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Computational Methods for Next Generation Sequencing Data Analysis
Author: Ion Mandoiu
Publisher: John Wiley & Sons
Published: 2016-10-03
Total Pages: 460
ISBN-13: 1118169484
DOWNLOAD EBOOK →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.
Computational Methods for the Discovery and Analysis of Genes and Other Functional DNA Sequences
Author: Cyriac Kandoth
Publisher:
Published: 2010
Total Pages: 126
ISBN-13:
DOWNLOAD EBOOK →"The need for automating genome analysis is a result of the tremendous amount of genomic data. As of today, a high-throughput DNA sequencing machine can run millions of sequencing reactions in parallel, and it is becoming faster and cheaper to sequence the entire genome of an organism. Public databases containing genomic data are growing exponentially, and hence the rise in demand for intuitive automated methods of DNA analysis and subsequent gene identification. However, the complexity of gene organization makes automation a challenging task, and smart algorithm design and parallelization are necessary to perform accurate analyses in reasonable amounts of time. This work describes two such automated methods for the identification of novel genes within given DNA sequences. The first method utilizes negative selection patterns as an evolutionary rationale for the identification of additional members of a gene family. As input it requires a known protein coding gene in that family. The second method is a massively parallel data mining algorithm that searches a whole genome for inverted repeats (palindromic sequences) and identifies potential precursors of non-coding RNA genes. Both methods were validated successfully on the fully sequenced and well studied plant species, Arabidopsis thaliana"--Abstract, leaf iv.
Computational Methods for Fast and Accurate DNA Fragment Assembly
Automated DNA Sequencing and Analysis
Author: Mark D. Adams
Publisher: Elsevier
Published: 1994-06-29
Total Pages: 400
ISBN-13: 9780127170107
DOWNLOAD EBOOK →A timely book for DNA researchers, Automated DNA Sequencing and Analysis reviews and assesses the state of the art of automated DNA sequence analysis-from the construction of clone libraries to the developmentof laboratory and community databases. It presents the methodologies and strategies of automated DNA sequence analysis in a way that allows them to be compared and contrasted. By taking a broad view of the process of automated sequence analysis, the present volume bridges the gap between the protocols supplied with instrument and reaction kits and the finalized data presented in the research literature. It will be an invaluable aid to both small laboratories that are interested in taking maximum advantageof automated sequence resources and to groups pursuing large-scale cDNA and genomic sequencing projects. The field of automation in DAN sequencing and analysis is rapidly moving, this book fulfils those needs, reviews the history of the art and provides pointers to future development.
High Performance Computational Methods for Biological Sequence Analysis
Author: Tieng K. Yap
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 219
ISBN-13: 1461313910
DOWNLOAD EBOOK →High Performance Computational Methods for Biological Sequence Analysis presents biological sequence analysis using an interdisciplinary approach that integrates biological, mathematical and computational concepts. These concepts are presented so that computer scientists and biomedical scientists can obtain the necessary background for developing better algorithms and applying parallel computational methods. This book will enable both groups to develop the depth of knowledge needed to work in this interdisciplinary field. This work focuses on high performance computational approaches that are used to perform computationally intensive biological sequence analysis tasks: pairwise sequence comparison, multiple sequence alignment, and sequence similarity searching in large databases. These computational methods are becoming increasingly important to the molecular biology community allowing researchers to explore the increasingly large amounts of sequence data generated by the Human Genome Project and other related biological projects. The approaches presented by the authors are state-of-the-art and show how to reduce analysis times significantly, sometimes from days to minutes. High Performance Computational Methods for Biological Sequence Analysis is tremendously important to biomedical science students and researchers who are interested in applying sequence analyses to their studies, and to computational science students and researchers who are interested in applying new computational approaches to biological sequence analyses.
DNA Sequencing Strategies
Author: Wilhelm Ansorge
Publisher: Wiley-Liss
Published: 1997
Total Pages: 220
ISBN-13:
DOWNLOAD EBOOK →This outstanding lab bench reference to the technology of DNA sequencing offers a collection of concise sequencing strategies and cloning protocols. Concentrates on the most up-to-the-minute automated methods and advanced approaches. Preparing DNA for sequencing, sequencing single- doubled-stranded DNA and their variations, how to optimise the primers used, preparation of DNA sequencing gels and the actual collection of results, labelling of DNA fragments for sequencing and data analysis are among the topics covered.
