Computational And Statistical Epigenomics

Author : Andrew E. Teschendorff
Publisher : Springer
ISBN 13 : 940179927X
Total Pages : 218 pages
Book Rating : 4.70/5 ( download)

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Download or read book Computational and Statistical Epigenomics written by Andrew E. Teschendorff and published by Springer. This book was released on 2015-05-12 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the reader to modern computational and statistical tools for translational epigenomics research. Over the last decade, epigenomics has emerged as a key area of molecular biology, epidemiology and genome medicine. Epigenomics not only offers us a deeper understanding of fundamental cellular biology, but also provides us with the basis for an improved understanding and management of complex diseases. From novel biomarkers for risk prediction, early detection, diagnosis and prognosis of common diseases, to novel therapeutic strategies, epigenomics is set to play a key role in the personalized medicine of the future. In this book we introduce the reader to some of the most important computational and statistical methods for analyzing epigenomic data, with a special focus on DNA methylation. Topics include normalization, correction for cellular heterogeneity, batch effects, clustering, supervised analysis and integrative methods for systems epigenomics. This book will be of interest to students and researchers in bioinformatics, biostatistics, biologists and clinicians alike. Dr. Andrew E. Teschendorff is Head of the Computational Systems Genomics Lab at the CAS-MPG Partner Institute for Computational Biology, Shanghai, China, as well as an Honorary Research Fellow at the UCL Cancer Institute, University College London, UK.

Author :
Publisher : Academic Press
ISBN 13 : 0128145145
Total Pages : 450 pages
Book Rating : 4.42/5 ( download)

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Download or read book Computational Epigenetics and Diseases written by and published by Academic Press. This book was released on 2019-02-06 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Epigenetics and Diseases, written by leading scientists in this evolving field, provides a comprehensive and cutting-edge knowledge of computational epigenetics in human diseases. In particular, the major computational tools, databases, and strategies for computational epigenetics analysis, for example, DNA methylation, histone modifications, microRNA, noncoding RNA, and ceRNA, are summarized, in the context of human diseases. This book discusses bioinformatics methods for epigenetic analysis specifically applied to human conditions such as aging, atherosclerosis, diabetes mellitus, schizophrenia, bipolar disorder, Alzheimer disease, Parkinson disease, liver and autoimmune disorders, and reproductive and respiratory diseases. Additionally, different organ cancers, such as breast, lung, and colon, are discussed. This book is a valuable source for graduate students and researchers in genetics and bioinformatics, and several biomedical field members interested in applying computational epigenetics in their research. Provides a comprehensive and cutting-edge knowledge of computational epigenetics in human diseases Summarizes the major computational tools, databases, and strategies for computational epigenetics analysis, such as DNA methylation, histone modifications, microRNA, noncoding RNA, and ceRNA Covers the major milestones and future directions of computational epigenetics in various kinds of human diseases such as aging, atherosclerosis, diabetes, heart disease, neurological disorders, cancers, blood disorders, liver diseases, reproductive diseases, respiratory diseases, autoimmune diseases, human imprinting disorders, and infectious diseases

Author : Misook Ha
Publisher : Academic Press
ISBN 13 : 9780128041048
Total Pages : 320 pages
Book Rating : 4.48/5 ( download)

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Download or read book Computational Epigenomics and Disease written by Misook Ha and published by Academic Press. This book was released on 2017-01-01 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Epigenomics and Diseases: Epigenomic Data Analytics for Human Health Application explains the current computational approaches inferring epigenetic mechanisms from epigenetic data. Epigenetic research leads to a considerable amount of data that can be more efficiently organized and analyzed using computer-based systems. All applicable computational approaches are explained in detail within this volume. Computational Epigenetics discusses topics such as statistical analysis and management of big epigenetics datasets; relationships among epigenetic factors and diseases; computational inference of spatial organization of genome; differential regulations and inference of variations of chromatin modifications; and systems biology approaches for identifying chromatin regulators. Additionally, strategies for applying epigenetics data analysis results to disease diagnosis, prognosis, and case studies are included in order to provide thorough and translational comprehension and applicability. The book is a valuable resource for computer scientists, mathematicians, and statisticians interested in bioinformatics and computational biology approaches to epigenetic data analysis, as well as geneticists who are looking to improve their knowledge of computational analytics for their research. Explains the computational methods inferring features of epigenetic marks; Describes the basic computational methods for understanding and deciphering chromatin signatures at the primary organization level; Offers example publications and case studies to show the range of possible applications of the computational analyses of epigenetics data.

