STATISTICAL ANALYSIS OF NEXT GENERATION SEQUENCING DATA FRONTIERS IN PROBABILITY AND THE STATISTICAL SCIENCES

Download Statistical Analysis Of Next Generation Sequencing Data Frontiers In Probability And The Statistical Sciences ebook PDF or Read Online books in PDF, EPUB, and Mobi Format. Click Download or Read Online button to STATISTICAL ANALYSIS OF NEXT GENERATION SEQUENCING DATA FRONTIERS IN PROBABILITY AND THE STATISTICAL SCIENCES book pdf for free now.

Statistical Analysis Of Next Generation Sequencing Data

Author : Somnath Datta
ISBN : 9783319072128
Genre : Medical
File Size : 82.45 MB
Format : PDF, Mobi
Download : 325
Read : 217

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.
Category: Medical

Computational Methods For Next Generation Sequencing Data Analysis

Author : Ion Mandoiu
ISBN : 9781119272175
Genre : Computers
File Size : 30.88 MB
Format : PDF, Mobi
Download : 486
Read : 1291

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.
Category: Computers

Frontiers In Massive Data Analysis

Author : National Research Council
ISBN : 9780309287814
Genre : Mathematics
File Size : 36.60 MB
Format : PDF, ePub, Mobi
Download : 563
Read : 179

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.
Category: Mathematics

Handbook Of Statistical Systems Biology

Author : Michael Stumpf
ISBN : 9781119952046
Genre : Science
File Size : 63.10 MB
Format : PDF, ePub
Download : 574
Read : 1174

Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the unknown parameters. This book: Provides a comprehensive account of inference techniques in systems biology. Introduces classical and Bayesian statistical methods for complex systems. Explores networks and graphical modeling as well as a wide range of statistical models for dynamical systems. Discusses various applications for statistical systems biology, such as gene regulation and signal transduction. Features statistical data analysis on numerous technologies, including metabolic and transcriptomic technologies. Presents an in-depth presentation of reverse engineering approaches. Provides colour illustrations to explain key concepts. This handbook will be a key resource for researchers practising systems biology, and those requiring a comprehensive overview of this important field.
Category: Science

Handbook Of Medical Statistics

Author : Fang Ji-qian
ISBN : 9789813148970
Genre : Medical
File Size : 74.57 MB
Format : PDF, Mobi
Download : 880
Read : 825

This unique volume focuses on the "tools" of medical statistics. It contains over 500 concepts or methods, all of which are explained very clearly and in detail. Each chapter focuses on a specific field and its applications. There are about 20 items in each chapter with each item independent of one another and explained within one page (plus references). The structure of the book makes it extremely handy for solving targeted problems in this area. As the goal of the book is to encourage students to learn more combinatorics, every effort has been made to provide them with a not only useful, but also enjoyable and engaging reading. This handbook plays the role of "tutor" or "advisor" for teaching and further learning. It can also be a useful source for "MOOC-style teaching".
Category: Medical

Music And Disorders Of Consciousness Emerging Research Practice And Theory

Author : Wendy L. Magee
ISBN : 9782889450992
Genre :
File Size : 69.74 MB
Format : PDF, Mobi
Download : 821
Read : 1331

Music processing in severely brain-injured patients with disorders of consciousness has been an emergent field of interest for over 30 years, spanning the disciplines of neuroscience, medicine, the arts and humanities. Disorders of consciousness (DOC) is an umbrella term that encompasses patients who present with disorders across a continuum of consciousness including people who are in a coma, in vegetative state (VS)/have unresponsive wakefulness syndrome (UWS), and in minimally conscious state (MCS). Technological developments in recent years, resulting in improvements in medical care and technologies, have increased DOC population numbers, the means for investigating DOC, and the range of clinical and therapeutic interventions under validation. In neuroimaging and behavioural studies, the auditory modality has been shown to be the most sensitive in diagnosing awareness in this complex population. As misdiagnosis remains a major problem in DOC, exploring auditory responsiveness and processing in DOC is, therefore, of central importance to improve therapeutic interventions and medical technologies in DOC. In recent years, there has been a growing interest in the role of music as a potential treatment and medium for diagnosis with patients with DOC, from the perspectives of research, clinical practice and theory. As there are almost no treatment options, such a non-invasive method could constitute a promising strategy to stimulate brain plasticity and to improve consciousness recovery. It is therefore an ideal time to draw together specialists from diverse disciplines and interests to share the latest methods, opinions, and research on this topic in order to identify research priorities and progress inquiry in a coordinated way. This Research Topic aimed to bring together specialists from diverse disciplines involved in using and researching music with DOC populations or who have an interest in theoretical development on this topic. Specialists from the following disciplines participated in this special issue: neuroscience; medicine; music therapy; clinical psychology; neuromusicology; and cognitive neuroscience.
Category:

