Genome Data Analysis

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Genome Data Analysis

Author : Ju Han Kim
Publisher : Springer
ISBN 13 : 9811319421
Total Pages : 367 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Genome Data Analysis by : Ju Han Kim

Download or read book Genome Data Analysis written by Ju Han Kim and published by Springer. This book was released on 2019-04-30 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

An Introduction to Statistical Genetic Data Analysis

Author : Melinda C. Mills
Publisher : MIT Press
ISBN 13 : 0262357445
Total Pages : 433 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis An Introduction to Statistical Genetic Data Analysis by : Melinda C. Mills

Download or read book An Introduction to Statistical Genetic Data Analysis written by Melinda C. Mills and published by MIT Press. This book was released on 2020-02-18 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Computational Genomics with R

Author : Altuna Akalin
Publisher : CRC Press
ISBN 13 : 1498781861
Total Pages : 462 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Computational Genomics with R by : Altuna Akalin

Download or read book Computational Genomics with R written by Altuna Akalin and published by CRC Press. This book was released on 2020-12-16 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

Primer to Analysis of Genomic Data Using R

Author : Cedric Gondro
Publisher : Springer
ISBN 13 : 3319144758
Total Pages : 270 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Primer to Analysis of Genomic Data Using R by : Cedric Gondro

Download or read book Primer to Analysis of Genomic Data Using R written by Cedric Gondro and published by Springer. This book was released on 2015-05-18 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Topological Data Analysis for Genomics and Evolution

Author : Raul Rabadan
Publisher : Cambridge University Press
ISBN 13 : 1108757499
Total Pages : 522 pages
Book Rating : 4.1/5 (87 download)

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Book Synopsis Topological Data Analysis for Genomics and Evolution by : Raul Rabadan

Download or read book Topological Data Analysis for Genomics and Evolution written by Raul Rabadan and published by Cambridge University Press. This book was released on 2019-12-19 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biology has entered the age of Big Data. A technical revolution has transformed the field, and extracting meaningful information from large biological data sets is now a central methodological challenge. Algebraic topology is a well-established branch of pure mathematics that studies qualitative descriptors of the shape of geometric objects. It aims to reduce comparisons of shape to a comparison of algebraic invariants, such as numbers, which are typically easier to work with. Topological data analysis is a rapidly developing subfield that leverages the tools of algebraic topology to provide robust multiscale analysis of data sets. This book introduces the central ideas and techniques of topological data analysis and its specific applications to biology, including the evolution of viruses, bacteria and humans, genomics of cancer, and single cell characterization of developmental processes. Bridging two disciplines, the book is for researchers and graduate students in genomics and evolutionary biology as well as mathematicians interested in applied topology.

Data Analysis for the Life Sciences with R

Author : Rafael A. Irizarry
Publisher : CRC Press
ISBN 13 : 1498775861
Total Pages : 461 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Data Analysis for the Life Sciences with R by : Rafael A. Irizarry

Download or read book Data Analysis for the Life Sciences with R written by Rafael A. Irizarry and published by CRC Press. This book was released on 2016-10-04 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained.

Next-Generation Sequencing and Sequence Data Analysis

Author : Kuo Ping Chiu
Publisher : Bentham Science Publishers
ISBN 13 : 1681080923
Total Pages : 160 pages
Book Rating : 4.6/5 (81 download)

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Book Synopsis Next-Generation Sequencing and Sequence Data Analysis by : Kuo Ping Chiu

Download or read book Next-Generation Sequencing and Sequence Data Analysis written by Kuo Ping Chiu and published by Bentham Science Publishers. This book was released on 2015-11-04 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Genomics in the Cloud

Author : Geraldine A. Van der Auwera
Publisher : O'Reilly Media
ISBN 13 : 1491975164
Total Pages : 496 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Genomics in the Cloud by : Geraldine A. Van der Auwera

Download or read book Genomics in the Cloud written by Geraldine A. Van der Auwera and published by O'Reilly Media. This book was released on 2020-04-02 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data in the genomics field is booming. In just a few years, organizations such as the National Institutes of Health (NIH) will host 50+ petabytes—or over 50 million gigabytes—of genomic data, and they’re turning to cloud infrastructure to make that data available to the research community. How do you adapt analysis tools and protocols to access and analyze that volume of data in the cloud? With this practical book, researchers will learn how to work with genomics algorithms using open source tools including the Genome Analysis Toolkit (GATK), Docker, WDL, and Terra. Geraldine Van der Auwera, longtime custodian of the GATK user community, and Brian O’Connor of the UC Santa Cruz Genomics Institute, guide you through the process. You’ll learn by working with real data and genomics algorithms from the field. This book covers: Essential genomics and computing technology background Basic cloud computing operations Getting started with GATK, plus three major GATK Best Practices pipelines Automating analysis with scripted workflows using WDL and Cromwell Scaling up workflow execution in the cloud, including parallelization and cost optimization Interactive analysis in the cloud using Jupyter notebooks Secure collaboration and computational reproducibility using Terra

