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Software Development And Analysis Of High Throughput Sequencing Data For Genomic Enhancer Prediction
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Book Synopsis Software Development and Analysis of High Throughput Sequencing Data for Genomic Enhancer Prediction by : Juan González-Vallinas Rostes
Download or read book Software Development and Analysis of High Throughput Sequencing Data for Genomic Enhancer Prediction written by Juan González-Vallinas Rostes and published by . This book was released on 2013 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: High Throughput Sequencing technologies (HTS) are becoming the standard in genomic regulation analysis. During my thesis I developed software for the analysis of HTS data. Through collaborations with other research groups, I specialized in the analysis of ChIP-Seq short mapped reads. For instance, I collaborated in the analysis of the effect of Hog1 stress induced response in Yeast and helped in the design of a multiple promoter-alignment method using ChIP-Seq data, among other collaborations. Making use of expertise and the software developed during this time, I analyzed ENCODE datasets in order to detect active genomic enhancers. Genomic enhancers are regions in the genome known to regulate transcription levels of close by or distant genes. Mechanism of activation and silencing of enhancers is still poorly understood. Epigenomic elements, like histone modifications and transcription factors play a critical role in enhancer activity. Modeling epigenomic signals, I predicted active and silenced enhancers in two cell lines and studied their effect in splicing and transcription initiation.
Book Synopsis New High Throughput Technologies for DNA Sequencing and Genomics by : Keith R. Mitchelson
Download or read book New High Throughput Technologies for DNA Sequencing and Genomics written by Keith R. Mitchelson and published by Elsevier. This book was released on 2011-09-22 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the independent invention of DNA sequencing by Sanger and by Gilbert 30 years ago, it has grown from a small scale technique capable of reading several kilobase-pair of sequence per day into today's multibillion dollar industry. This growth has spurred the development of new sequencing technologies that do not involve either electrophoresis or Sanger sequencing chemistries. Sequencing by Synthesis (SBS) involves multiple parallel micro-sequencing addition events occurring on a surface, where data from each round is detected by imaging. New High Throughput Technologies for DNA Sequencing and Genomics is the second volume in the Perspectives in Bioanalysis series, which looks at the electroanalytical chemistry of nucleic acids and proteins, development of electrochemical sensors and their application in biomedicine and in the new fields of genomics and proteomics. The authors have expertly formatted the information for a wide variety of readers, including new developments that will inspire students and young scientists to create new tools for science and medicine in the 21st century. Reviews of complementary developments in Sanger and SBS sequencing chemistries, capillary electrophoresis and microdevice integration, MS sequencing and applications set the framework for the book. * 'Hot Topic' with DNA sequencing continuing as a major research activity in many areas of life science and medicine. * Bringing together new developments in DNA sequencing technology * Reviewing issues relevant to the new applications used
Book Synopsis Technology and Method Developments for High-throughput Translational Medicine by : Junhee Seok
Download or read book Technology and Method Developments for High-throughput Translational Medicine written by Junhee Seok and published by Stanford University. This book was released on 2011 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Translation of knowledge from basic science to medicine is essential to improving both clinical research and practice. In this translation, high-throughput genomic approaches can greatly accelerate our understanding of molecular mechanisms of diseases. A successful high-throughput genomic study of disease requires, first, comprehensive and efficient platforms to collect genomic data from clinical samples, and second, computational analysis methods that utilize databases of prior biological knowledge together with experimental data to derive clinically meaningful results. In this thesis, we discuss the development of a new microarray platform as well as computational methods for knowledge-based analysis along with their applications in clinical research. First, we and other colleagues have developed a new high-density oligonucleo-tide array of the human transcriptome for high-throughput and cost-efficient analysis of patient samples in clinical studies. This array allows comprehensive examination of gene expression and genome-wide identification of alternative splicing, and also pro-vides assays for coding SNP detection and non-coding transcripts. Compared with high-throughput mRNA sequencing technology, we show that this array is highly re-producible in estimating gene and exon expression, and sensitive in detecting expres-sion changes. In addition, the exon-exon junction feature of this array is shown to im-prove detection efficiency for mRNA alternative splicing when combined with an ap-propriate computational method. We implemented the use of this array in a multi-center clinical program and have obtained comparable levels of high quality and re-producible data. With low costs and high throughputs for sample processing, we antic-ipate that this array platform will have a wide range of applications in high-throughput clinical studies. Second, we investigated knowledge-based methods that utilize prior know-ledge from biology and medicine to improve analysis and interpretation of high-throughput genomic data. We have developed knowledge-based methods to enrich our prior knowledge, illustrate dynamic response to external stimulus, and identify distur-bances in cellular pathways by chemical exposure, as well as discover hidden biological signatures for the prediction of patient outcomes. Finally, we applied a knowledge-based approach in a large scale genomic study of trauma patients. Cooperating with clinical information, prior knowledge improved the interpretation of common and dif-ferential genomic response to injury, and provided efficient risk assessment for patient outcomes. The clinical and genomic data as well as analysis results in this trauma study were systematically organized and provided to research communities as new knowledge of traumatic injury. The microarray platform and knowledge-based methods presented in this thesis provide appropriate research tools for high-throughput translational medicine in a large clinical setting. This thesis is expected to advance understanding and treatment for dis-eases, and finally, improve public health.
