Big Data Analytics in Genomics
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
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Big Data Analytics in Genomics
Big Data Analytics in Genomics
Ka-Chun Wong
Big Data Analytics in Genomics
123
Ka-Chun Wong Department of Computer Science City University of Hong Kong Kowloon Tong, Hong Kong
ISBN 978-3-319-41278-8 DOI 10.1007/978-3-319-41279-5
ISBN 978-3-319-41279-5 (eBook)
Library of Congress Control Number: 2016950204 © Springer International Publishing Switzerland (outside the USA) 2016 Chapter 12 completed within the capacity of an US governmental employment. US copy-right protection does not apply. This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG Switzerland
Preface
At the beginning of the 21st century, next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies have enabled high-throughput sequencing data generation for genomics; international projects (e.g., the Encyclopedia of DNA Elements (ENCODE) Consortium, the 1000 Genomes Project, The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) program, and the Functional Annotation Of Mammalian genome (FANTOM) project) have been successfully launched, leading to massive genomic data accumulation at an unprecedentedly fast pace. To reveal novel genomic insights from those big data within a reasonable time frame, traditional data analysis methods may not be sufficient and scalable. Therefore, big data analytics have to be developed for genomics. As an attempt to summarize the current efforts in big data analytics for genomics, an open book chapter call is made at the end of 2015, resulting in 40 book chapter submissions which have gone through rigorous single-blind review process. After the initial screening and hundreds of reviewer invitations, the authors of each eligible book chapter submission have received at least 2 anonymous expert reviews (at most, 6 reviews) for improvements, resulting in the current 13 book ch
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