DNA Sequencing Protocols
Author: Annette M. Griffin
Publisher: Springer Science & Business Media
Published: 2008-02-02
Total Pages: 386
ISBN-13: 1592595103
DOWNLOAD EBOOK →The purpose of DNA Sequencing Protocols is to provide detailed practical procedures for the widest range of DNA sequencing meth ods, and we believe that all the vanguard techniques now being applied in this fast-evolving field are comprehensively covered. Sequencing technology has advanced at a phenomenal rate since the original methods were first described in the late 1970s and there is now a huge variety of strategies and methods that can be employed to determine the sequence of any DNA of interest. More recently, a large number of new and innovative sequencing techniques have been developed, including the use of such novel polymerases as Tag poly merase and Sequenase, the harnessing of PCR technology for linear amplification (cycle) sequencing, and the advent of automated DNA sequencers. DNA sequencing is surely one of the most important techniques in the molecular biology laboratory. Sequence analysis is providing an increasingly useful approach to the characterization of biological systems, and major multinational projects are already underway to map and sequence the entire genome of organisms, such as Escherichia coli, Saccharomyces cerevisiae, Caenorhabditis elegans, and Homo sapiens. Most scientists recognize the importance of DNA sequence data and perceive DNA sequencing as a valuable and indispensable aspect of their work. Recent technological advances, especially in the area of automated sequencing, have removed much of the drudg ery that was formerly associated with the technique, whereas innova tive computer software has greatly simplified the analysis and manipulation of sequence data.
Automation in Proteomics and Genomics
Author: Gil Alterovitz
Publisher: John Wiley & Sons
Published: 2009-03-16
Total Pages: 340
ISBN-13: 9780470741177
DOWNLOAD EBOOK →In the last decade DNA sequencing costs have decreased over a magnitude, largely because of increasing throughput by incremental advances in tools, technologies and process improvements. Further cost reductions in this and in related proteomics technologies are expected as a result of the development of new high-throughput techniques and the computational machinery needed to analyze data generated. Automation in Proteomics & Genomics: An Engineering Case-Based Approach describes the automation technology currently in the areas of analysis, design, and integration, as well as providing basic biology concepts behind proteomics and genomics. The book also discusses the current technological limitations that can be viewed as an emerging market rather than a research bottleneck. Topics covered include: molecular biology fundamentals: from ‘blueprint’ (DNA) to ‘task list’ (RNA) to ‘molecular machine’ (protein); proteomics methods and technologies; modelling protein networks and interactions analysis via automation: DNA sequencing; microarrays and other parallelization technologies; protein characterization and identification; protein interaction and gene regulatory networks design via automation: DNA synthesis; RNA by design; building protein libraries; synthetic networks integration: multiple modalities; computational and experimental methods; trends in automation for genomics and proteomics new enabling technologies and future applications Automation in Proteomics & Genomics: An Engineering Case-Based Approach is an essential guide to the current capabilities and challenges of high-throughput analysis of genes and proteins for bioinformaticians, engineers, chemists, and biologists interested in developing a cross-discipline problem-solving based approach to systems biology.
Computational Methods for the Analysis of Next Generation Sequencing Data
Author: Wei Wang
Publisher:
Published: 2014
Total Pages: 186
ISBN-13:
DOWNLOAD EBOOK →Recently, next generation sequencing (NGS) technology has emerged as a powerful approach and dramatically transformed biomedical research in an unprecedented scale. NGS is expected to replace the traditional hybridization-based microarray technology because of its affordable cost and high digital resolution. Although NGS has significantly extended the ability to study the human genome and to better understand the biology of genomes, the new technology has required profound changes to the data analysis. There is a substantial need for computational methods that allow a convenient analysis of these overwhelmingly high-throughput data sets and address an increasing number of compelling biological questions which are now approachable by NGS technology. This dissertation focuses on the development of computational methods for NGS data analyses. First, two methods are developed and implemented for detecting variants in analysis of individual or pooled DNA sequencing data. SNVer formulates variant calling as a hypothesis testing problem and employs a binomial-binomial model to test the significance of observed allele frequency by taking account of sequencing error. SNVerGUI is a GUI-based desktop tool that is built upon the SNVer model to facilitate the main users of NGS data, such as biologists, geneticists and clinicians who often lack of the programming expertise. Second, collapsing singletons strategy is explored for associating rare variants in a DNA sequencing study. Specifically, a gene-based genome-wide scan based on singleton collapsing is performed to analyze a whole genome sequencing data set, suggesting that collapsing singletons may boost signals for association studies of rare variants in sequencing study. Third, two approaches are proposed to address the 3'UTR switching problem. PolyASeeker is a novel bioinformatics pipeline for identifying polyadenylation cleavage sites from RNA sequencing data, which helps to enhance the knowledge of alternative polyadenylation mechanisms and their roles in gene regulation. A change-point model based on a likelihood ratio test is also proposed to solve such problem in analysis of RNA sequencing data. To date, this is the first method for detecting 3'UTR switching without relying on any prior knowledge of polyadenylation cleavage sites.
