Genome Data Analysis
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Genome Data Analysis
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Author : Ju Han Kim
language : en
Publisher: Springer
Release Date : 2019-04-30
Genome Data Analysis written by Ju Han Kim and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-30 with Computers categories.
This textbook describes recent advances in genomics and bioinformatics and provides numerous examples of genome data analysis that illustrate its relevance to real world problems and will improve the reader’s bioinformatics skills. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine learning algorithms using R and Python are demonstrated for gene-expression microarrays, genotyping microarrays, next-generation sequencing data, epigenomic data, and biological network and semantic analyses. In addition, detailed attention is devoted to integrative genomic data analysis, including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and integrated management of biomolecular databases. The textbook is primarily intended for life scientists, medical scientists, statisticians, data processing researchers, engineers, and other beginners in bioinformatics who are experiencing difficulty in approaching the field. However, it will also serve as a simple guideline for experts unfamiliar with the new, developing subfield of genomic analysis within bioinformatics.
Genomics Data Analysis
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Author : David R. Bickel
language : en
Publisher: CRC Press
Release Date : 2019-09-24
Genomics Data Analysis written by David R. Bickel and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-24 with Mathematics categories.
Statisticians have met the need to test hundreds or thousands of genomics hypotheses simultaneously with novel empirical Bayes methods that combine advantages of traditional Bayesian and frequentist statistics. Techniques for estimating the local false discovery rate assign probabilities of differential gene expression, genetic association, etc. without requiring subjective prior distributions. This book brings these methods to scientists while keeping the mathematics at an elementary level. Readers will learn the fundamental concepts behind local false discovery rates, preparing them to analyze their own genomics data and to critically evaluate published genomics research. Key Features: * dice games and exercises, including one using interactive software, for teaching the concepts in the classroom * examples focusing on gene expression and on genetic association data and briefly covering metabolomics data and proteomics data * gradual introduction to the mathematical equations needed * how to choose between different methods of multiple hypothesis testing * how to convert the output of genomics hypothesis testing software to estimates of local false discovery rates * guidance through the minefield of current criticisms of p values * material on non-Bayesian prior p values and posterior p values not previously published
Genomic Data Analysis
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Author : Mr. Rohit Manglik
language : en
Publisher: EduGorilla Publication
Release Date : 2024-04-06
Genomic Data Analysis written by Mr. Rohit Manglik and has been published by EduGorilla Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-06 with Science categories.
EduGorilla Publication is a trusted name in the education sector, committed to empowering learners with high-quality study materials and resources. Specializing in competitive exams and academic support, EduGorilla provides comprehensive and well-structured content tailored to meet the needs of students across various streams and levels.
High Performance In Memory Genome Data Analysis
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Author : Hasso Plattner
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-19
High Performance In Memory Genome Data Analysis written by Hasso Plattner and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-19 with Science categories.
Recent achievements in hardware and software developments have enabled the introduction of a revolutionary technology: in-memory data management. This technology supports the flexible and extremely fast analysis of massive amounts of data, such as diagnoses, therapies, and human genome data. This book shares the latest research results of applying in-memory data management to personalized medicine, changing it from computational possibility to clinical reality. The authors provide details on innovative approaches to enabling the processing, combination, and analysis of relevant data in real-time. The book bridges the gap between medical experts, such as physicians, clinicians, and biological researchers, and technology experts, such as software developers, database specialists, and statisticians. Topics covered in this book include - amongst others - modeling of genome data processing and analysis pipelines, high-throughput data processing, exchange of sensitive data and protection of intellectual property. Beyond that, it shares insights on research prototypes for the analysis of patient cohorts, topology analysis of biological pathways, and combined search in structured and unstructured medical data, and outlines completely new processes that have now become possible due to interactive data analyses.
Genomics Data Analysis For Crop Improvement
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Author : Priyanka Anjoy
language : en
Publisher: Springer Nature
Release Date : 2024-01-09
Genomics Data Analysis For Crop Improvement written by Priyanka Anjoy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-09 with Technology & Engineering categories.
This book addresses complex problems associated with crop improvement programs, using a wide range of programming solutions, for genomics data handling and sustainable agriculture. It describes important concepts in genomics data analysis and sequence-based mapping approaches along with references. The book contains 16 chapters on recent developments in several methods of genomic data analysis for crop improvements and sustainable agriculture, all authored by eminent researchers who are experts in their fields. These chapters focus on applications of a wide range of key bioinformatics topics, including assembly, annotation, and visualization of next-generation sequencing (NGS) data; expression profiles of coding and noncoding RNA; statistical and quantitative genetics; trait-based association analysis, quantitative trait loci (QTL) mapping, and artificial intelligence in genomic studies. Real examples and case studies in the book will come in handy when applying the techniques. The relative scarcity of reference materials covering bioinformatics applications as compared with the readily available books also enhances the utility of this book. The targeted readers of the book are scientists, researchers, and bioinformaticians from genomics and advanced breeding in different areas. The book will appeal to the applied researchers engaged in crop improvements and sustainable agriculture by using bioinformatics tools, students, research project leaders, and practitioners from the various marginal disciplines and interdisciplinary research.
Primer To Analysis Of Genomic Data Using R
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Author : Cedric Gondro
language : en
Publisher: Springer
Release Date : 2015-05-18
Primer To Analysis Of Genomic Data Using R written by Cedric Gondro and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-18 with Medical categories.
