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Next-Generation Sequencing Data Analysis (Hardcover, 2nd edition)
Loot Price: R2,418
Discovery Miles 24 180
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Next-Generation Sequencing Data Analysis (Hardcover, 2nd edition)
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Next-generation DNA and RNA sequencing has revolutionized biology
and medicine. With sequencing cost continuously dropping and our
ability to generate large datasets rising, data analysis becomes
more important than ever. Next-Generation Sequencing Data Analysis
walks readers through NGS data analysis step-by-step for a wide
range of NGS applications. For each NGS application, this book
covers topics from experimental design, sample processing,
sequencing strategy formulation, to sequencing reads quality
control, data preprocessing, reads mapping or assembly, and more
advanced stages that are specific to each application. Major
applications include: RNA-seq: both bulk and single-cell (separate
chapters) Genotyping and variant discovery through whole
genome/exome sequencing Clinical sequencing and detection of
actionable variants De novo genome assembly ChIP-seq to map
protein-DNA interactions Epigenomics through DNA methylation
sequencing Metagenome sequencing for microbiome analysis Before
detailing the analytic steps for each of these applications, the
book presents introductory cellular and molecular biology as a
refresher mostly for data scientists, the ins and outs of widely
used NGS platforms, and an overview of computing needs for NGS data
management and analysis. The book concludes with a chapter on the
changing landscape of NGS technologies and data analytics. The
second edition of this book builds on the well-received first
edition by providing updates to each chapter. Two brand new
chapters are added to meet rising data analysis demands on
single-cell RNA-seq and clinical sequencing. The increasing use of
long-reads sequencing has also been reflected in all NGS
applications. This book discusses concepts and principles that
underlie each analytic step, along with software tools for
implementation. It highlights key features of the tools while
omitting tedious details to provide an easy-to-follow guide for
practitioners in life sciences, bioinformatics, biostatistics, and
data science. Tools introduced in this book are open-source and
freely available.
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