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Computational Methods For The Analysis Of Next Generation Sequencing Data

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Computational Methods For Next Generation Sequencing Data Analysis

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Author : Ion Mandoiu
language : en
Publisher: John Wiley & Sons
Release Date : 2016-10-03

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 2016-10-03 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.

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 The Analysis Of Next Generation Sequencing Data

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Author : Wei Wang
language : en
Publisher:
Release Date : 2014

Computational Methods For The Analysis Of Next Generation Sequencing Data written by Wei Wang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.

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.

Advances In Complex Data Modeling And Computational Methods In Statistics

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Author : Anna Maria Paganoni
language : en
Publisher: Springer
Release Date : 2014-11-04

Advances In Complex Data Modeling And Computational Methods In Statistics written by Anna Maria Paganoni and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-04 with Mathematics categories.

The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed.

Computational Methods For Microbiome Analysis Volume 2

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Author : Setubal
language : en
Publisher: Frontiers Media SA
Release Date : 2023-01-04

Computational Methods For Microbiome Analysis Volume 2 written by Setubal and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-04 with Science categories.

Statistical Analysis Of Next Generation Sequencing Data

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Author : Somnath Datta
language : en
Publisher: Springer
Release Date : 2014-07-03

Statistical Analysis Of Next Generation Sequencing Data written by Somnath Datta and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-03 with Medical categories.

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.

Computational Methods For Precision Oncology

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Author : Alessandro Laganà
language : en
Publisher: Springer Nature
Release Date : 2022-03-01

Computational Methods For Precision Oncology written by Alessandro Laganà and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-01 with Medical categories.

Precision medicine holds great promise for the treatment of cancer and represents a unique opportunity for accelerated development and application of novel and repurposed therapeutic approaches. Current studies and clinical trials demonstrate the benefits of genomic profiling for patients whose cancer is driven by specific, targetable alterations. However, precision oncologists continue to be challenged by the widespread heterogeneity of cancer genomes and drug responses in designing personalized treatments. Chapters provide a comprehensive overview of the computational approaches, methods, and tools that enable precision oncology, as well as related biological concepts. Covered topics include genome sequencing, the architecture of a precision oncology workflow, and introduces cutting-edge research topics in the field of precision oncology. This book is intended for computational biologists, bioinformaticians, biostatisticians and computational pathologists working in precision oncology and related fields, including cancer genomics, systems biology, and immuno-oncology.

Biological Sequence Analysis

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Author : Richard Durbin
language : en
Publisher: Cambridge University Press
Release Date : 1998-04-23

Biological Sequence Analysis written by Richard Durbin and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-04-23 with Medical categories.

Presents up-to-date computer methods for analysing DNA, RNA and protein sequences.

Computational Methods For Understanding Complexity The Use Of Formal Methods In Biology

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Author : David A. Rosenblueth,
language : en
Publisher: Frontiers Media SA
Release Date : 2016-11-21

Computational Methods For Understanding Complexity The Use Of Formal Methods In Biology written by David A. Rosenblueth, and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-21 with categories.

The complexity of living organisms surpasses our unaided habilities of analysis. Hence, computational and mathematical methods are necessary for increasing our understanding of biological systems. At the same time, there has been a phenomenal recent progress allowing the application of novel formal methods to new domains. This progress has spurred a conspicuous optimism in computational biology. This optimism, in turn, has promoted a rapid increase in collaboration between specialists of biology with specialists of computer science. Through sheer complexity, however, many important biological problems are at present intractable, and it is not clear whether we will ever be able to solve such problems. We are in the process of learning what kind of model and what kind of analysis and synthesis techniques to use for a particular problem. Some existing formalisms have been readily used in biological problems, others have been adapted to biological needs, and still others have been especially developed for biological systems. This Research Topic has examples of cases (1) employing existing methods, (2) adapting methods to biology, and (3) developing new methods. We can also see discrete and Boolean models, and the use of both simulators and model checkers. Synthesis is exemplified by manual and by machine-learning methods. We hope that the articles collected in this Research Topic will stimulate new research.

Computational Techniques For Biological Sequence Analysis

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Author : Saiyed Umer
language : en
Publisher: CRC Press
Release Date : 2025-06-17

Computational Techniques For Biological Sequence Analysis written by Saiyed Umer and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-17 with Computers categories.

This book provides an overview of basic and advanced computational techniques for analyzing and understanding protein, RNA, and DNA sequences. It covers effective computing techniques for DNA and protein classifications, evolutionary and sequence information analysis, evolutionary algorithms, and ensemble algorithms. Furthermore, the book reviews the role of machine learning techniques, artificial intelligence, ensemble learning, and sequence-based features in predicting post-translational modifications in proteins, DNA methylation, and mRNA methylation, along with their functional implications. The book also discusses the prediction of protein–protein and protein–DNA interactions, protein structure, and function using computational methods. It also presents techniques for quantitative analysis of protein–DNA interactions and protein methylation and their involvement in gene regulation. Additionally, the use of nature-inspired algorithms to gain insights into gene regulatory mechanisms and metabolic pathways in human diseases is explored. This book acts as a useful reference for bioinformaticians and computational biologists working in the fields of molecular biology, genomics, and bioinformatics. Key Features: Reviews machine learning techniques for DNA sequence classification and protein structure prediction Discusses genetic algorithms for analyzing multiple sequence alignments and predicting protein–protein interaction sites Explores computational methods for quantitative analysis of protein–DNA interactions Examine the role of nature-inspired algorithms in understanding the gene regulation and metabolic pathways Covers evolutionary algorithms and sequence-based features in predicting post-translational modifications

Computational Methods For The Analysis Of Next Generation Sequencing Data

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