Ultra High Throughput Single Cell Co Sequencing Of Dna Methylation And Rna Using 3 Level Combinatorial Indexing
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Ultra High Throughput Single Cell Co Sequencing Of Dna Methylation And Rna Using 3 Level Combinatorial Indexing
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Author : Huy Lam
language : en
Publisher:
Release Date : 2022
Ultra High Throughput Single Cell Co Sequencing Of Dna Methylation And Rna Using 3 Level Combinatorial Indexing written by Huy Lam and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
DNA methylation at cytosines has long been associated with early development, maturation, and aging of human tissues. Traditionally, DNA methylation is associated with gene silencing. However, recent single cell multi-omic DNA methylation and RNA sequencing methods have shown that the role of DNA methylation on the expression of nearby genes could silence or activate them depending on the gene and cell type. The recent developments these assays have detected cell type specific DNA methylation and RNA coupling in stem cell rich and human brain tissues. This specificity underscores the need for future growth in DNA methylation and RNA co-sequencing technologies and analysis tools. Presently, about 100,000 single cell profiles are required to adequately map tissues. DNA methylation and RNA co-sequencing methods require the physical isolation of single cells in individual wells. There is no method that can assay 100,000 cells without utilizing extensive liquid handling systems. We address this challenge by developing a novel ultra-high throughput DNA methylation and RNA co-sequencing platform, sci-Gel, that utilizes three levels of combinatorial indexing to increase the throughput of existing technologies to 50,000-100,000 cells per experiment with just three 96 well plates. In this dissertation, we first push the boundaries of present combinatorial indexing techniques where the DNA and RNA of single cells are simultaneously extracted and immobilized within polyacrylamide gel beads that are used for indexing. This resulted in the development of a 2-level combinatorial indexing platform that could be used to co-sequence DNA copy-number variations, relevant in cancers, and RNA at the scale of thousands of cells. We then describe the adaptations made from existing bisulfite conversion chemistries to our gel beads to incorporate the DNA methylation feature. We then describe the development of a 3-level combinatorial indexing platform to increase the cell throughput of our technology to 50,000-100,000 cells per experiment. Finally, we discuss future efforts to utilize sci-Gel to create the first single cell DNA methylation and RNA co-sequencing map of peripheral blood mononuclear cells.
Single Cell Sequencing And Methylation
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Author : Buwei Yu
language : en
Publisher: Springer Nature
Release Date : 2020-09-19
Single Cell Sequencing And Methylation written by Buwei Yu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-19 with Science categories.
With the rapid development of biotechnologies, single-cell sequencing has become an important tool for understanding the molecular mechanisms of diseases, defining cellular heterogeneities and characteristics, and identifying intercellular communications and single-cell-based biomarkers. Providing a clear overview of the clinical applications, the book presents state-of-the-art information on immune cell function, cancer progression, infection, and inflammation gained from single-cell DNA or RNA sequencing. Furthermore, it explores the role of target gene methylation in the pathogenesis of diseases, with a focus on respiratory cancer, infection and chronic diseases. As such it is a valuable resource for clinical researchers and physicians, allowing them to refresh their knowledge and improve early diagnosis and therapy for patients.