Book Synopsis Preprocessing Algorithms and Software for Genomic Studies with High-throughput Sequencing Data by : Ilya Y. Zhbannikov
Download or read book Preprocessing Algorithms and Software for Genomic Studies with High-throughput Sequencing Data written by Ilya Y. Zhbannikov and published by . This book was released on 2015 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: DNA sequencing technologies address problems, the solutions of which were not possible before, such as whole genome sequencing or microbial community characterization without pre-cultivation. Current High-Throughput Sequencing (HTS) techniques allow genomic studies in small labs as well as in large genomic centers. Together with modern computational software, HTS becomes a powerful tool, which allows researchers to answer important biological questions in novel ways. Despite the advantages of modern HTS technologies, large amounts of data and accompanying noise in HTS library confound bioinformatic analysis. Data preprocessing is needed in order to prepare data for subsequent analysis. Data preprocessing includes noise removal as well as techniques such as data reduction. In this dissertation I present a set of software tools that may be used in genomic studies in order to prepare HTS data for subsequent bioinformatic analysis. The first two chapters in this dissertation describe preprocessing tools developed for data denoising. In the last two chapters I explore the use of multiple genomic markers in 16S data analysis with a meta-amplicon analysis algorithm, which facilitates usage of all the information that can be obtained with 16S amplicon sequencing. Meta-amplicon analysis represents improvements on current methods used to characterize bacterial composition and community structure.
Book Synopsis Field Guidelines for Genetic Experimental Designs in High-Throughput Sequencing by : Ana M. Aransay
Download or read book Field Guidelines for Genetic Experimental Designs in High-Throughput Sequencing written by Ana M. Aransay and published by Springer. This book was released on 2016-06-02 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: High throughput sequencing (HTS) technologies have conquered the genomics and epigenomics worlds. The applications of HTS methods are wide, and can be used to sequence everything from whole or partial genomes, transcriptomes, non-coding RNAs, ribosome profiling, to single-cell sequencing. Having such diversity of alternatives, there is a demand for information by research scientists without experience in HTS that need to choose the most suitable methodology or combination of platforms and to define their experimental designs to achieve their specific objectives. Field Guidelines for Genetic Experimental Designs in High-Throughput Sequencing aims to collect in a single volume all aspects that should be taken into account when HTS technologies are being incorporated into a research project and the reasons behind them. Moreover, examples of several successful strategies will be analyzed to make the point of the crucial features. This book will be of use to all scientist that are unfamiliar with HTS and want to incorporate such technologies to their research.
Book Synopsis Statistical Analysis of Next Generation Sequencing Data by : Somnath Datta
Download or read book Statistical Analysis of Next Generation Sequencing Data written by Somnath Datta and published by Springer. This book was released on 2014-07-03 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized medicine. About the editors: Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics. Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Association and has published research on a variety of topics in statistics, biology and bioinformatics.
Book Synopsis Next Generation Sequencing Technologies and Challenges in Sequence Assembly by : Sara El-Metwally
Download or read book Next Generation Sequencing Technologies and Challenges in Sequence Assembly written by Sara El-Metwally and published by Springer Science & Business. This book was released on 2014-04-19 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: The introduction of Next Generation Sequencing (NGS) technologies resulted in a major transformation in the way scientists extract genetic information from biological systems, revealing limitless insight about the genome, transcriptome and epigenome of any species. However, with NGS, came its own challenges that require continuous development in the sequencing technologies and bioinformatics analysis of the resultant raw data and assembly of the full length genome and transcriptome. Such developments lead to outstanding improvements of the performance and coverage of sequencing and improved quality for the assembled sequences, nevertheless, challenges such as sequencing errors, expensive processing and memory usage for assembly and sequencer specific errors remains major challenges in the field. This book aims to provide brief overviews the NGS field with special focus on the challenges facing the NGS field, including information on different experimental platforms, assembly algorithms and software tools, assembly error correction approaches and the correlated challenges.
