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Statistical Analysis Of Next Generation Sequencing Data

Author: Somnath Datta
Publisher: Springer
ISBN: 3319072129
Size: 21.91 MB
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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.

Frontiers In Massive Data Analysis

Author: National Research Council
Publisher: National Academies Press
ISBN: 0309287812
Size: 20.21 MB
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Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale--terabytes and petabytes--is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge--from computer science, statistics, machine learning, and application disciplines--that must be brought to bear to make useful inferences from massive data.

Frontiers In Computational And Systems Biology

Author: Jianfeng Feng
Publisher: Springer Science & Business Media
ISBN: 9781849961967
Size: 14.17 MB
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Biological and biomedical studies have entered a new era over the past two decades thanks to the wide use of mathematical models and computational approaches. A booming of computational biology, which sheerly was a theoretician’s fantasy twenty years ago, has become a reality. Obsession with computational biology and theoretical approaches is evidenced in articles hailing the arrival of what are va- ously called quantitative biology, bioinformatics, theoretical biology, and systems biology. New technologies and data resources in genetics, such as the International HapMap project, enable large-scale studies, such as genome-wide association st- ies, which could potentially identify most common genetic variants as well as rare variants of the human DNA that may alter individual’s susceptibility to disease and the response to medical treatment. Meanwhile the multi-electrode recording from behaving animals makes it feasible to control the animal mental activity, which could potentially lead to the development of useful brain–machine interfaces. - bracing the sheer volume of genetic, genomic, and other type of data, an essential approach is, ?rst of all, to avoid drowning the true signal in the data. It has been witnessed that theoretical approach to biology has emerged as a powerful and st- ulating research paradigm in biological studies, which in turn leads to a new - search paradigm in mathematics, physics, and computer science and moves forward with the interplays among experimental studies and outcomes, simulation studies, and theoretical investigations.

Advances In The Understanding Of Biological Sciences Using Next Generation Sequencing Ngs Approaches

Author: Gaurav Sablok
Publisher: Springer
ISBN: 3319171577
Size: 72.75 MB
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Provides a global view of the recent advances in the biological sciences and the adaption of the pathogen to the host plants revealed using NGS. Molecular Omic’s is now a major driving force to learn the adaption genetics and a great challenge to the scientific community, which can be resolved through the application of the NGS technologies. The availability of complete genome sequences, the respective model species for dicot and monocot plant groups, presents a global opportunity to delineate the identification, function and the expression of the genes, to develop new tools for the identification of the new genes and pathway identification. Genome-wide research tools, resources and approaches such as data mining for structural similarities, gene expression profiling at the DNA and RNA level with rapid increase in available genome sequencing efforts, expressed sequence tags (ESTs), RNA-seq, gene expression profiling, induced deletion mutants and insertional mutants, and gene expression knock-down (gene silencing) studies with RNAi and microRNAs have become integral parts of plant molecular omic’s. Molecular diversity and mutational approaches present the first line of approach to unravel the genetic and molecular basis for several traits, QTL related to disease resistance, which includes host approaches to combat the pathogens and to understand the adaptation of the pathogen to the plant host. Using NGS technologies, understanding of adaptation genetics towards stress tolerance has been correlated to the epigenetics. Naturally occurring allelic variations, genome shuffling and variations induced by chemical or radiation mutagenesis are also being used in functional genomics to elucidate the pathway for the pathogen and stress tolerance and is widely illustrated in demonstrating the identification of the genes responsible for tolerance in plants, bacterial and fungal species.

Game Changer Next Generation Sequencing And Its Impact On Food Microbiology

Author: Jennifer Ronholm
Publisher: Frontiers Media SA
ISBN: 2889454630
Size: 53.41 MB
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Advances in next-generation sequencing technologies (NGS) are revolutionizing the field of food microbiology. Microbial whole genome sequencing (WGS) can provide identification, characterization, and subtyping of pathogens for epidemiological investigations at a level of precision previously not possible. This allows for connections and source attribution to be inferred between related isolates that may be overlooked by traditional techniques. The archiving and global sharing of genome sequences allow for retrospective analysis of virulence genes, antimicrobial resistance markers, mobile genetic elements and other novel genes. The advent of high-throughput 16S rRNA amplicon sequencing, in combination with the advantages offered by massively parallel second-generation sequencing for metagenomics, enable intensive studies on the microbiomes of food products and the impact of foods on the human microbiome. These studies may one day lead to the development of reliable culture-independent methods for food monitoring and surveillance. Similarly, RNA-seq has provided insights into the transcriptomes and hence the behaviour of bacterial pathogens in food, food processing environments, and in interaction with the host at a resolution previously not achieved through the use of microarrays and/or RT-PCR. The vast un-tapped potential applications of NGS along with its rapidly declining costs, give this technology the ability to contribute significantly to consumer protection, global trade facilitation, and increased food safety and security. Despite the rapid advances, challenges remain. How will NGS data be incorporated into our existing global food safety infrastructure? How will massive NGS data be stored and shared globally? What bioinformatics solutions will be used to analyse and optimise these large data sets? This Research Topic discusses recent advances in the field of food microbiology made possible through the use of NGS.