Computational Methods For Studying Gene Regulation And Genome Organization Using High Throughput Dna Sequencing
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Author : Giancarlo A. Bonora
language : en
Publisher:
Release Date : 2015
Computational Methods For Studying Gene Regulation And Genome Organization Using High Throughput Dna Sequencing written by Giancarlo A. Bonora and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
The full sequencing of the human genome ushered in the genomics era and laid the foundation for a more comprehensive understanding of gene regulation and development. But, since the DNA sequence represents only one aspect of the genomic information housed within the nucleus, the question of exactly how it is utilized to direct developmental programs and tissue-specific gene expression is still an open one. However, rapid advances in high-throughput DNA sequencing (HTS) technologies over the past decade have allowed biologists to begin to tackle the question on a genomic scale. HTS has been coupled to bisulfite conversion of DNA for assessing cytosine methylation (bisulfite sequencing), to chromatin immunoprecipitation for ascertaining genomic locations bound by specific factors or found in a particular chromatin state (ChIP-seq), to the isolation of transcripts for the measurement of gene expression (RNA-seq), and to methods of chromosome conformation capture for the identification of genome-wide DNA-DNA interactions (4C-seq and Hi-C). The focus of my doctoral research has been the development of novel bioinformatics approaches to analyze the data produced by these technologies in order to shed light on how distinct cell identities are established and maintained. Here, I present highlights of this work in six chapters. Chapter 1 presents a study investigating DNA methylation changes going from the differentiated to pluripotent state, which shows that changes predominantly occur late in the process and are strongly associated with changes to chromatin state. Chapter 2 introduces methylation-sensitive restriction enzyme bisulfite sequencing (MREBS) as a method for assessing precise differential DNA methylation at cost comparable to RRBS, while providing additional information over a coverage area more comparable to WGBS. Chapter 3 presents a study showing that inhibition of ribonucleotide reductase decreased DNA methylation genome-wide by enhancing the incorporation of a cytidine analog into DNA. Chapter 4 describes a study showing that, for genes important to leaf senescence, temporal changes in expression closely matched changes to two histone modifications. Chapter 5 reviews cutting-edge research exploring the link between regulatory networks and genome organization. Chapter 6 describes a study showing that regulators responsible for cell identity contribute to cell type-specific genome organization.
Single Molecule And Single Cell Sequencing
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Author : Yutaka Suzuki
language : en
Publisher: Springer
Release Date : 2019-04-09
Single Molecule And Single Cell Sequencing written by Yutaka Suzuki and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-09 with Medical categories.
This book presents an overview of the recent technologies in single molecule and single cell sequencing. These sequencing technologies are revolutionizing the way of the genomic studies and the understanding of complex biological systems. The PacBio sequencer has enabled extremely long-read sequencing and the MinION sequencer has made the sequencing possible in developing countries. New developments and technologies are constantly emerging, which will further expand sequencing applications. In parallel, single cell sequencing technologies are rapidly becoming a popular platform. This volume presents not only an updated overview of these technologies, but also of the related developments in bioinformatics. Without powerful bioinformatics software, where rapid progress is taking place, these new technologies will not realize their full potential. All the contributors to this volume have been involved in the development of these technologies and software and have also made significant progress on their applications. This book is intended to be of interest to a wide audience ranging from genome researchers to basic molecular biologists and clinicians.
Multiplex Single Cell Rna Sequencing For Chemical Genomics And Spatial Transcriptomics
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Author : Sanjay R. Srivatsan
language : en
Publisher:
Release Date : 2021
Multiplex Single Cell Rna Sequencing For Chemical Genomics And Spatial Transcriptomics written by Sanjay R. Srivatsan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Each of us begins life as a single fertilized cell. Following a seemingly predetermined set of cell divisions, the single cell morphs into a rough mass, then a hollowed tube, and finally becomes a recognizable neonatal form. How the information contained within a single cell si- multaneously specifies an organism’s anatomy, the construction of its organs, and the ability to cogitate on this very question, remains one of biology’s open questions. Although centuries of careful experiments devoted to characterizing development have revealed many important genes and mechanisms, the results of these experiments span different model organisms, developmental stages, cell populations and measurement modalities. Integrating this knowledge base into coher- ent representation requires a cellular scaffold that charts an organism’s development over the axes of time and space. Preliminary unified representations of developing organisms (e.g. C. Elegans, Zebrafish and Mouse) have been created by large-scale single cell RNA sequencing (scRNA-seq) efforts. These efforts have characterized the set of intermediates through which differentiating cells transit and have profiled the large number of cell types present in a developing organism. Although scRNA-seq data have proven powerful in cataloging cellular states, they lack crucial context: i) the experimental context afforded by the comparison of multiple conditions (e.g. wild-type vs. perturbation) and ii) a cell’s spatial context, a crucial factor driving its behavior. To address these knowledge gaps, over the course of my PhD I have developed two scRNA-seq technologies: 1) sci- Plex, a generalizable strategy to label cell populations and 2) sci-Space, a methodology to record acell’s spatial position in conjunction with its single cell transcriptome. (1) First I developed the sci-Plex protocol, an inexpensive and efficient method to label single cells through the chemical fixation of unmodified