Big Data Analytics in Genomics

1. Front Matter

   Pages i-viii

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2. Statistical Analytics

   1. Front Matter

      Pages 1-1

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   2. Introduction to Statistical Methods for Integrative Data Analysis in Genome-Wide Association Studies

      • Can Yang, Xiang Wan, Jin Liu, Michael Ng

      Pages 3-23

   3. Robust Methods for Expression Quantitative Trait Loci Mapping

      • Wei Cheng, Xiang Zhang, Wei Wang

      Pages 25-88

   4. Causal Inference and Structure Learning of Genotype–Phenotype Networks Using Genetic Variation

      • Adèle H. Ribeiro, Júlia M. P. Soler, Elias Chaibub Neto, André Fujita

      Pages 89-143

   5. Genomic Applications of the Neyman–Pearson Classification Paradigm

      • Jingyi Jessica Li, Xin Tong

      Pages 145-167

3. Computational Analytics

   1. Front Matter

      Pages 169-169

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   2. Improving Re-annotation of Annotated Eukaryotic Genomes

      • Shishir K. Gupta, Elena Bencurova, Mugdha Srivastava, Pirasteh Pahlavan, Johannes Balkenhol, Thomas Dandekar

      Pages 171-195

   3. State-of-the-Art in Smith–Waterman Protein Database Search on HPC Platforms

      • Enzo Rucci, Carlos García, Guillermo Botella, Armando De Giusti, Marcelo Naiouf, Manuel Prieto-Matías

      Pages 197-223

   4. A Survey of Computational Methods for Protein Function Prediction

      • Amarda Shehu, Daniel Barbará, Kevin Molloy

      Pages 225-298

   5. Genome-Wide Mapping of Nucleosome Position and Histone Code Polymorphisms in Yeast

      • Muniyandi Nagarajan, Vandana R. Prabhu

      Pages 299-313

4. Cancer Analytics

   1. Front Matter

      Pages 315-315

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   2. Perspectives of Machine Learning Techniques in Big Data Mining of Cancer

      • Archana Prabahar, Subashini Swaminathan

      Pages 317-336

   3. Mining Massive Genomic Data for Therapeutic Biomarker Discovery in Cancer: Resources, Tools, and Algorithms

      • Pan Tong, Hua Li

      Pages 337-355

   4. NGS Analysis of Somatic Mutations in Cancer Genomes

      • T. Prieto, J. M. Alves, D. Posada

      Pages 357-372

   5. OncoMiner: A Pipeline for Bioinformatics Analysis of Exonic Sequence Variants in Cancer

      • Ming-Ying Leung, Joseph A. Knapka, Amy E. Wagler, Georgialina Rodriguez, Robert A. Kirken

      Pages 373-396

   6. A Bioinformatics Approach for Understanding Genotype–Phenotype Correlation in Breast Cancer

      • Sohiya Yotsukura, Masayuki Karasuyama, Ichigaku Takigawa, Hiroshi Mamitsuka

      Pages 397-428

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.

• Big Data
• Genomics
• Data Mining
• Genome Research
• Biostatistics
• Molecular Genetics
• Data Analytics
• Biology
• Bioinformatics
• Computational Biology
• Cancer Research
• Cancer Genomes
• Computational Science
• Scientific Computing
• Computational Annotation

“This edited volume is intended to showcase the current research on big data analytics for genomics … . The edited volume is well-organized, structured, and topics appeared sequentially. Most of the chapters are self-contained. … this is a good collection of work in one place; I think this volume will attract a broader audience. I enjoyed reading a few chapters of the book and found them interesting and useful.” (Technometrics, Vol. 59 (2), April, 2017)

• Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

  Ka-Chun Wong

Ka-Chun Wong is Assistant Professor in the Department of Computer Science at City University of Hong Kong. He received his B.Eng. in Computer Engineering in 2008 and his M.Phil. degree in the Department of Computer Science and Engineering in 2010, both from United College, the Chinese University of Hong Kong. He finished his PhD at the Department of Computer Science at University of Toronto . His research interests include computational biology, bioinformatics, evolutionary computation, big data analytics, application machine learning, and interdisciplinary research.

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