Computational Methods For Next Generation Sequencing Data Analysis

Author: Ion Mandoiu
Publisher: John Wiley & Sons
ISBN: 1118169484
Size: 72.43 MB
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Aiming to foster future collaborations between researchers in algorithms, bioinformatics, and molecular biology, this book serves as an up-to-date survey of the most important recent developments and computational challenges in various application areas of next-generation sequencing technologies. Offering helpful insight from renowned experts, the book covers topics such as NGS error correction, road mapping, variant detection and genotyping, characterization of structural variants with NGS, genome-assisted transcriptome reconstruction, small RNA analysis, and much more.

Primer To Analysis Of Genomic Data Using R

Author: Cedric Gondro
Publisher: Springer
ISBN: 3319144758
Size: 25.82 MB
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Through this book, researchers and students will learn to use R for analysis of large-scale genomic data and how to create routines to automate analytical steps. The philosophy behind the book is to start with real world raw datasets and perform all the analytical steps needed to reach final results. Though theory plays an important role, this is a practical book for graduate and undergraduate courses in bioinformatics and genomic analysis or for use in lab sessions. How to handle and manage high-throughput genomic data, create automated workflows and speed up analyses in R is also taught. A wide range of R packages useful for working with genomic data are illustrated with practical examples. The key topics covered are association studies, genomic prediction, estimation of population genetic parameters and diversity, gene expression analysis, functional annotation of results using publically available databases and how to work efficiently in R with large genomic datasets. Important principles are demonstrated and illustrated through engaging examples which invite the reader to work with the provided datasets. Some methods that are discussed in this volume include: signatures of selection, population parameters (LD, FST, FIS, etc); use of a genomic relationship matrix for population diversity studies; use of SNP data for parentage testing; snpBLUP and gBLUP for genomic prediction. Step-by-step, all the R code required for a genome-wide association study is shown: starting from raw SNP data, how to build databases to handle and manage the data, quality control and filtering measures, association testing and evaluation of results, through to identification and functional annotation of candidate genes. Similarly, gene expression analyses are shown using microarray and RNAseq data. At a time when genomic data is decidedly big, the skills from this book are critical. In recent years R has become the de facto tool for analysis of gene expression data, in addition to its prominent role in analysis of genomic data. Benefits to using R include the integrated development environment for analysis, flexibility and control of the analytic workflow. Included topics are core components of advanced undergraduate and graduate classes in bioinformatics, genomics and statistical genetics. This book is also designed to be used by students in computer science and statistics who want to learn the practical aspects of genomic analysis without delving into algorithmic details. The datasets used throughout the book may be downloaded from the publisher’s website./p

Rna Seq Data Analysis

Author: Eija Korpelainen
Publisher: CRC Press
ISBN: 1466595019
Size: 32.18 MB
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The State of the Art in Transcriptome Analysis RNA sequencing (RNA-seq) data offers unprecedented information about the transcriptome, but harnessing this information with bioinformatics tools is typically a bottleneck. RNA-seq Data Analysis: A Practical Approach enables researchers to examine differential expression at gene, exon, and transcript levels and to discover novel genes, transcripts, and whole transcriptomes. Balanced Coverage of Theory and Practice Each chapter starts with theoretical background, followed by descriptions of relevant analysis tools and practical examples. Accessible to both bioinformaticians and nonprogramming wet lab scientists, the examples illustrate the use of command-line tools, R, and other open source tools, such as the graphical Chipster software. The Tools and Methods to Get Started in Your Lab Taking readers through the whole data analysis workflow, this self-contained guide provides a detailed overview of the main RNA-seq data analysis methods and explains how to use them in practice. It is suitable for researchers from a wide variety of backgrounds, including biology, medicine, genetics, and computer science. The book can also be used in a graduate or advanced undergraduate course.

Resampling Methods For Dependent Data

Author: S. N. Lahiri
Publisher: Springer Science & Business Media
ISBN: 147573803X
Size: 11.80 MB
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By giving a detailed account of bootstrap methods and their properties for dependent data, this book provides illustrative numerical examples throughout. The book fills a gap in the literature covering research on re-sampling methods for dependent data that has witnessed vigorous growth over the last two decades but remains scattered in various statistics and econometrics journals. It can be used as a graduate level text and also as a research monograph for statisticians and econometricians.

Molecular Evolution

Author: Ziheng Yang
Publisher: Oxford University Press
ISBN: 0199602603
Size: 20.39 MB
Format: PDF
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"Studies of evolution at the molecular level have experienced phenomenal growth in the last few decades, due to rapid accumulation of genetic sequence data, improved computer hardware and software, and the development of sophisticated analytical methods. The flood of genomic data has generated an acute need for powerful statistical methods and efficient computational algorithms to enable their effective analysis and interpretation. This advanced textbook is aimed at graduate level students and professional researchers (both empiricists and theoreticians) in the fields of bioinformatics and computational biology, statistical genomics, evolutionary biology, molecular systematics, and population genetics. It will also be of relevance and use to a wider audience of applied statisticians, mathematicians, and computer scientists working in computational biology."--back cover.