single stranded oligos to nuclei prior to scRNA- seq library preparation. To demonstrate proof-of-concept of the sci-Plex protocol, I performed a high-throughput, high-content drug screen at single cell resolution in 3 cancer cell lines; effectively conducting 4,500 independent scRNA-seq experiments at once. The resulting dataset enabled characterization of a drug’s potency, class, mechanism of action, and the heterogeneity of cellular responses induced upon drug treatment. For example, our scRNA-seq data showed that histone deacetylase inhibitors likely lead to cell death by trapping valuable acetyl molecules on chromatin. (2) Next, I extended the application of the sci-Plex protocol and developed the sci-Space method to capture spatial information from sectioned tissue. The fast and scalable sci-Space method uses patterned oligonucleotide barcodes in a regular array such that each spot contains a unique set of sequences. Then, to mark each nucleus’ coordinates on the grid, the barcodes are stamped onto a tissue section prior to disaggregation and library preparation. To showcase the power of sci-Space, I collected a dataset comprising over 120,000 cells originating from 14 sections of a single E14 mouse embryo. The resulting data uncovers the genes that drive the devel- oping organism’s body plan and reveals a widespread migration signature within neurons that form the developing brain. These data also provide a quantitative assessment of how cell state relates to spatial position within the developing embryo. Specifically, our estimates indicate that 25% of the variance in gene expression observed is attributable to spatial position. It is my hope that this technology will power the generation of a unified scaffold of development akin to the reference genome. I believe that such a unified representation will be instrumental in amassing data, accel- erating discovery and facilitating translation through the training of machine learning models of cellular state.
Computational Methods For Next Generation Sequencing Data Analysis
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Author : Ion Mandoiu
language : en
Publisher: John Wiley & Sons
Release Date : 2016-09-12
Computational Methods For Next Generation Sequencing Data Analysis written by Ion Mandoiu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-12 with Computers categories.
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.
Dna Methylation Protocols
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Author : Jörg Tost
language : en
Publisher: Humana Press
Release Date : 2018-08-30
Dna Methylation Protocols written by Jörg Tost and has been published by Humana Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-30 with Medical categories.
This third edition volume expands on the previous editions by providing a comprehensive update on the available technologies required to successfully perform DNA methylation analysis. The different technologies discussed in this book analyze the global DNA methylation contents, comprehensive analyses using various NGS based methods for genome-wide DNA methylation analysis, along with precise quantification of DNA methylation levels on single CpG positions. The chapters in this book are divided into 7 parts: an introduction to the field along with tips on study design and data analysis; global DNA methylation levels; genome-wide DNA methylation analysis; highly multiplexed target regions; locus-specific DNA methylation analysis; DNA methylation analysis of specific biological samples; and hydroxymethylation. 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, DNA Methylation Protocols, Third Edition is a valuable resource for postdoctoral investigators and research scientists who work with different aspects of genetics, and cellular and molecular biology, as well as clinicians who are involved in diagnostics or treatment of diseases with epigenetic components.
Analyzing High Dimensional Gene Expression And Dna Methylation Data With R
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Author : Hongmei Zhang
language : en
Publisher: CRC Press
Release Date : 2020-05-14
Analyzing High Dimensional Gene Expression And Dna Methylation Data With R written by Hongmei Zhang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-14 with Computers categories.
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.
High Resolution Dna Methylation Analysis Of Gene Promoters In Human Chromosome 21
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Author : Yingying Zhang
language : en
Publisher:
Release Date : 2009
High Resolution Dna Methylation Analysis Of Gene Promoters In Human Chromosome 21 written by Yingying Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
DNA methylation is an important epigenetic signal, which has an essential role in gene regulation, development and disease processes. We optimized the work procedure for DNA methylation analysis and set up a sequencing and analysis pipeline for medium to high throughput DNA methylation analysis. In addition, we developed a web interface program for bisulfite sequencing data presentation, compilation and comparison (BDPC). By using the high resolution methods, we analyzed DNA methylation patterns for 190 gene promoter regions on chromosome 21 in five human cell types. Our results show that average DNA methylation levels are distributed bimodally with enrichment of highly methylated and unmethylated sequences. Within CpG-rich sequences, DNA methylation was found to be anti-correlated with CpG dinucleotide density and GC content. We observed over-representation of CpG sites in distances of 9, 18 and 27 bps in highly methylated sequences. DNA methylation in promoter regions is strongly correlated with the absence of gene expression and low levels of activating epigenetic marks. Additionally, we found that amplicons from different parts of a CpG island frequently differ in their DNA methylation level, methylation levels of individual cells in one tissue are very similar and methylation patterns follow a relaxed site specific distribution. We further analyzed DNA methylation pattern of 16 amplicons from chromosome 21 in 38 individuals and identified allele-specific DNA methylation of 6 amplicons. The allele-specific methylation of these amplicons is not connected to imprinting but due to the genetic polymorphisms between the two alleles. Our data indicate that genetic differences strongly influence interindividual variations of DNA methylation. We extrapolate that the allele-specific methylation is likely affecting many genes in the human genome and might contribute to allele specific expression, which is a widespread phenomenon in the human genome.
