) - Histone marks review, ChIP-seq. Graphics of histone marks roles
Wang, Zhibin, Dustin E. Schones, and Keji Zhao. “Characterization of Human Epigenomes.” Current Opinion in Genetics & Development, (April 2009) - Concise description of main histone marks, their roles in transcription, and the corresponding studies. Figure 2 - schematic distribution of histone marks with respect to genes-TSSs.
Zhang, Z. D., A. Paccanaro, Y. Fu, S. Weissman, Z. Weng, J. Chang, M. Snyder, and M. B. Gerstein. “Statistical Analysis of the Genomic Distribution and Correlation of Regulatory Elements in the ENCODE Regions.” Genome Research, (June 1, 2007) - ENCODE pilot project analysis. Non-random location of regulatory elements. Enrichment in TSSs, not in the middle or end of transcription sites. PCA and biplot representation of interrelatedness among TFs and histone marks, clustering.
Huen, David S., and Steven Russell. “On the Use of Resampling Tests for Evaluating Statistical Significance of Binding-Site Co-Occurrence.” BMC Bioinformatics (June 30, 2010) - Thorough review on permutation. Problem with different numbers of ROIs/epiregions in permutations. Regions may overlap (i.e., occur in clusters). Independent assignment is best.

Epigenomic enrichment

McLean, Cory Y., Dave Bristor, Michael Hiller, Shoa L. Clarke, Bruce T. Schaar, Craig B. Lowe, Aaron M. Wenger, and Gill Bejerano. “GREAT Improves Functional Interpretation of Cis-Regulatory Regions.” Nature Biotechnology 28, no. 5 (May 2010) - Hypergeometric and binomial enrichment of regulatory regions in relation to genesgenomic regions and their ontologies.
De, Subhajyoti, Brent S. Pedersen, and Katerina Kechris. “The Dilemma of Choosing the Ideal Permutation Strategy While Estimating Statistical Significance of Genome-Wide Enrichment.” Briefings in Bioinformatics 15, no. 6 (November 2014)
Dozmorov, Mikhail G. “Epigenomic Annotation-Based Interpretation of Genomic Data: From Enrichment Analysis to Machine Learning.” Bioinformatics (Oxford, England) 33, no. 20 (October 15, 2017)

Hidden Markov Models, genome segmentation overview

Eddy, Sean R. “What Is a Hidden Markov Model?” Nature Biotechnology, (October 2004)
Eddy, S. R. “Multiple Alignment Using Hidden Markov Models.” Proceedings. International Conference on Intelligent Systems for Molecular Biology (1995) - HMMER hidden markov models for protein sequence alignment. First paper
Schuster-Böckler, Benjamin, and Alex Bateman. “An Introduction to Hidden Markov Models.” 2007 - Hidden Markov Models primer
L.R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proceedings of the IEEE, (February 1989) - Theory and statistics of Hidden Markov Models. Very detailed, thorough
Ernst, Jason, and Manolis Kellis. “Chromatin-State Discovery and Genome Annotation with ChromHMM.” Nature Protocols, (December 2017) - ChromHMM protocol. Intro about ChromHMM, other methods. Links to genome annotation models.
Hoffman, Michael M., Orion J. Buske, Jie Wang, Zhiping Weng, Jeff A. Bilmes, and William Stafford Noble. “Unsupervised Pattern Discovery in Human Chromatin Structure through Genomic Segmentation.” Nature Methods 9, no. 5 (May 2012) - Segway - segmentation and prediction of genomic states. Using dynamic Bayesian network