Book Synopsis Genome-Scale Algorithm Design by : Veli Mäkinen
Download or read book Genome-Scale Algorithm Design written by Veli Mäkinen and published by Cambridge University Press. This book was released on 2015-05-07 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput sequencing has revolutionised the field of biological sequence analysis. Its application has enabled researchers to address important biological questions, often for the first time. This book provides an integrated presentation of the fundamental algorithms and data structures that power modern sequence analysis workflows. The topics covered range from the foundations of biological sequence analysis (alignments and hidden Markov models), to classical index structures (k-mer indexes, suffix arrays and suffix trees), Burrows–Wheeler indexes, graph algorithms and a number of advanced omics applications. The chapters feature numerous examples, algorithm visualisations, exercises and problems, each chosen to reflect the steps of large-scale sequencing projects, including read alignment, variant calling, haplotyping, fragment assembly, alignment-free genome comparison, transcript prediction and analysis of metagenomic samples. Each biological problem is accompanied by precise formulations, providing graduate students and researchers in bioinformatics and computer science with a powerful toolkit for the emerging applications of high-throughput sequencing.
Book Synopsis Advancing Development of Synthetic Gene Regulators by : Anandhakumar Chandran
Download or read book Advancing Development of Synthetic Gene Regulators written by Anandhakumar Chandran and published by Springer. This book was released on 2017-09-19 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on an “outside the box” notion by utilizing the powerful applications of next-generation sequencing (NGS) technologies in the interface of chemistry and biology. In personalized medicine, developing small molecules targeting a specific genomic sequence is an attractive goal. N-methylpyrrole (P)–N-methylimidazole (I) polyamides (PIPs) are a class of small molecule that can bind to the DNA minor groove. First, a cost-effective NGS (ion torrent platform)-based Bind-n-Seq was developed to identify the binding specificity of PIP conjugates in a randomized DNA library. Their biological influences rely primarily on selective DNA binding affinity, so it is important to analyze their genome-wide binding preferences. However, it is demanding to enrich specifically the small-molecule-bound DNA without chemical cross-linking or covalent binding in chromatinized genomes. Herein is described a method that was developed using high-throughput sequencing to map the differential binding sites and relative enriched regions of non-cross-linked SAHA-PIPs throughout the complex human genome. SAHA-PIPs binding motifs were identified and the genome-level mapping of SAHA-PIPs-enriched regions provided evidence for the differential activation of the gene network. A method using high-throughput sequencing to map the binding sites and relative enriched regions of alkylating PIP throughout the human genome was also developed. The genome-level mapping of alkylating the PIP-enriched region and the binding sites on the human genome identifies significant genomic targets of breast cancer. It is anticipated that this pioneering low-cost, high through-put investigation at the sequence-specific level will be helpful in understanding the binding specificity of various DNA-binding small molecules, which in turn will be beneficial for the development of small-molecule-based drugs targeting a genome-level sequence.
Book Synopsis Computational Tools for the Analysis of High-throughput Genome-scale Sequence Data by : David Adrian Lopez
Download or read book Computational Tools for the Analysis of High-throughput Genome-scale Sequence Data written by David Adrian Lopez and published by . This book was released on 2016 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: As high-throughput sequence data becomes increasingly used in a variety of fields, there is a growing need for computational tools that facilitate analyzing and interpreting the sequence data to extract biological meaning. To date, several computational tools have been developed to analyze raw and processed sequence data in a number of contexts. However, many of these tools primarily focus on well-studied, reference organisms, and in some cases, such as the visualization of molecular signatures in expression data, there is a scarcity or complete absence of tools. Furthermore, the compendium of genome-scale data in publicly accessible databases can be leveraged to inform new studies. The focus of this dissertation is the development of computational tools and methods to analyze high-throughput genome-scale sequence data, as well as applications in mammalian, algal, and bacterial systems. Chapter 1 introduces the challenges of analyzing high-throughput sequence data. Chapter 2 presents the Signature Visualization Tool (SaVanT), a framework to visualize molecular signatures in user-generated expression data on a sample-by-sample basis. This chapter demonstrates that SaVanT can use immune activation signatures to distinguish patients with different types of acute infections (influenza A and bacterial pneumonia), and determine the primary cell types underlying different leukemias (acute myeloid and acute lymphoblastic) and skin disorders. Chapter 3 describes the Algal Functional Annotation Tool, which biologically interprets large gene lists, such as those derived from differential expression experiments. This tool integrates data from several pathway, ontology, and protein domain databases and performs enrichment testing on gene lists for several algal genomes. Chapter 4 describes a survey of the Chlamydomonas reinhardtii transcriptome and methylome across various stages of its sexual life cycle. This chapter discusses the identification and function of 361 gamete-specific and 627 zygote-specific genes, the first base-resolution methylation map of C. reinhardtii, and the changes in chloroplast methylation throughout key stages of its life cycle. Chapter 5 presents a comparative genomics approach to identifying previously uncharacterized bacterial microcompartment (BMC) proteins. Based on genomic proximity of genes in 131 fully-sequenced bacterial genomes, this chapter describes new putative microcompartments and their function.