Algorithms For Determining Differentially Expressed Genes And Chromosome Structures From High Throughput Sequencing Data
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Author : Yi-Wen Yang
language : en
Publisher:
Release Date : 2015
Algorithms For Determining Differentially Expressed Genes And Chromosome Structures From High Throughput Sequencing Data written by Yi-Wen Yang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Bioinformatics categories.
Next-generation sequencing (NGS) technologies are able to sequence DNA or RNA molecules at unprecedented speed and with high accuracy. Recently, NGS technologies have been applied in a variety of contexts, e.g., whole genome sequencing, transcript expression profiling, chromatin immunoprecipitation sequencing, and small RNA sequencing, to accelerate genomic researches. The size of NGS data is usually gigantic such that the data analysis in these applications of NGS largely relies on efficient computational methods. Due to the critical demand for high performance computational algorithms, in the past few years, my research interest was focused on designing novel algorithms to address challenges in NGS data analysis. The main theme of this dissertation includes algorithmic solutions to three crucial problems in NGS data analysis, two arising from differential expression analysis using high-throughput mRNA sequencing (RNA-Seq) and the other from chromosome structure capture using high-throughput DNA sequencing (Hi-C). (1) In differential expression analysis of RNA-Seq data, long or highly expressed genes are more likely to be detected by most of existing computational methods. However, such bias against short or lowly expressed genes may distort down-stream data analysis at system biology level. To further improve the sensitivity to short or lowly expressed genes, we designed a new computational tool, called MRFSeq, to combine both gene coexpression and RNA-Seq data. The performance of MRFSeq was carefully assessed using simulated and real benchmark datasets and the experimental results showed that MRFSeq was able to provide more accurate prediction in calling differentially expressed genes than the other existing methods such that the distortion due to the bias against short and lowly expressed genes was significantly alleviated. (2) Most of the existing differential expression analysis tools are developed for comparing RNA-Seq samples between known biological conditions. However, the differential expression analysis is also important to other biological researches where the predefined conditions of samples are not available as a priori. For example, differential expressed transcripts can be used as biomarkers to classify a cohort of cancer samples into subtypes such that better diagnosis and therapy methods can be developed for each subtype. So, the first computational method, called SDEAP, was proposed to identify differential expressed genes and their alternative splicing events without the requirement of the predefined conditions. SDEAP provided accurate prediction in our experiments on simulated and real datasets. The utility of SDEAP was further demonstrated by classifying subtypes of breast cancer, cell types and the cycle phases of mouse cells. (3) Chromosome structures in nucleus play important roles in biological processes of cells. The Hi-C technology allows biology researchers to reconstruct the three dimensional structures of chromosomes in nucleus of cells on a genome-wide scale and thus serves as a vital component in studies of chromosome structures. During the experimental steps of Hi-C, systematic biases may be introduced into Hi-C data. Hence, eliminating the systematic biases is essential to all the applications using Hi-C data. We developed an improved bias reduction algorithm, called GDNorm. By taking advantages of a Poisson regression model that explicitly formulates the causal relationship of Hi-C data, systematic biases and spatial distances in chromosome structures, our experimental results showed that GDNorm was able to remove the biases from Hi-C data such that the corrected Hi-C data could lead to accurate reconstruction of chromosome structures. In the near future, with the rapid accumulation of NGS data, we expect these efficient computational methods to become valuable tools for discovering novel biological knowledge and benefit numerous genomic researches.