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.
Data Analysis And Visualization In Genomics And Proteomics
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Author : Francisco Azuaje
language : en
Publisher: John Wiley & Sons
Release Date : 2005-06-24
Data Analysis And Visualization In Genomics And Proteomics written by Francisco Azuaje 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 2005-06-24 with Science categories.
Data Analysis and Visualization in Genomics and Proteomics is the first book addressing integrative data analysis and visualization in this field. It addresses important techniques for the interpretation of data originating from multiple sources, encoded in different formats or protocols, and processed by multiple systems. One of the first systematic overviews of the problem of biological data integration using computational approaches This book provides scientists and students with the basis for the development and application of integrative computational methods to analyse biological data on a systemic scale Places emphasis on the processing of multiple data and knowledge resources, and the combination of different models and systems
An Introduction To Statistical Genetic Data Analysis
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Author : Melinda C. Mills
language : en
Publisher: MIT Press
Release Date : 2020-02-18
An Introduction To Statistical Genetic Data Analysis written by Melinda C. Mills and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-18 with Science categories.
A comprehensive introduction to modern applied statistical genetic data analysis, accessible to those without a background in molecular biology or genetics. Human genetic research is now relevant beyond biology, epidemiology, and the medical sciences, with applications in such fields as psychology, psychiatry, statistics, demography, sociology, and economics. With advances in computing power, the availability of data, and new techniques, it is now possible to integrate large-scale molecular genetic information into research across a broad range of topics. This book offers the first comprehensive introduction to modern applied statistical genetic data analysis that covers theory, data preparation, and analysis of molecular genetic data, with hands-on computer exercises. It is accessible to students and researchers in any empirically oriented medical, biological, or social science discipline; a background in molecular biology or genetics is not required. The book first provides foundations for statistical genetic data analysis, including a survey of fundamental concepts, primers on statistics and human evolution, and an introduction to polygenic scores. It then covers the practicalities of working with genetic data, discussing such topics as analytical challenges and data management. Finally, the book presents applications and advanced topics, including polygenic score and gene-environment interaction applications, Mendelian Randomization and instrumental variables, and ethical issues. The software and data used in the book are freely available and can be found on the book's website.
Big Data Analytics In Genomics
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Author : Ka-Chun Wong
language : en
Publisher: Springer
Release Date : 2016-10-24
Big Data Analytics In Genomics written by Ka-Chun Wong and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-24 with Computers categories.
This contributed volume explores the emerging intersection between big data analytics and genomics. Recent sequencing technologies have enabled high-throughput sequencing data generation for genomics resulting in several international projects which have led to massive genomic data accumulation at an unprecedented pace. To reveal novel genomic insights from this data within a reasonable time frame, traditional data analysis methods may not be sufficient or scalable, forcing the need for big data analytics to be developed for genomics. The computational methods addressed in the book are intended to tackle crucial biological questions using big data, and are appropriate for either newcomers or veterans in the field.This volume offers thirteen peer-reviewed contributions, written by international leading experts from different regions, representing Argentina, Brazil, China, France, Germany, Hong Kong, India, Japan, Spain, and the USA. In particular, the book surveys three main areas: statistical analytics, computational analytics, and cancer genome analytics. Sample topics covered include: statistical methods for integrative analysis of genomic data, computation methods for protein function prediction, and perspectives on machine learning techniques in big data mining of cancer. Self-contained and suitable for graduate students, this book is also designed for bioinformaticians, computational biologists, and researchers in communities ranging from genomics, big data, molecular genetics, data mining, biostatistics, biomedical science, cancer research, medical research, and biology to machine learning and computer science. Readers will find this volume to be an essential read for appreciating the role of big data in genomics, making this an invaluable resource for stimulating further research on the topic.
Statistical Analysis Of Molecular And Genomic Evolution
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Author : Xun Gu
language : en
Publisher: Oxford University Press
Release Date : 2024-10-08
Statistical Analysis Of Molecular And Genomic Evolution written by Xun Gu and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-08 with Science categories.
The field of molecular and genomic evolution has been catalysed by the ever increasing availability of high throughput data such as transcriptome evolution, genotype-phenotype evolution, and genetic robustness. However, there is also an urgent requirement for the emergence of new paradigms (universally accepted scientific frameworks) supported by conceptual breakthroughs, since there is now widespread agreement that genome evolution research should be far more than a static pattern characterized by some well-known arguments and yet more big data for testing or extension. Furthermore, while the internet has made a vast body of literature and data widely accessible, researchers are increasingly facing significant challenges in how to select from this huge reserve appropriately and systematically. Statistical Analysis of Molecular and Genomic Evolution sets out to provide a solution to the most frequently asked question by next-generation young researchers in the area of evolutionary genomics: What is the knowledge that is essential for moving the research forward and where can it be found? Although the book incorporates the latest research foci, it is written at the simplest mathematical level whilst sophisticated enough to provide a deep understanding of current principles and methods. Technical issues are described only briefly, mathematical derivations are kept to a minimum, and it is structured and presented in a way that encourages its use as a graduate textbook. Mindful of the steep learning curve that some biologist readers may face, online appendices review basic mathematical and statistical concepts used in the book, and provide further examples and practical exercises. This is an advanced textbook suitable for graduate level students as well as professional researchers (both empiricists and theoreticians) in the fields of molecular phylogenetics, evolutionary biology, bioinformatics, mathematics, and statistics.