{"product_id":"computational-methods-for-next-generation-sequencing-data-analysis-isbn-9781118169483","title":"Computational Methods for Next Generation Sequencing Data Analysis","description":"\u003cp\u003e\u003cb\u003eIntroduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications\u003c\/b\u003e\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: \u003c\/p\u003e \u003cp\u003ePart I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols.\u003c\/p\u003e \u003cp\u003ePart II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. \u003c\/p\u003e \u003cp\u003ePart III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. \u003c\/p\u003e \u003cp\u003ePart IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eComputational Methods for Next Generation Sequencing Data Analysis:\u003c\/i\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eReviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms\u003c\/li\u003e \u003cli\u003eDiscusses the mathematical and computational challenges in NGS technologies\u003c\/li\u003e \u003cli\u003eCovers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.\u003c\/p\u003e \u003cp\u003eCONTRIBUTORS xix\u003c\/p\u003e \u003cp\u003ePREFACE xxiii\u003c\/p\u003e \u003cp\u003eABOUT THE COMPANION WEBSITE xxv\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I COMPUTING AND EXPERIMENTAL INFRASTRUCTURE FOR NGS 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1 Cloud Computing for Next-Generation Sequencing Data Analysis 3\u003cbr\u003e\u003ci\u003eXuan Guo, Ning Yu, Bing Li, and Yi Pan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2 Introduction to the Analysis of Environmental Sequence Information Using Metapathways 25\u003cbr\u003e\u003ci\u003eNiels W. Hanson, Kishori M. Konwar, Shang-Ju Wu, and Steven J. Hallam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3 Pooling Strategy for Massive Viral Sequencing 57\u003cbr\u003e\u003ci\u003ePavel Skums, Alexander Artyomenko, Olga Glebova, Sumathi Ramachandran, David S. Campo, Zoya Dimitrova, Ion I. Mândoiu, Alexander Zelikovsky, and Yury Khudyakov\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4 Applications of High-Fidelity Sequencing Protocol to RNA Viruses 85\u003cbr\u003e\u003ci\u003eSerghei Mangul, Nicholas C. Wu, Ekaterina Nenastyeva, Nicholas Mancuso, Alexander Zelikovsky, Ren Sun, and Eleazar Eskin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II GENOMICS AND EPIGENOMICS 105\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5 Scaffolding Algorithms 107\u003cbr\u003e\u003ci\u003eIgor Mandric, James Lindsay, Ion I.Mândoiu, and Alexander Zelikovsky\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6 Genomic Variants Detection and Genotyping 133\u003cbr\u003e\u003ci\u003eJorge Duitama\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7 Discovering and Genotyping Twilight Zone Deletions 149\u003cbr\u003e\u003ci\u003eTobias Marschall and Alexander Schönhuth\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8 Computational Approaches for Finding Long Insertions and Deletions with NGS Data 175\u003cbr\u003e\u003ci\u003eJin Zhang, Chong Chu, and Yufeng Wu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9 Computational Approaches in Next-Generation Sequencing Data Analysis for Genome-Wide DNA Methylation Studies 197\u003cbr\u003e\u003ci\u003eJeong-Hyeon Choi and Huidong Shi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10 Bisulfite-Conversion-Based Methods for DNA Methylation Sequencing Data Analysis 227\u003cbr\u003e\u003ci\u003eElena Harris and Stefano Lonardi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III TRANSCRIPTOMICS 245\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11 Computational Methods for Transcript Assembly from RNA-SEQ Reads 247\u003cbr\u003e\u003ci\u003eStefan Canzar and Liliana Florea\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12 An Overview And Comparison of Tools for RNA-Seq Assembly 269\u003cbr\u003e\u003ci\u003eRasiah Loganantharaj and Thomas A. Randall\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13 Computational Approaches for Studying Alternative Splicing in Nonmodel Organisms From RNA-SEQ Data 287\u003cbr\u003e\u003ci\u003eSing-Hoi Sze\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14 Transcriptome Quantification and Differential Expression From NGS Data 301\u003cbr\u003e\u003ci\u003eOlga Glebova, Yvette Temate-Tiagueu, Adrian Caciula, Sahar Al Seesi, Alexander Artyomenko, Serghei Mangul, James Lindsay, Ion I. M¢andoiu, and Alexander Zelikovsky\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART IV MICROBIOMICS 329\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15 Error Correction of NGS Reads from Viral Populations 331\u003cbr\u003e\u003ci\u003ePavel Skums, Alexander Artyomenko, Olga Glebova, David S. Campo, Zoya Dimitrova, Alexander Zelikovsky, and Yury Khudyakov\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16 Probabilistic Viral Quasispecies Assembly 355\u003cbr\u003e\u003ci\u003eArmin Töpfer and Niko Beerenwinkel\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17 Reconstruction of Infectious Bronchitis Virus Quasispecies from NGS Data 383\u003cbr\u003e\u003ci\u003eBassam Tork, Ekaterina Nenastyeva, Alexander Artyomenko, Nicholas Mancuso, Mazhar I. Khan, Rachel O’Neill, Ion I. Mândoiu, and Alexander Zelikovsky\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18 Microbiome Analysis: State of the Art and Future Trends 401\u003cbr\u003e\u003ci\u003eMitch Fernandez, Vanessa Aguiar-Pulido, Juan Riveros, Wenrui Huang, Jonathan Segal, Erliang Zeng, Michael Campos, Kalai Mathee, and Giri Narasimhan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eINDEX 425\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIon Mandoiu, PhD,\u003c\/b\u003e is an associate professor in the Computer Science and Engineering Department at the University of Connecticut, USA. His main research interests are in the design and analysis of approximation algorithms for NP-hard optimization problems, particularly in the area of bioinformatics. Dr. Mandoiu has authored over 100 refereed articles in journals and conference proceedings. He has also co-edited (with A. Zelikovsky) a book on \u003ci\u003eBioinformatics Algorithms: Techniques and Applications\u003c\/i\u003e (Wiley 2008).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAlexander Zelikovsky\u003c\/b\u003e\u003cb\u003e, PhD\u003c\/b\u003e, is a Distinguished University Professor with the Computer Science Department at the Georgia State University, USA. His research focuses on discrete algorithms and their applications in computational biotechnology and biology, bioinformatics, VLSI CAD, and wireless networks. Dr. Zelikovsky has authored more than 170 refereed publications. He served as the co-Chair of International Symposium on Bioinformatics Research and Applications (2005-2016) and the Workshop on Computational Advances in Next-Generation Sequencing (2011-2015).\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThis book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts:\u003c\/p\u003e \u003cp\u003ePart I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols.\u003c\/p\u003e \u003cp\u003ePart II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data.\u003c\/p\u003e \u003cp\u003ePart III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis.\u003c\/p\u003e \u003cp\u003ePart IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eComputational Methods for Next Generation Sequencing Data Analysis:\u003c\/i\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eReviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms\u003c\/li\u003e \u003cli\u003eDiscusses the mathematical and computational challenges in NGS technologies\u003c\/li\u003e \u003cli\u003eCovers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis textis a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988966457573,"sku":"NP9781118169483","price":134.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118169483.jpg?v=1761782242","url":"https:\/\/k12savings.com\/products\/computational-methods-for-next-generation-sequencing-data-analysis-isbn-9781118169483","provider":"K12savings","version":"1.0","type":"link"}