Download or read book Computational Methods for the Analysis of DNA Methylation and Gene Expression Data written by Larry Tao Lam and published by . This book was released on 2016 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: RNA expression profiling and DNA methylation analysis have been essential tools in understanding genomic mechanisms underlying human health and disease. Although many annotation databases are publically available, alternative data resources may be overlooked. This work focuses on the development of computational tools and strategies that incorporate results from both the leading functional annotation tools as well as working directly with publicly available expression and methylation datasets. Chapter 1 outlines the leading approaches for interpreting DNA methylation and RNA expression analyses. In addition, chapter 1 provides a brief background of Burkitt's lymphoma and amyotophic lateral sclerosis (ALS) for studies discussed in later chapters. In chapter 2, we developed a set of methylation characterization and visualization tools for bisulfite sequencing data. These tools also characterize methylation levels at genomic features, like gene bodies as well as transcription factor targets. We provide a means to detect epigenetic regulation of transcription factor binding sites. Chapter 3 describes a multi- omics approach to understand an epigenetic mechanism for chemoresistance in Burkitt's lymphoma. Burkitt's lymphoma cell lines were cultured with drugs and developed increasing levels of resistance to chemotherapy. By analyzing transcriptional profiles of the chemoresistant cell lines with healthy B-cells at different stages of maturation as well as subsequent integration of DNA methylation and ChiP-Seq data from the chemoresistant cell lines, we were able to propose a novel mechanism of drug resistance in which E2a and PRC2 drive changes in the B- cell epigenome. In chapters 3 and chapter 4, we focused on the transcriptional and DNA methylation analysis of peripheral blood mononuclear cells (PBMCs) of patients affected with amyotrophic lateral sclerosis (ALS). Using transcriptional data of monocytes stimulated by different molecules, we were able to categorize our samples into inflammatory and non- inflammatory groups. A pathway enrichment analysis of the differentially expressed genes reveals potential targets of immune based treatments for ALS. In chapter 5, we investigated the differences in DNA methylation profiles in PBMCs from a pair of monzygotic twins discordant in the diagnosis for ALS. We developed a cell type abundance analysis method which suggest that the affected twin loses T-cells and gains monocytes during the course of the disease. Our direct use of reference data sets highlights the potential for understanding RNA-Seq and BS-Seq data and provides the groundwork for development of generalized transcription or methylation analysis tools, like CEllFi. Chapter 6 outlines the implementation of CEllFi, a bisulfite sequencing based method that allows for cellular deconvolution of heterogenous samples.

Download or read book Computational Methods for High throughput DNA Methylation and Gene Expression Data Analysis with Applications to Colorectal Tumors written by Stephany Orjuela and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Computational Methods for the Analysis of Genomic Data and Biological Processes written by Francisco A. Gómez Vela and published by MDPI. This book was released on 2021-02-05 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.

Download or read book Computational Methods for Next Generation Sequencing Data Analysis written by Ion Mandoiu and published by John Wiley & Sons. This book was released on 2016-10-03 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols. Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis. Computational Methods for Next Generation Sequencing Data Analysis: Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms Discusses the mathematical and computational challenges in NGS technologies Covers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.

Download or read book Analyzing High Dimensional Gene Expression and DNA Methylation Data with R written by Hongmei Zhang and published by CRC Press. This book was released on 2020-05-14 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analyzing high-dimensional gene expression and DNA methylation data with R is the first practical book that shows a ``pipeline" of analytical methods with concrete examples starting from raw gene expression and DNA methylation data at the genome scale. Methods on quality control, data pre-processing, data mining, and further assessments are presented in the book, and R programs based on simulated data and real data are included. Codes with example data are all reproducible. Features: • Provides a sequence of analytical tools for genome-scale gene expression data and DNA methylation data, starting from quality control and pre-processing of raw genome-scale data. • Organized by a parallel presentation with explanation on statistical methods and corresponding R packages/functions in quality control, pre-processing, and data analyses (e.g., clustering and networks). • Includes source codes with simulated and real data to reproduce the results. Readers are expected to gain the ability to independently analyze genome-scaled expression and methylation data and detect potential biomarkers. This book is ideal for students majoring in statistics, biostatistics, and bioinformatics and researchers with an interest in high dimensional genetic and epigenetic studies.

Download or read book Computational Methods With Applications In Bioinformatics Analysis written by Jeffrey J P Tsai and published by World Scientific. This book was released on 2017-06-09 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: This compendium contains 10 chapters written by world renowned researchers with expertise in semantic computing, genome sequence analysis, biomolecular interaction, time-series microarray analysis, and machine learning algorithms.The salient feature of this book is that it highlights eight types of computational techniques to tackle different biomedical applications. These techniques include unsupervised learning algorithms, principal component analysis, fuzzy integral, graph-based ensemble clustering method, semantic analysis, interolog approach, molecular simulations and enzyme kinetics.The unique volume will be a useful reference material and an inspirational read for advanced undergraduate and graduate students, computer scientists, computational biologists, bioinformatics and biomedical professionals.

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 452 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

Download or read book Computational Methods for Multi Omics Data Analysis in Cancer Precision Medicine written by Ehsan Nazemalhosseini-Mojarad and published by Frontiers Media SA. This book was released on 2023-08-02 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cancer is a complex and heterogeneous disease often caused by different alterations. The development of human cancer is due to the accumulation of genetic and epigenetic modifications that could affect the structure and function of the genome. High-throughput methods (e.g., microarray and next-generation sequencing) can investigate a tumor at multiple levels: i) DNA with genome-wide association studies (GWAS), ii) epigenetic modifications such as DNA methylation, histone changes and microRNAs (miRNAs) iii) mRNA. The availability of public datasets from different multi-omics data has been growing rapidly and could facilitate better knowledge of the biological processes of cancer. Computational approaches are essential for the analysis of big data and the identification of potential biomarkers for early and differential diagnosis, and prognosis.