Design, Analysis, and Interpretation of Genome-Wide Association Scans

Author : Daniel O. Stram
Publisher : Springer Science & Business Media
ISBN 13 : 1461494435
Total Pages : 334 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Design, Analysis, and Interpretation of Genome-Wide Association Scans by : Daniel O. Stram

Download or read book Design, Analysis, and Interpretation of Genome-Wide Association Scans written by Daniel O. Stram and published by Springer Science & Business Media. This book was released on 2013-11-23 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the statistical aspects of designing, analyzing and interpreting the results of genome-wide association scans (GWAS studies) for genetic causes of disease using unrelated subjects. Particular detail is given to the practical aspects of employing the bioinformatics and data handling methods necessary to prepare data for statistical analysis. The goal in writing this book is to give statisticians, epidemiologists, and students in these fields the tools to design a powerful genome-wide study based on current technology. The other part of this is showing readers how to conduct analysis of the created study. Design and Analysis of Genome-Wide Association Studies provides a compendium of well-established statistical methods based upon single SNP associations. It also provides an introduction to more advanced statistical methods and issues. Knowing that technology, for instance large scale SNP arrays, is quickly changing, this text has significant lessons for future use with sequencing data. Emphasis on statistical concepts that apply to the problem of finding disease associations irrespective of the technology ensures its future applications. The author includes current bioinformatics tools while outlining the tools that will be required for use with extensive databases from future large scale sequencing projects. The author includes current bioinformatics tools while outlining additional issues and needs arising from the extensive databases from future large scale sequencing projects.

Computational Genome Analysis

Author : Richard C. Deonier
Publisher : Springer Science & Business Media
ISBN 13 : 0387288074
Total Pages : 543 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis Computational Genome Analysis by : Richard C. Deonier

Download or read book Computational Genome Analysis written by Richard C. Deonier and published by Springer Science & Business Media. This book was released on 2005-12-27 with total page 543 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book features a free download of the R software statistics package and the text provides great crossover material that is interesting and accessible to students in biology, mathematics, statistics and computer science. More than 100 illustrations and diagrams reinforce concepts and present key results from the primary literature. Exercises are given at the end of chapters.

Computational Exome and Genome Analysis

Author : Peter N. Robinson
Publisher : CRC Press
ISBN 13 : 1351650815
Total Pages : 518 pages
Book Rating : 4.3/5 (516 download)

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Book Synopsis Computational Exome and Genome Analysis by : Peter N. Robinson

Download or read book Computational Exome and Genome Analysis written by Peter N. Robinson and published by CRC Press. This book was released on 2017-09-13 with total page 518 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exome and genome sequencing are revolutionizing medical research and diagnostics, but the computational analysis of the data has become an extremely heterogeneous and often challenging area of bioinformatics. Computational Exome and Genome Analysis provides a practical introduction to all of the major areas in the field, enabling readers to develop a comprehensive understanding of the sequencing process and the entire computational analysis pipeline.

Genome Annotation

Author : Jung Soh
Publisher : CRC Press
ISBN 13 : 1439841187
Total Pages : 270 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Genome Annotation by : Jung Soh

Download or read book Genome Annotation written by Jung Soh and published by CRC Press. This book was released on 2016-04-19 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: The success of individualized medicine, advanced crops, and new and sustainable energy sources requires thoroughly annotated genomic information and the integration of this information into a coherent model. A thorough overview of this field, Genome Annotation explores automated genome analysis and annotation from its origins to the challenges of next-generation sequencing data analysis. The book initially takes you through the last 16 years since the sequencing of the first complete microbial genome. It explains how current analysis strategies were developed, including sequencing strategies, statistical models, and early annotation systems. The authors then present visualization techniques for displaying integrated results as well as state-of-the-art annotation tools, including MAGPIE, Ensembl, Bluejay, and Galaxy. They also discuss the pipelines for the analysis and annotation of complex, next-generation DNA sequencing data. Each chapter includes references and pointers to relevant tools. As very few existing genome annotation pipelines are capable of dealing with the staggering amount of DNA sequence information, new strategies must be developed to accommodate the needs of today’s genome researchers. Covering this topic in detail, Genome Annotation provides you with the foundation and tools to tackle this challenging and evolving area. Suitable for both students new to the field and professionals who deal with genomic information in their work, the book offers two genome annotation systems on an accompanying CD-ROM.

