[PDF] Analysis Of Next Gen Sequencing Data With Excess Of Zeros eBook

Author : Somnath Datta
Publisher : Springer
Page : 438 pages
File Size : 45,27 MB
Release : 2014-07-03
Category : Medical
ISBN : 3319072129

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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.

Author : Xinkun Wang
Publisher : CRC Press
Page : 252 pages
File Size : 31,28 MB
Release : 2016-04-06
Category : Mathematics
ISBN : 1482217899

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A Practical Guide to the Highly Dynamic Area of Massively Parallel SequencingThe development of genome and transcriptome sequencing technologies has led to a paradigm shift in life science research and disease diagnosis and prevention. Scientists are now able to see how human diseases and phenotypic changes are connected to DNA mutation, polymorphi

Author : Mourad Elloumi
Publisher : Springer
Page : 356 pages
File Size : 35,78 MB
Release : 2017-09-18
Category : Computers
ISBN : 3319598260

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The 14 contributed chapters in this book survey the most recent developments in high-performance algorithms for NGS data, offering fundamental insights and technical information specifically on indexing, compression and storage; error correction; alignment; and assembly. The book will be of value to researchers, practitioners and students engaged with bioinformatics, computer science, mathematics, statistics and life sciences.

Author : Ion Mandoiu
Publisher : John Wiley & Sons
Page : 460 pages
File Size : 41,49 MB
Release : 2016-10-03
Category : Computers
ISBN : 1118169484

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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.

Author : Kuo Ping Chiu
Publisher : Bentham Science Publishers
Page : 160 pages
File Size : 44,71 MB
Release : 2015-11-04
Category : Science
ISBN : 1681080923

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Nucleic acid sequencing techniques have enabled researchers to determine the exact order of base pairs - and by extension, the information present - in the genome of living organisms. Consequently, our understanding of this information and its link to genetic expression at molecular and cellular levels has lead to rapid advances in biology, genetics, biotechnology and medicine. Next-Generation Sequencing and Sequence Data Analysis is a brief primer on DNA sequencing techniques and methods used to analyze sequence data. Readers will learn about recent concepts and methods in genomics such as sequence library preparation, cluster generation for PCR technologies, PED sequencing, genome assembly, exome sequencing, transcriptomics and more. This book serves as a textbook for students undertaking courses in bioinformatics and laboratory methods in applied biology. General readers interested in learning about DNA sequencing techniques may also benefit from the simple format of information presented in the book.

Author : Jerzy Kulski
Publisher : BoD – Books on Demand
Page : 466 pages
File Size : 34,76 MB
Release : 2016-01-14
Category : Medical
ISBN : 9535122401

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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.

Author : Melanie Kappelmann-Fenzl
Publisher : Springer Nature
Page : 218 pages
File Size : 17,49 MB
Release : 2021-05-04
Category : Science
ISBN : 3030624900

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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.

Author : Ruochen Jiang
Publisher :
Page : 175 pages
File Size : 19,99 MB
Release : 2021
Category :
ISBN :

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Next generation sequencing (NGS) has revolutionized biomedical research and has a broad impact and applications. Since its advent around 15 years ago, this high scalable DNA sequencing technology has generated numerous biological data with new features and brought new challenges to data analysis. For example, researchers utilize RNA sequencing (RNA-seq) technology to more accurately quantify the gene expression levels. However, the NGS technology involves many processing steps and technical variations when measuring the expression values in the biological samples. In other words, the NGS data researchers observed could be biased due to the randomness and constraints in the NGS technology. This dissertation will mainly focus on microbiome sequencing data and single-cell RNA-seq (scRNA-seq) data. Both of them are highly sparse matrix-form count data. The zeros could either be biological or non-biological, and the high sparsity in the data have brought challenges to data analysis. Missing data imputation problem has been studied in statistics and social science as the survey data often experience non-response to some of the survey questions and those unresponded questions will be marked as "NA" or missing values in the data. Imputation methods are used to provide a sophisticated guess for the missing values, and the purpose is to avoid discarding the collected samples and for the ease of using the state-of-the-art statistical methods. In machine learning, the famous Netflix data challenge regarding film recommendation system also falls into the missing data imputation problem category. Netflix wants to find a way to predict users' fondness of the movies they have not watched. The potential scores these users would give to the unwatched films are regarded as missing values in the data. NGS data imputation problem is different from the previous two cases in that the missing values in the NGS data are not so well-defined. The zeros in the NGS data could either come from the biological origin (should not be regarded as missing values) or non-biological origin (due to the limitation of the sequencing technology and should be regarded as missing values). The size (number of samples and features) of the NGS matrix data is usually larger than the size of survey data but smaller than the size of the recommendation system data. In addition, in most cases, the percentage of missing values in the survey data is less than the percentage of zeros in the NGS data, and the missing values in the film recommendation system data have the highest percentage (> 99.9%). As a result, the commonly used missing data imputation methods in statistics and machine learning are not directly applicable to NGS data. In recent years, numerous imputation methods have been proposed to deal with the highly sparse scRNA-seq data. In light of this, this dissertation aims to address two questions. First, the microbiome sequencing data, having additional information comparing to the scRNA-seq data, lacks an imputation method. Secondly, whether to use imputation or not in scRNA-seq data analysis is still a controversial problem. The first part of this dissertation focuses on the first imputation method developed for the microbiome sequencing data: mbImpute. Microbiome studies have gained increased attention since many discoveries revealed connections between human microbiome compositions and diseases. A critical challenge in microbiome data analysis is the existence of many non-biological zeros, which distort taxon abundance distributions, complicate data analysis, and jeopardize the reliability of scientific discoveries. To address this issue, we propose the first imputation method for microbiome data---mbImpute---to identify and recover likely non-biological zeros by borrowing information jointly from similar samples, similar taxa, and optional metadata including sample covariates and taxon phylogeny. Comprehensive simulations verify that mbImpute achieves better imputation accuracy under multiple metrics, compared with five state-of-the-art imputation methods designed for non-microbiome data. In real data applications, we demonstrate that mbImpute improves the power of identifying disease-related taxa from microbiome data of type 2 diabetes and colorectal cancer, and mbImpute preserves non-zero distributions of taxa abundances. The second part of this dissertation focuses on how to deal with high sparsity in the scRNA-seq data. ScRNA-seq technologies have revolutionized biomedical sciences by enabling genome-wide profiling of gene expression levels at an unprecedented single-cell resolution. A distinct characteristic of scRNA-seq data is the vast proportion of zeros unseen in bulk RNA-seq data. Researchers view these zeros differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as false signals or missing data to be corrected. As a result, the scRNA-seq field faces much controversy regarding how to handle zeros in data analysis. We first discuss the sources of biological and non-biological zeros in scRNA-seq data. Second, we evaluate the impacts of non-biological zeros on cell clustering and differential gene expression analysis. Third, we summarize the advantages, disadvantages, and suitable users of three input data types: observed counts, imputed counts, and binarized counts and evaluate the performance of downstream analysis on these three input data types. Finally, we discuss the open questions regarding non-biological zeros, the need for benchmarking, and the importance of transparent analysis.