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Next-Generation Sequencing Data Analysis (Hardcover)
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Next-Generation Sequencing Data Analysis (Hardcover)
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A Practical Guide to the Highly Dynamic Area of Massively Parallel
Sequencing The development of genome and transcriptome sequencing
technologies has led to a paradigm shift in life science research
and disease diagnosis and prevention. Scientists are now able to
see how human diseases and phenotypic changes are connected to DNA
mutation, polymorphism, genome structure, and epigenomic
abnormality. Next-Generation Sequencing Data Analysis shows how
next-generation sequencing (NGS) technologies are applied to
transform nearly all aspects of biological research. The book walks
readers through the multiple stages of NGS data generation and
analysis in an easy-to-follow fashion. It covers every step in each
stage, from the planning stage of experimental design, sample
processing, sequencing strategy formulation, the early stage of
base calling, reads quality check and data preprocessing to the
intermediate stage of mapping reads to a reference genome and
normalization to more advanced stages specific to each application.
All major applications of NGS are covered, including: RNA-seq:
mRNA-seq and small RNA-seq Genotyping and variant discovery through
genome re-sequencing De novo genome assembly ChIP-seq to study
DNA-protein interaction Methylated DNA sequencing on epigenetic
regulation Metagenome analysis through community genome shotgun
sequencing Before detailing the analytic steps for each of these
applications, the book presents the ins and outs of the most widely
used NGS platforms, with side-by-side comparisons of key technical
aspects. This helps practitioners decide which platform to use for
a particular project. The book also offers a perspective on the
development of DNA sequencing technologies, from Sanger to
future-generation sequencing technologies. The 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, and biostatistics. In addition, references to
detailed descriptions of the tools are given for further reading if
needed. The accompanying website for the book provides
step-by-step, real-world examples of how to apply the tools covered
in the text to research projects. All the tools are freely
available to academic users.
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