Methylation

Pidsley, R., et. al., and Susan J. Clark. “Critical Evaluation of the Illumina MethylationEPIC BeadChip Microarray for Whole-Genome DNA Methylation Profiling.” Genome Biology 2016
Pan D., et. al. “Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis.” BMC Bioinformatics, 2010
Bock, Christoph, Eleni M Tomazou, Arie B Brinkman, Fabian Müller, Femke Simmer, Hongcang Gu, Natalie Jäger, Andreas Gnirke, Hendrik G Stunnenberg, and Alexander Meissner. “Quantitative Comparison of Genome-Wide DNA Methylation Mapping Technologies.” Nature Biotechnology, (October 2010) - Methylation intro, technology. Software tools, tables. Quality control and problems. Differential analysis. Public repositories.
Krueger, Felix, Benjamin Kreck, Andre Franke, and Simon R Andrews. “DNA Methylome Analysis Using Short Bisulfite Sequencing Data.” Nature Methods, (January 30, 2012) - Methylation intro, technologies to measure. Alignment problems, QC considerations, processing workflow. Theoretical, references.
Wreczycka, Katarzyna, Alexander Gosdschan, Dilmurat Yusuf, Bjoern Gruening, Yassen Assenov, and Altuna Akalin. “Strategies for Analyzing Bisulfite Sequencing Data,” August 9, 2017 - Bisufite sequencing data analysis steps. Intro into methylation. Refs to packages.
Robinson, Mark D., Abdullah Kahraman, Charity W. Law, Helen Lindsay, Malgorzata Nowicka, Lukas M. Weber, and Xiaobei Zhou. “Statistical Methods for Detecting Differentially Methylated Loci and Regions.” Frontiers in Genetics (2014) - Methylation review, technology, databases, experimental design, statistics and tools for differential methylation detection, beta-binomial distribution, cell type deconvolution.
Krueger, Felix, and Simon R. Andrews. “Bismark: A Flexible Aligner and Methylation Caller for Bisulfite-Seq Applications.” Bioinformatics, (June 1, 2011) - Bismark paper. stranded and unstranded BS sequencing. Conversion of reads, genomes, best alignment strategy. https://www.bioinformatics.babraham.ac.uk/projects/bismark/
Xi, Yuanxin, and Wei Li. “BSMAP: Whole Genome Bisulfite Sequence MAPping Program.” BMC Bioinformatics, (2009) - BSMAP paper. Bisulphite conversion technology introduction, problems. BSMAP algorithm. Very good figures explaining all steps. BSMAP software
Chen, Yunshun, Bhupinder Pal, Jane E. Visvader, and Gordon K. Smyth. “Differential Methylation Analysis of Reduced Representation Bisulfite Sequencing Experiments Using EdgeR.” F1000Research (November 28, 2017) - RRBS differential methylation analysis. Methylation intro. R code tutorial.
Teschendorff, Andrew E., and Caroline L. Relton. “Statistical and Integrative System-Level Analysis of DNA Methylation Data.” Nature Reviews Genetics, November 13, 2017 - Deconvolution of methylation profiles. Reference-based, reference-free, semi-reference-free. Table 1 - tools
Methylation statistics packages: Table 2 in Liu, Hongbo, Song Li, Xinyu Wang, Jiang Zhu, Yanjun Wei, Yihan Wang, Yanhua Wen, et al. “DNA Methylation Dynamics: Identification and Functional Annotation.” Briefings in Functional Genomics, 2016
Mark D. Robinson et al., “Statistical Methods for Detecting Differentially Methylated Loci and Regions,” Frontiers in Genetics 5 (2014) - Methylation nethods review. From data, experimental design to software tools for finding differentially methylated regions.
Shafi, Adib, Cristina Mitrea, Tin Nguyen, and Sorin Draghici. “A Survey of the Approaches for Identifying Differential Methylation Using Bisulfite Sequencing Data.” Briefings in Bioinformatics, March 8, 2017 - Review of differential methylation methods and 22 tools. Categorized by approaches. Pros and cons of each approach, Table 1. Summary of the important characteristics of the 22 surveyed approaches, Table 2. Comparison of the available implementations of the 22 surveyed approaches
Wreczycka, Katarzyna, Alexander Gosdschan, Dilmurat Yusuf, Björn Grüning, Yassen Assenov, and Altuna Akalin. “Strategies for Analyzing Bisulfite Sequencing Data.” Journal of Biotechnology (November 2017) - Review of bisulfite sequencing technology, data preprocessing, and analysis methods and tools. Differential methylation.
Venet, D., F. Pecasse, C. Maenhaut, and H. Bersini. “Separation of Samples into Their Constituents Using Gene Expression Data.” Bioinformatics (2001) - Statistical derivation of deconvolution.

Last updated on April 12, 2021

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