Biostatistics With R

Author : Babak Shahbaba
ISBN : 9781461413028
Genre : Medical
File Size : 25.95 MB
Format : PDF, Kindle
Download : 463
Read : 594

Biostatistics with R is designed around the dynamic interplay among statistical methods, their applications in biology, and their implementation. The book explains basic statistical concepts with a simple yet rigorous language. The development of ideas is in the context of real applied problems, for which step-by-step instructions for using R and R-Commander are provided. Topics include data exploration, estimation, hypothesis testing, linear regression analysis, and clustering with two appendices on installing and using R and R-Commander. A novel feature of this book is an introduction to Bayesian analysis. This author discusses basic statistical analysis through a series of biological examples using R and R-Commander as computational tools. The book is ideal for instructors of basic statistics for biologists and other health scientists. The step-by-step application of statistical methods discussed in this book allows readers, who are interested in statistics and its application in biology, to use the book as a self-learning text.
Category: Medical

Primer To Analysis Of Genomic Data Using R

Author : Cedric Gondro
ISBN : 9783319144757
Genre : Medical
File Size : 75.59 MB
Format : PDF, Docs
Download : 986
Read : 941

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
Category: Medical

New Advances In Statistics And Data Science

Author : Ding-Geng Chen
ISBN : 9783319694160
Genre : Mathematics
File Size : 45.51 MB
Format : PDF, ePub
Download : 173
Read : 893

This book is comprised of the presentations delivered at the 25th ICSA Applied Statistics Symposium held at the Hyatt Regency Atlanta, on June 12-15, 2016. This symposium attracted more than 700 statisticians and data scientists working in academia, government, and industry from all over the world. The theme of this conference was the “Challenge of Big Data and Applications of Statistics,” in recognition of the advent of big data era, and the symposium offered opportunities for learning, receiving inspirations from old research ideas and for developing new ones, and for promoting further research collaborations in the data sciences. The invited contributions addressed rich topics closely related to big data analysis in the data sciences, reflecting recent advances and major challenges in statistics, business statistics, and biostatistics. Subsequently, the six editors selected 19 high-quality presentations and invited the speakers to prepare full chapters for this book, which showcases new methods in statistics and data sciences, emerging theories, and case applications from statistics, data science and interdisciplinary fields. The topics covered in the book are timely and have great impact on data sciences, identifying important directions for future research, promoting advanced statistical methods in big data science, and facilitating future collaborations across disciplines and between theory and practice.
Category: Mathematics

Immune System Modeling And Analysis

Author : Ramit Mehr
ISBN : 9782889195015
Genre :
File Size : 89.65 MB
Format : PDF, ePub, Docs
Download : 754
Read : 1043

The rapid development of new methods for immunological data collection – from multicolor flow cytometry, through single-cell imaging, to deep sequencing – presents us now, for the first time, with the ability to analyze and compare large amounts of immunological data in health, aging and disease. The exponential growth of these datasets, however, challenges the theoretical immunology community to develop methods for data organization and analysis. Furthermore, the need to test hypotheses regarding immune function, and generate predictions regarding the outcomes of medical interventions, necessitates the development of mathematical and computational models covering processes on multiple scales, from the genetic and molecular to the cellular and system scales. The last few decades have seen the development of methods for presentation and analysis of clonal repertoires (those of T and B lymphocytes) and phenotypic (surface-marker based) repertoires of all lymphocyte types, and for modeling the intricate network of molecular and cellular interactions within the immune systems. This e-Book, which has first appeared as a ‘Frontiers in Immunology’ research topic, provides a comprehensive, online, open access snapshot of the current state of the art on immune system modeling and analysis.
Category: