Big Data Analysis for Bioinformatics and Biomedica

文件名称: Big Data Analysis for Bioinformatics and Biomedical Discoveries.CRC(2016).pdf

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详细说明：This series aims to capture new developments and summarize what is known over the entire spectrum of mathematical and computational biology and medicine. It seeks to encourage the integration of mathematical, statistical, and computational methods into biology by publishing a broad range of textbooks, reference works, and handbooks. The titles included in the series are meant to appeal to students, researchers, and professionals in the mathematical, statistical and computational sciences, fundamental biology and bioengineering, as well as interdisciplinary researchers involved in the field. The inclusion of concrete examples and applications, and programming techniques and examples, is highly encouraged.chapman hALl/ crc Mathematical and Computational biology series Aims and scope: This series aims to capture new developments and summarize what is known over the entire spectrum of mathematical and computational biology and medicine. It seeks to encourage the integration of mathematical. statistical and computational methods into biology by publishing a broad range of textbooks. reference works and handbooks The titles included in the series are meant to appeal to students, researchers, and professionals in the mathematical, statistical and computational sciences, fundamental biology and bioengineering, as well as interdisciplinary researchers involved in the field. The inclusion of concrete examples and applications, and programming techniques and examples, is highly encouraged Series editors N. F. Britton Department of Mathematical Sciences University of bath Xihong lin Department of Biostatistics Harvard University Nicola mulder University of Cape Town South africa Maria victoria schneider European Bioinformatics Institute Mona singh Department of Computer Science Princeton University Anna tramontano Department of physics University of rome la sapienza Proposals for the series should be submitted to one of the series editors above or directly to CRC Press, Taylor& Francis Group 3 Park square, Milton Park abingdon, Oxfordshire OX14 4RN UK Published titles An Introduction to Systems Biology Normal Mode Analysis: Theory and Design Principles of Biological Circuits Applications to Biological and chemical Uri Alon Systems Glycome Informatics: Methods and Qiang Cui and /vet Bahar Applications Kinetic Modelling in Systems Biology Kiyoko F Aoki-Kinoshita Oleg Demin and igor goryanin Computational Systems Biology of Data Analysis Tools for dNa Microarrays Cancer Sorin Draghici Emmanuel Barillot, Laurence calzone Statistics and data Analysis for Philippe hupe, Jean-Philippe vert, and Microarrays Using R and Bioconductor Andrei Zinovyev Second edition Python for Bioinformatics Sorin Draghici Sebastian Bass Computational Neuroscience: Quantitative Biology: From Molecular to A Comprehensive Approach Cellular Systems Jianfeng Feng Sebastian bassi Biological Sequence Analysis Using Methods in medical informatics: the SeqAn C++ Library Fundamentals of healthcare Andreas Gogol-Doring and Knut Reinert Programming in Perl, Python, and Ruby Gene Expression Studies Using Jules, berman Affymetrix Microarrays Computational Biology: A Statistical Hinrich gohlmann and willem talloen Mechanics Perspective Handbook of hidden markov models Ralf Blossey in Bioinformatics Game-Theoretical Models in Biology Martin Gallery Mark Broom and Jan Rychtar Meta-analysis and combining Computational and visualization Information in Genetics and Genomics Techniques for Structural Bioinformatics Rudy Guerra and darlene R. goldstein Using Chimera Differential Equations and Mathematical Forbes burkowski Biology, Second Edition Structural Bioinformatics: An Algorithmic D.S. Jones, M.. Plank, and B.D. Sleeman Approach Knowledge Discovery in Proteomics Forbes j. Burkowski Igor Jurisica and Dennis