Book Synopsis Computational Methods for Next Generation Sequencing Data Analysis by : Ion Mandoiu
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 2016-09-12 with total page 464 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.
Book Synopsis Next Generation Sequencing and Data Analysis by : Melanie Kappelmann-Fenzl
Download or read book Next Generation Sequencing and Data Analysis written by Melanie Kappelmann-Fenzl and published by Springer Nature. This book was released on 2021-05-04 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides step-by-step protocols and detailed explanations for RNA Sequencing, ChIP-Sequencing and Epigenetic Sequencing applications. The reader learns how to perform Next Generation Sequencing data analysis, how to interpret and visualize the data, and acquires knowledge on the statistical background of the used software tools. Written for biomedical scientists and medical students, this textbook enables the end user to perform and comprehend various Next Generation Sequencing applications and their analytics without prior understanding in bioinformatics or computer sciences.
Book Synopsis Genome Analysis: Current Procedures and Applications by : Maria S. Poptsova
Download or read book Genome Analysis: Current Procedures and Applications written by Maria S. Poptsova and published by Caister Academic Press. This book was released on 2019-04-28 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years there have been tremendous achievements made in DNA sequencing technologies and corresponding innovations in data analysis and bioinformatics that have revolutionized the field of genome analysis.In this book, an impressive array of expert authors highlight and review current advances in genome analysis. This volume provides an invaluable, up-to-date and comprehensive overview of the methods currently employed for next-generation sequencing (NGS) data analysis, highlights their problems and limitations, demonstrates the applications and indicates the developing trends in various fields of genome research. The first part of the book is devoted to the methods and applications that arose from, or were significantly advanced by, NGS technologies: the identification of structural variation from DNA-seq data; whole-transcriptome analysis and discovery of small interfering RNAs (siRNAs) from RNA-seq data; motif finding in promoter regions, enhancer prediction and nucleosome sequence code discovery from ChiP-Seq data; identification of methylation patterns in cancer from MeDIP-seq data; transposon identification in NGS data; metagenomics and metatranscriptomics; NGS of viral communities; and causes and consequences of genome instabilities. The second part is devoted to the field of RNA biology with the last three chapters devoted to computational methods of RNA structure prediction including context-free grammar applications.An essential book for everyone involved in sequence data analysis, next-generation sequencing, high-throughput sequencing, RNA structure prediction, bioinformatics and genome analysis.
Book Synopsis Complex Genome Analysis with High-throughput Sequencing Data by : Xin Li
Download or read book Complex Genome Analysis with High-throughput Sequencing Data written by Xin Li and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The genomes of most eukaryotes are large and complex. The presence of large amounts of non-coding sequences is a general property of the genomes of complex eukaryotes. High-throughput sequencing is increasingly important for the study of complex genomes. In this dissertation, we focus on two computational problems for high-throughput sequence data analysis, including detecting circular RNA and calling structural variations (especially deletions). Circular RNA (or circRNA) is a kind of non-coding RNA, which consists of a circular configuration through a typical 5' to 3' phosphodiester bond by non-canonical splicing. CircRNA was originally thought as the byproduct from the process of mis-splicing and considered to be of low abundance. Recently, however, circRNA is considered as a new class of functional molecule, and the importance of circRNA in gene regulation and their biological functions in some human diseases have started to be recognized. In this research work, we propose two algorithms to detect potential circRNA. In order to improve the performance of running time, we design an algorithm called CircMarker to find circRNA by creating k-mer table rather than conventional reads mapping. Furthermore, we develop an algorithm named CircDBG by taking advantage of the information from both reads and annotated genome to create de Bruijn graph for circRNA detection, which improves the accuracy and sensitivity. Structural variation (SV), which ranges from 50 bp to ~3 Mb in size, is an important type of genetic variations. Deletion is a type of SV in which a part of a chromosome or a sequence of DNA is lost during DNA replication. In this research work, we develop a new method called EigenDel for detecting genomic deletions. EigenDel first takes advantage of discordant read-pairs and clipped reads to get initial deletion candidates. Then, EigenDel clusters similar deletion candidates together and calls true deletions from each cluster by using unsupervised learning method. EigenDel outperforms other major methods in terms of balancing accuracy and sensitivity as well as reducing bias. Our results in this dissertation show that sequencing data can be used to study complex genomes by using effective computational approaches.