Author : Pedro H. Oliveira
Publisher : Springer Nature
ISBN 13 : 107162962X
Total Pages : 267 pages
Book Rating : 4.28/5 ( download)

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Download or read book Computational Epigenomics and Epitranscriptomics written by Pedro H. Oliveira and published by Springer Nature. This book was released on 2023-02-01 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume details state-of-the-art computational methods designed to manage, analyze, and generally leverage epigenomic and epitranscriptomic data. Chapters guide readers through fine-mapping and quantification of modifications, visual analytics, imputation methods, supervised analysis, and integrative approaches for single-cell data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and thorough, Computational Epigenomics and Epitranscriptomics aims to provide an overview of epiomic protocols, making it easier for researchers to extract impactful biological insight from their data.

Author : Wei Zhang
Publisher : Springer Science & Business Media
ISBN 13 : 0387262881
Total Pages : 426 pages
Book Rating : 4.88/5 ( download)

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Download or read book Computational and Statistical Approaches to Genomics written by Wei Zhang and published by Springer Science & Business Media. This book was released on 2007-12-26 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of this book adds eight new contributors to reflect a modern cutting edge approach to genomics. It contains the newest research results on genomic analysis and modeling using state-of-the-art methods from engineering, statistics, and genomics. These tools and models are then applied to real biological and clinical problems. The book’s original seventeen chapters are also updated to provide new initiatives and directions.

Author : Angela Yen
Publisher :
ISBN 13 :
Total Pages : 225 pages
Book Rating : 4.85/5 ( download)

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Download or read book Computational Epigenomics written by Angela Yen and published by . This book was released on 2016 with total page 225 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the fundamental aims of biology is to determine what lies at the root of differences across individuals, species, diseases, and cell types. Furthermore, the sequencing of genomes has revolutionized the ways in which scientists can investigate biological processes and disease pathways; new genome-wide, high-throughput experiments require computer scientists with a biological understanding to analyze and interpret the data to improve our understanding about life science. This provides us with a key opportunity to use computational techniques for new biological discoveries. While genetic variation plays an important role in influence phenotype, sequence alone cannot account for all differences: for example, different types of cells in an individual have varying function and attributes, but identical genetic makeup. This highlights the importance of studying epigenetic changes, which are dynamic chemical changes to and around the DNA. While the DNA of every cell in an individual is the same, the epigenetic context for that DNA varies from cell to cell. In this way, these epigenetic differences play a crucial role in gene regulation, with epigenetic changes both causing and recording regulatory mechanisms. In this thesis, we combine the power of computational, statistical, and data science approaches with the new wave of epigenetic data at a genome-wide level in a number of ways. First, in chapter 2, we demonstrate the importance of computational analysis at an epigenomic level by identifying an epigenomic signature of the olfactory receptor gene family that gives insight into the mechanism behind monogenic gene regulation. Next, in chapter 3, we explain our development of ChromDiff, a novel statistical and information theoretic computational methodology to identify chromatin state differences in groups of samples. In our methodology, we use correction for external covariates to isolate the relevant signal, and as a result, we find that our method outperforms existing computational methods, with further validation through randomized simulations. In chapter 4, we apply our methodology to characteristics including sex, developmental age, and tissue type, we unveil relevant chromatin states and genes that distinguish the groups of epigenomes, with further validation of our results through differential expression analysis and gene set enrichment. In chapter 5, we show the power of integrative analysis through the combination of DNA methylation data with chromatin state profiles, cell types, sample groups, experimental technologies, and histone mark data to reveal insightful epigenetic patterns and relationships. Finally, in chapter 6, we identify "hidden" or "unknown" covariates in epigenomic data by using agnostic principal component analysis on our samples to discover similarities between our known covariates and the identified components. In summation, our research highlights the importance of both algorithm development and method application for epigenomic questions, reaffirming the importance of interdisciplinary research that brings together cutting-edge techniques in computer science with appropriate biological hypotheses and data. While questions and analysis must be carefully paired in an informed manner to produce meaningful, interpretable, and believable results in computational biology, our work here provides a sampling of the vast potential for scientific discovery at the intersection of the fields of computer science and biology.