Wigle Spatial Ecology Introduction to Proteins: Structure Stephen Cantrell, Chris Cosner, and Function, and Moti Shigui ruan Amit Kessel and nir ben-Tal Cell Mechanics: From Single Scale- RNA-seq Data Analysis: A Practical Based Models to Multiscale Modeling Approach Arnaud Chauviere, Luigi Preziosi, Eja Korpelainen, Jarno Tuimala and claude verdier Panu somervuo, Mikael Huss, and garry Wong Bayesian Phylogenetics: Methods, Biological Computation Algorithms, and Applications Ehud Lamm and Ron Unger Ming-Hui Chen, Lynn Kuo, and Paul O Lew Optimal Control Applied to Biological Statistical Methods for QTL Mapping Models Zehua chen Suzanne lenhart and john t workman Published Titles(continued) Clustering in Bioinformatics and Drug Niche Modeling: Predictions from Discovery Statistical distributions John d mac Cuish and Norah E Maccuish David stockwell Spatiotemporal Patterns in Ecology Algorithms in Bioinformatics: A Practical and Epidemiology: Theory, Models, Introduction and simulation Wing-Kin Sung Horst Malchow, Sergei V Petrovski, and Introduction to bioinformatics Ezio venturino Anna tramontano Stochastic Dynamics for Systems The ten most wanted solutions in Biology Protein bioinformatics Christian Mazza and Michel Benaim Anna tramontano Engineering Genetic Circuits Combinatorial Pattern Matching Chris J. Myers Algorithms in Computational biology Pattern Discovery in Bioinformatics: Using Perl and R Theory algorithms Gabriel valiente Laxmi parida Managing your biological data with Exactly solvable Models of biologic Python Invasion Allegra Via, Kristian Rother, and Sergei V Petrovskii and Bai-Lian Li Anna tramontano Computational Hydrodynamics of Cancer Systems Biology Capsules and biological cells Edwin Wang C. Pozrikidis Stochastic Modelling for Systems Modeling and simulation of capsules Biology, second Edition and Biological Cells Darren. wilkinson C. Pozrikidis Big Data Analysis for Bioinformatics and Cancer Modelling and simulation Biomedical discoveries Luigi preziosi Shui Qing Ye Introduction to Bio-Ontologies Bioinformatics: A Practical Approach Peter n, robinson and sebastian baue Shui Qing re Dynamics of Biological Systems Introduction to Computational Michael sma∥ Proteomics Genome Annotation Golan yona Jung Soh, Paul M.K. Gordon, and Christoph W Sensen Chapman hall/crC mathematical and Computational Biology Series Big Data Analysis for Bioinformatics and Biomedical discoveries Edited by Shui Qing Ye c) CRc Press Taylor fl Boca raton London New york CRC Press is an imprint of the Taylor francis Group, an informa business a chapman hall book MATLAB is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks does not warrant the accuracy of the text or exercises in this book. This books use or discussion of mat- LAB software or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLAB software Cover Credit Foreground image: Zhang LQ, Adyshev DM, Singleton P, Li H, Cepeda J, Huang SY, Zou X, Verin AD, Tu J, Garcia JG, Ye SQ. Interactions between PBEF and oxidative stress proteins-A potential new mechanism underlying PBEF in the pathogenesis of acute lung injury. FEBS Lett. 2008; 582(13): 1802-8 Background image: Simon B, Easley RB, Gregoryov D, Ma Se, Ye SQ, Lavoie T, Garcia JGN. Microarray analysis of regional cellular responses to local mechanical stress in experimental acute lung injury. Am J Physiol Lung Cell Mol Physiol. 2006; 291(5): L851-61 CRC Press Taylor Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca raton fl 33487-2742 o 2016 by Taylor Francis Group, LLC CRC Press is an imprint of Taylor Francis group, an Informa business No claim to original U.S. Government works Version date: 20151228 International Standard Book Number-13: 978-1-4987-2454-8(eBook- PDF) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information stor age or retrieval system, without written permission from the publishers Forpermissiontophotocopyorusematerialelectronicallyfromthisworkpleaseaccesswww.copy rightcom(http://www.copyright.com/)orcontacttheCopyrightClearanceCenter,Inc.