Statistical Methods for the Analysis of Genomic Data

Author : Hui Jiang
Publisher :
ISBN 13 : 9783039361410
Total Pages : 136 pages
Book Rating : 4.3/5 (614 download)

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Book Synopsis Statistical Methods for the Analysis of Genomic Data by : Hui Jiang

Download or read book Statistical Methods for the Analysis of Genomic Data written by Hui Jiang and published by . This book was released on 2020 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, technological breakthroughs have greatly enhanced our ability to understand the complex world of molecular biology. Rapid developments in genomic profiling techniques, such as high-throughput sequencing, have brought new opportunities and challenges to the fields of computational biology and bioinformatics. Furthermore, by combining genomic profiling techniques with other experimental techniques, many powerful approaches (e.g., RNA-Seq, Chips-Seq, single-cell assays, and Hi-C) have been developed in order to help explore complex biological systems. As a result of the increasing availability of genomic datasets, in terms of both volume and variety, the analysis of such data has become a critical challenge as well as a topic of great interest. Therefore, statistical methods that address the problems associated with these newly developed techniques are in high demand. This book includes a number of studies that highlight the state-of-the-art statistical methods for the analysis of genomic data and explore future directions for improvement.

Next-Generation Sequencing Data Analysis

Author : Xinkun Wang
Publisher : CRC Press
ISBN 13 : 1482217899
Total Pages : 258 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Next-Generation Sequencing Data Analysis by : Xinkun Wang

Download or read book Next-Generation Sequencing Data Analysis written by Xinkun Wang and published by CRC Press. This book was released on 2016-04-06 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Fundamentals of Data Mining in Genomics and Proteomics

Author : Werner Dubitzky
Publisher : Springer Science & Business Media
ISBN 13 : 0387475095
Total Pages : 300 pages
Book Rating : 4.3/5 (874 download)

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Book Synopsis Fundamentals of Data Mining in Genomics and Proteomics by : Werner Dubitzky

Download or read book Fundamentals of Data Mining in Genomics and Proteomics written by Werner Dubitzky and published by Springer Science & Business Media. This book was released on 2007-04-13 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents state-of-the-art analytical methods from statistics and data mining for the analysis of high-throughput data from genomics and proteomics. It adopts an approach focusing on concepts and applications and presents key analytical techniques for the analysis of genomics and proteomics data by detailing their underlying principles, merits and limitations.

Handbook of Statistical Genomics

Author : David J. Balding
Publisher : John Wiley & Sons
ISBN 13 : 1119429250
Total Pages : 1828 pages
Book Rating : 4.1/5 (194 download)

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Book Synopsis Handbook of Statistical Genomics by : David J. Balding

Download or read book Handbook of Statistical Genomics written by David J. Balding and published by John Wiley & Sons. This book was released on 2019-07-09 with total page 1828 pages. Available in PDF, EPUB and Kindle. Book excerpt: A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics.

Genome Analysis And Bioinformatics: A Practical Approach

Author : T. R. Sharma
Publisher :
ISBN 13 : 9789380026367
Total Pages : 222 pages
Book Rating : 4.0/5 (263 download)

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Book Synopsis Genome Analysis And Bioinformatics: A Practical Approach by : T. R. Sharma

Download or read book Genome Analysis And Bioinformatics: A Practical Approach written by T. R. Sharma and published by . This book was released on 2009-01-01 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the decoding of whole genome sequences of many organisms, new vistas of research have emerged in computational biology. The scientific community has free access to the genome sequence data from the public databases. Many times, it is really hard to make sense of these huge data of DNA and protein sequences. Therefore, bioinformatics tools are used to handle, store and analyze genome sequence data for the benefit of mankind. The book has been written in a simplest possible manner so that every one should understand the basic concepts of genome sequence analysis and bioinformatics. The book is structured in such a way so that readers should first know about how whole genome sequences are generated by using high throughput DNA sequencing technologies and then storing of sequences in biological databases. Second part deals with the basic principals involved in sequence analysis and applications of softwares along with practical exercises. Thirdly, data mining approaches for the discovery of genes and DNA markers have also been discussed. Besides, glossary of important terms and introduction to basic bioinformatics softwares has been included for the benefits of readers. The book will serve as a text book to the B. Tech (Bioinformatics & Biotechnology) students and would also be useful reference book to the postgraduate students and research scientists working in the areas of life sciences, genomics, biotechnology and molecular biology as well as Masters in Computer Applications (MCA) who are interested in bioinformatics.