Book Synopsis Toward a More Accurate Genome by : William Jacob Benhardt Biesinger
Download or read book Toward a More Accurate Genome written by William Jacob Benhardt Biesinger and published by . This book was released on 2014 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-throughput sequencing enables basic and translational biology to query the mechanics of both life and disease at single-nucleotide resolution and with breadth that spans the genome. This revolutionary technology is a major tool in biomedical research, impacting our understanding of life's most basic mechanics and affecting human health and medicine. Unfortunately, this important technology produces very large, error-prone datasets that require substantial computational processing before experimental conclusions can be made. Since errors and hidden biases in the data may influence empirically-derived conclusions, accurate algorithms and models of the data are critical. This thesis focuses on the development of statistical models for high-throughput sequencing data which are capable of handling errors and which are built to reflect biological realities. First, we focus on increasing the fraction of the genome that can be reliably queried in biological experiments using high-throughput sequencing methods by expanding analysis into repeat regions of the genome. The method allows partial observation of the gene regulatory network topology through identification of transcription factor binding sites using Chromatin Immunoprecipitation followed by high-throughput sequencing (ChIP-seq). Binding site clustering, or "peak-calling", can be frustrated by the complex, repetitive nature of genomes. Traditionally, these regions are censored from any interpretation, but we re-enable their interpretation using a probabilistic method for realigning problematic DNA reads. Second, we leverage high-throughput sequencing data for the empirical discovery of underlying epigenetic cell state, enabled through analysis of combinations of histone marks. We use a novel probabilistic model to perform spatial and temporal clustering of histone marks and capture mark combinations that correlate well with cell activity. A first in epigenetic modeling with high-throughput sequencing data, we not only pool information across cell types, but directly model the relationship between them, improving predictive power across several datasets. Third, we develop a scalable approach to genome assembly using high-throughput sequencing reads. While several assembly solutions exist, most don't scale well to large datasets, requiring computers with copious memory to assemble large genomes. Throughput continues to increase and the large datasets available today and in the near future will require truly scalable methods. We present a promising distributed method for genome assembly which distributes the de Bruijn graph across many computers and seamlessly spills to disk when main memory is insufficient. We also show novel graph cleaning algorithms which should handle increased errors from large datasets better than traditional graph structure-based cleaning. High-throughput sequencing plays an important role in biomedical research, and has already affected human health and medicine. Future experimental procedures will continue to rely on statistical methods to provide crucial error and bias correction, in addition to modeling expected outcomes. Thus, further development of robust statistical models is critical to the future high-throughput sequencing, ensuring a strong foundation for correct biological conclusions.
Book Synopsis Practical Guide to ChIP-seq Data Analysis by : Borbala Mifsud
Download or read book Practical Guide to ChIP-seq Data Analysis written by Borbala Mifsud and published by CRC Press. This book was released on 2018-10-26 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: Chromatin immunoprecipitation sequencing (ChIP-seq), which maps the genome-wide localization patterns of transcription factors and epigenetic marks, is among the most widely used methods in molecular biology. Practical Guide to ChIP-seq Data Analysis will guide readers through the steps of ChIP-seq analysis: from quality control, through peak calling, to downstream analyses. It will help experimental biologists to design their ChIP-seq experiments with the analysis in mind, and to perform the basic analysis steps themselves. It also aims to support bioinformaticians to understand how the data is generated, what the sources of biases are, and which methods are appropriate for different analyses.
Book Synopsis Next Generation Sequencing by : Jerzy Kulski
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.