(ccc),222 Rosewood Drive, Danvers, MA01923, 978-750-8400. CCC is a not-for-profit organization that pro vides licenses and registration for a variety of users. For organizations that have been granted a photo- copy license by the CCC, a separate system of payment has been arranged Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Visit the Taylor francis Web site at http://www.taylorandfrancis.com and the crc press Web site at http://www.crcpress.com Contents Preface, ix Acknowledgments, xiii Editor. x Contributors xvii SECTIon Commonly Used Tools for big data analysis CHaPTer 1. Linux for big data analysis 3 SHUI QING YE AND DING-YOU LI CHAPTER 2. Python for Big Data Analysis 15 DMITRY N. GRIGORYEV CHAPTeR 3.R for Big Data analysis 35 STEPHEN D. S SECTION I Next-Generation DNA Sequencing Data Analysis CHAPTER 4. Genome- Sea data analysis 57 MIN XIONG, LI QIN ZHANG, AND SHUI QING YE CHAPTER 5 RNA-Seq data analysis 79 LI QIN ZHANG, MIN XIONG, DANIEL P. HERUTH, AND SHUI QING YE CHAPTER 6 Microbiome-Seg data analysis 97 DANIEL P. HERUTH, MIN XIONG, AND XUN JIANG vi■ Contents CHAPTER 7. miRNA-Seq Data Analysis 117 DANIEL P. HERUTH, MIN XIONG, AND GUANG-LIANG B CHAPTER 8 Methylome -Seg data Analysis 131 CHENGPENG BI CHAPTER ChlP-Seq data Analysis 147 SHUI QING YE, LI QIN ZHANG, AND JIANCHENG TU SECTION III Integrative and Comprehensive Big Data Analysis CHAPTER 10. Integrating Omics Data in Big Data Analysis 163 LI QIN ZHANG, DANIEL P. HERUTH, AND SHUI QING YE CHAPTER 11. Pharmacogenetics and genomics ANDREA GAEDIGK, KATRIN SANGKUHL, AND LARISA H. Cavallari CHAPTER 12. Exploring De-ldentified Electronic Health Record data with i2b2 201 MARK HOFFMAN CHAPTER 13 Big Data and Drug Discovery 215 GERALD. WYCKOFF AND D. ANDREW SKAFF CHAPTER 14. Literature-Based Knowledge Discovery 233 HONGFANG LIU AND MAJID RASTEGAR-MOJARAD CHAPTER 15. Mitigating High Dimensionality in Big Data Analysis 249 DEENDAYAL DINAKARPANDIAN Preface ARE ENTERING AN era of Big data. Big Data offer both unprec- edented opportunities and overwhelming challenges. This book is intended to provide biologists biomedical scientists, bioinformaticians computer data analysts, and other interested readers with a pragmatic blueprint to the nuts and bolts of Big Data so they more quickly, easily, and effectively harness the power of Big Data in their ground-breaking biological discoveries, translational medical researches, and personalized genomic medicine. Big Data refers to increasingly larger, more diverse, and more complex data sets that challenge the abilities of traditionally or most commonly used approaches to access, manage, and analyze data effectively the monu- mental completion of human genome sequencing ignited the generation of eg biomedical data. With the advent of ever-evolving, cutting-edge, high hroughput omic technologies, we are facing an explosive growth in the volume of biological and biomedical data. For example, Gene Expression Omnibus(http://www.ncbi.nim.nih.gov/geo/)holds3,848datasetsof transcriptome repositories derived from 1, 423, 663 samples, as of June 9, 2015. Big biomedical data come from government-sponsored projects suchasthe1000GenomesProject(http://www.1000genomes.org/),inter nationalconsortiasuchastheEncoDeProject(http://www.genome.gov/ encode/), millions of individual investigator-initiated research projects and vast pharmaceutical r&D projects. Data management can become a very complex process, especially when large volumes of data come from multiple sources and diverse types, such as images, molecules, phenotypes, and electronic medical records. These data need to be linked connected and correlated, which will enable researchers to grasp the information that is supposed to be conveyed by these data. It is evident that these Big Data with high-volume, high-velocity, and high-variety information provide us both tremendous opportunities and compelling challenges By leveraging

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