Author : Altuna Akalin
Publisher : CRC Press
ISBN 13 : 1498781861
Total Pages : 462 pages
Book Rating : 4.62/5 ( download)

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Download or read book Computational Genomics with R written by Altuna Akalin and published by CRC Press. This book was released on 2020-12-16 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

Author : Momiao Xiong
Publisher : CRC Press
ISBN 13 : 1498725805
Total Pages : 668 pages
Book Rating : 4.04/5 ( download)

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Download or read book Big Data in Omics and Imaging written by Momiao Xiong and published by CRC Press. This book was released on 2017-12-01 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both population and family genomic data in sequencing era. It is unique in that it presents both hypothesis testing and a data mining approach to holistically dissecting the genetic structure of complex traits and to designing efficient strategies for precision medicine. The general frameworks for association analysis and machine learning, developed in the text, can be applied to genomic, epigenomic and imaging data. FEATURES Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data analysis and the modern advanced statistical methods for big data Provides tools for high dimensional data reduction Discusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selection Provides real-world examples and case studies Will have an accompanying website with R code The book is designed for graduate students and researchers in genomics, bioinformatics, and data science. It represents the paradigm shift of genetic studies of complex diseases– from shallow to deep genomic analysis, from low-dimensional to high dimensional, multivariate to functional data analysis with next-generation sequencing (NGS) data, and from homogeneous populations to heterogeneous population and pedigree data analysis. Topics covered are: advanced matrix theory, convex optimization algorithms, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern association, interaction, pathway and network analysis of rare and common variants, biomarker identification, disease risk and drug response prediction.

Author : Jianzhong Su
Publisher : Frontiers Media SA
ISBN 13 : 2889665046
Total Pages : 465 pages
Book Rating : 4.44/5 ( download)

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Download or read book Computational Epigenetics in Human Diseases, Cell Differentiation, and Cell Reprogramming, Volume I written by Jianzhong Su and published by Frontiers Media SA. This book was released on 2021-02-24 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Petko Plamenov Fiziev
Publisher :
ISBN 13 :
Total Pages : 248 pages
Book Rating : 4.98/5 ( download)

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Download or read book Computational Methods for Analysis of Large-Scale Epigenomics Data written by Petko Plamenov Fiziev and published by . This book was released on 2018 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reverse-engineering and understanding the regulatory dynamics of genes is key to gaining insights into many biological processes on molecular level. Advances in genomics technologies and decreasing costs of DNA sequencing enabled interrogating relevant properties of the genome, collectively referred to as epigenetics, on very large scale. This work presents results from two collaborative projects with experimental biologists and two new general computational methods for analysis of high-throughput epigenomic data. The first collaborative project is joint work with Dr. Kathrin Plath and members of her lab at UCLA on studying the epigenetics of somatic cell reprogramming in mouse. By generating and analyzing a large compendium of genomics datasets at four distinct stages during reprogramming, we discovered key properties of the regulatory dynamics during this process and proposed new ways to improve its efficiency. The first computational method in this work, ChromTime, presents a novel framework for modeling spatio-temporal dynamics of chromatin marks. ChromTime detects expanding, contracting and steady domains of chromatin marks from time course epigenomics data. Applications of the method to a diverse set of biological systems show that predicted dynamic domains likely mark important regulatory regions as they associate with changes in gene expression and transcription factor binding. Furthermore, ChromTime enables analyses of the directionality of spatio-temporal dynamics of epigenetic domains, which is a previously understudied aspect of chromatin dynamics. Our results uncover associations between the direction of expanding and contracting domains of several chromatin marks and the direction of transcription of nearby genes. The second collaborative project is joint work with cancer researchers, Dr. Lynda Chin and Dr. Kunal Rai and members of their labs at MD Anderson Cancer Center in Houston, TX. Within this project we studied the epigenetics of melanoma cancer progression. Our collaborators generated genome-wide maps for a large number of histone modifications, DNA methylation and gene expression in tumorigenic and non-tumorigenic human melanocytes. By comparing these maps we discovered that loss of acetylation marks at regulatory regions is characteristic of tumorigenic melanocytes and that modulating acetylation levels can impact tumorigenic potential of cells. In addition, we developed a novel nanostring assay for interrogating the chromatin state at a small subset of genomic locations, which can potentially be used for diagnostic or prognostic purposes in future. The second computational method presented in this work, CSDELTA, is designed to detect differential chromatin sites from genome-wide chromatin state maps in groups with multiple samples. Biological relevance of detected differential sites is supported by associations with changes in gene expression and transcription factor binding. Furthermore, CSDELTA models the functional similarity between chromatin states and improves upon the resolution of detection compared to existing methods, which enables more accurate downstream analyses to gain insights into the regulatory dynamics of biological systems.