Download or read book Computational Methods for the Analysis of Genomic Data and Biological Processes written by Francisco A. Gómez Vela and published by . This book was released on 2021 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.

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.

Download or read book Computational Methods for Epigenetic Studies written by Elena Yavorska Harris and published by . This book was released on 2010 with total page 105 pages. Available in PDF, EPUB and Kindle. Book excerpt: The epigenome has increasingly been the focus of research over the past decade. Epigenetic control occurs in two primary ways: DNA methylation and chemical modification of histones. The latter mechanism determines whether the chromatin is tightly packed, in which case gene expression is repressed, or relaxed, in which case gene expression is enhanced. DNA methylation is the addition of the methyl groups to cytocines. Methylation of DNA is involved in a variety of biological processes, including embryogenesis and development, silencing of transposable elements, and regulation of gene transcription. In our research, we developed a set of computational methods and software tools that enable genome-wide epigenetic studies. In particular, our computational methods and software tools allow for (1) the determination of dynamic nucleosome positioning and the analysis of the correlation between the nucleosome landscape and gene expression, (2) the identification of methylation patterns from raw data obtained from next-generation sequencing technologies such as the Illumina Genome Analyzer, and (3) the discovery of transcription factors binding sites from data on the dynamic chromatin structure remodeling. Our methods and tools were used to study nucleosome landscape, methylation and control of transcription in the human malaria parasite which is responsible to one million deaths world-wide every year.

Download or read book Next Generation Sequencing written by Jerzy Kulski and published by BoD – Books on Demand. This book was released on 2016-01-14 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: Next generation sequencing (NGS) has surpassed the traditional Sanger sequencing method to become the main choice for large-scale, genome-wide sequencing studies with ultra-high-throughput production and a huge reduction in costs. The NGS technologies have had enormous impact on the studies of structural and functional genomics in all the life sciences. In this book, Next Generation Sequencing Advances, Applications and Challenges, the sixteen chapters written by experts cover various aspects of NGS including genomics, transcriptomics and methylomics, the sequencing platforms, and the bioinformatics challenges in processing and analysing huge amounts of sequencing data. Following an overview of the evolution of NGS in the brave new world of omics, the book examines the advances and challenges of NGS applications in basic and applied research on microorganisms, agricultural plants and humans. This book is of value to all who are interested in DNA sequencing and bioinformatics across all fields of the life sciences.

Download or read book Computational Methods for Single Cell Data Analysis written by Guo-Cheng Yuan and published by Humana Press. This book was released on 2019-02-14 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: This detailed book provides state-of-art computational approaches to further explore the exciting opportunities presented by single-cell technologies. Chapters each detail a computational toolbox aimed to overcome a specific challenge in single-cell analysis, such as data normalization, rare cell-type identification, and spatial transcriptomics analysis, all with a focus on hands-on implementation of computational methods for analyzing experimental 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. Authoritative and cutting-edge, Computational Methods for Single-Cell Data Analysis aims to cover a wide range of tasks and serves as a vital handbook for single-cell data analysis.

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 463 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.

Download or read book Gene Expression Data Analysis written by Pankaj Barah and published by CRC Press. This book was released on 2021-11-08 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and the biological sciences

Download or read book Computational Analyses of DNA Methylation and Gene Expression for the Molecular Profiling of Disease States written by Nyasha Chambwe and published by . This book was released on 2014 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Assessing patterns of molecular entities such as DNA or RNA can contribute to an understanding of disease and describe signatures associated with clinical outcomes. The development of high throughput platforms such as microarrays and sequencing technologies allows the comprehensive molecular characterization of disease samples. These molecular profiling approaches generate large volumes of data that require the implementation of scalable computational approaches for analysis and data management. This dissertation focuses on computational analysis methods for DNA methylation and gene expression data and their application in molecular profiling studies to characterize disease states. First, the development of computational analysis pipelines for gene expression and DNA methylation sequencing datasets is presented. These analysis pipelines are implemented in GobyWeb, a user-friendly integrated analysis suite, developed in the Campagne laboratory, that supports the analysis and management of high throughput sequencing data. Second, we profiled DNA methylation in a mouse model of anxiety to investigate the hypothesis that an adverse maternal environment characterized by a maternal serotonin receptor knockout is associated with the anxiety phenotype in adult mice raised in this environment. Primarily we found that genes encoding cell adhesion molecules and neurotransmitter receptor genes were aber-raptly methylated in mice raised by mothers with a serotonin receptor deficit (either full knock-outs, or heterozygotes). Many of the aberrantly methylated genes have been previously implicated in anxiety. Finally we present the application of DNA methylation profiling to identify molecular subtypes of Diffuse Large B Cell Lymphoma (DLBCL). We carried out unsupervised clustering of DLBCLs based on how variable the genome-wide methylation profile compared to normal germinal center B cells and identified six DNA methylation-based clusters. The novel clusters are characterized by aberrant methylation of genes involved specific biological pathways such as cytokine-mediated signaling, ephrin signaling and pathways associated with apoptosis and cell cycle regulation. We found that the magnitude of methylation changes is significantly associated with survival outcomes in this cohort. This dissertation concludes with a discussion on future directions and perspectives of this work.

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.