Next Generation Sequencing
May 1, 2024
Updated May 8, 2025
23 minute read
Next Generation Sequencing (NGS) represents a monumental leap in our ability to read and understand genetic material. At its core, NGS is a massively parallel sequencing technology, meaning it can determine the order of DNA or RNA building blocks (nucleotides) in millions of different fragments simultaneously. This high-throughput capability delivers an unprecedented amount of data with remarkable speed and scalability, revolutionizing biological sciences and opening doors to insights previously unimaginable. From sequencing entire genomes to analyzing specific regions of interest or exploring the intricacies of gene expression, NGS has become an indispensable tool for researchers and clinicians worldwide.
Working with NGS can be an incredibly engaging and exciting prospect. Imagine being at the forefront of personalized medicine, tailoring treatments based on an individual's unique genetic makeup. Picture yourself contributing to breakthroughs in understanding complex diseases like cancer by identifying the subtle genetic mutations that drive their progression. Or perhaps you're drawn to the challenge of exploring the vast microbial ecosystems within us and around us, uncovering new organisms and their roles in health and disease. The ability to generate and interpret vast datasets, to piece together the complex puzzles of life encoded in DNA and RNA, offers a profoundly rewarding intellectual journey.
Introduction to Next Generation Sequencing
This section will introduce you to the fundamental concepts of Next Generation Sequencing, tracing its origins and highlighting its transformative impact on science and medicine. We aim to provide a clear understanding of what NGS is and why it has become such a pivotal technology.
Definition and Basic Principles
zbpjy9|
Find a path to becoming a Next Generation Sequencing. Learn more at:
OpenCourser.com/topic/zbpjy9/next
Reading list
We've selected 21 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Next Generation Sequencing.
Offers a step-by-step approach to NGS data analysis for a wide range of applications, including RNA-seq, variant discovery, and clinical sequencing. The second edition includes new chapters on single-cell RNA-seq and clinical sequencing, reflecting contemporary topics. It's a practical guide for practitioners in life sciences, bioinformatics, and biostatistics.
Provides a comprehensive guide to NGS data analysis, covering various applications like genome assembly, variant discovery, and metagenomics. It's suitable for researchers and graduate students and can serve as a textbook, offering detailed analysis steps rather than just black-box pipelines.
Offers a plain-language introduction to the informatics methods and tools for analyzing NGS data. It covers experimental design, data preprocessing, alignment, and assembly, with updated information and new chapters on emerging technologies. It's suitable for students, laboratory scientists, and computing specialists.
Compiles chapters on various aspects of NGS, including genomics, transcriptomics, methylomics, platforms, and bioinformatics challenges. It examines advances and challenges of NGS applications in research, providing insights into contemporary topics.
This practical book focuses on analyzing genomic data using cloud infrastructure and open-source tools like GATK, Docker, and WDL. It's highly relevant for professionals and researchers dealing with large-scale NGS data analysis in a cloud environment, covering contemporary computational approaches.
Provides a practical guide to the analysis of RNA-seq data, a common application of NGS. It covers the workflow from theoretical background to practical examples using various tools, suitable for both bioinformaticians and wet lab scientists.
Introduces the fundamental concepts of RNA-seq experiments and guides readers through the data analysis process. It covers experimental design, quality control, alignment, quantification, and differential gene expression analysis, valuable for those focusing on transcriptomics.
Explores recent advances in NGS instrumentation and data analysis, with contributions from experts. It covers current NGS platforms, sequencing chemistries, and applications in various fields, providing a broad overview of the technology and its uses.
This widely-used textbook provides a broad introduction to bioinformatics and genomics, including a chapter on next-generation sequencing in its third edition. It's suitable for advanced undergraduate and graduate students and serves as a valuable resource for researchers.
Focuses on the use of NGS in diagnosing diseases. It provides a comprehensive examination of the wide range of clinical applications, highlighting the potential of NGS to optimize healthcare and identify disease-causing variants.
Focuses specifically on NGS technology in the context of DNA analysis. It likely covers the technical aspects of the sequencing platforms and their applications in DNA sequencing projects.
Covers bioinformatics methods ranging from omics technologies to next generation sequencing. It provides a broader context within the 'omics' field and then focuses on methods relevant to NGS data.
Focuses on practical data skills for bioinformatics using open-source tools, which is essential for working with NGS data. While not exclusively about NGS, it provides foundational knowledge in data manipulation, programming, and reproducible research, highly valuable for anyone analyzing sequencing data.
Provides a comprehensive introduction to bioinformatics, including sequence and genome analysis. It's a widely recommended textbook for gaining a solid understanding of the fundamental concepts and algorithms used in the field, serving as good background for NGS.
Focuses on the algorithmic foundations of bioinformatics. Understanding these algorithms is crucial for comprehending how NGS data is processed and analyzed. It's a good resource for those wanting to delve deeper into the computational aspects.
Considered a classic in bioinformatics, this book covers probabilistic models for analyzing biological sequences. While published before the advent of modern NGS, the foundational concepts of sequence analysis are still highly relevant for understanding NGS data processing and interpretation.
Presents the foundations of computational molecular biology and bioinformatics, with a focus on computational and statistical principles applied to genomes. While not solely focused on NGS, it provides essential background in the algorithms and statistics necessary for analyzing sequencing data.
Provides an introduction to bioinformatics, covering biological context, classical sequence analysis, and omics. It's a good starting point for those new to the field, offering foundational knowledge relevant to understanding the biological data generated by NGS.
Offers an overview of classical bioinformatics concepts. While not solely focused on NGS, it provides essential foundational knowledge in the field, making it useful as a preparatory text for understanding the broader context of NGS data analysis.
Is considered a classic in computational biology, focusing on algorithms for sequence analysis. While theoretical and not specific to NGS technology, the algorithms discussed are fundamental to many NGS data analysis tasks like alignment and assembly.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/zbpjy9/next
Save
