Next-Generation Sequencing (NGS) is increasingly common and has applications in various fields such as clinical diagnosis, animal and plant breeding, and conservation of species. This incredible tool has become cost-effective. However, it generates a deluge of sequence data that requires efficient analysis. The highly sought-after skills in computational and statistical analyses include machine learning and, are essential for successful research within a wide range of specializations, such as identifying causes of cancer, vaccine design, new antibiotics, drug development, personalized medicine, and increased crop yields in agriculture.
This invaluable book provides step-by-step guides to complex topics that make it easy for readers to perform specific analyses, from raw sequenced data to answer important biological questions using machine learning methods. It is an excellent hands-on material for lecturers who conduct courses in bioinformatics and as reference material for professionals. The chapters are standalone recipes making them suitable for readers who wish to self-learn selected topics. Readers gain the essential skills necessary to work on sequenced data from NGS platforms; hence, making themselves more attractive to employers who need skilled bioinformaticians.
Contents:
- Introduction to Next Generation Sequencing Technologies (Lloyd Low and Martti T Tammi)
- Primer on Linux (Adeel Malik and Muhammad Farhan Sjaugi)
- Inspection of Sequence Quality (Kwong Qi Bin, Ong Ai Ling, Heng Huey Ying and Martti T Tammi)
- Alignment of Sequenced Reads (Akzam Saidin)
- Establish a Research Workflow (Joel Low Zi-Bin and Heng Huey Ying)
- De novo Assembly of a Genome (Joel Low Zi-Bin, Martti T Tammi and Wai Yee Low)
- Exome Sequencing (Setia Pramana, Kwong Qi Bin, Heng Huey Ying, Nuha Hassim and Ong Ai Ling)
- Transcriptomics (Yan Ren, Akzam Saidin and Wai Yee Low)
- Metagenomics (Sim Chun Hock, Kee Shao Yong, Ong Ai Ling, Heng Huey Ying and Teh Chee Keng)
- Applications of NGS Data (Teh Chee Keng, Ong Ai Ling and Kwong Qi Bin)
- Predicting Human Enhancers with Machine Learning (Callum MacPhillamy and Wai Yee Low)
Readership: It is an excellent hands-on material for teachers and lecturers who conduct courses in bioinformatics and as a reference material for professionals. The chapters are written to be standalone recipes making it suitable for students who wish to self-learn selected topics such as how to apply machine learning to study genomic features. It is a necessary companion for undergraduates, graduate students, researchers and anyone interested in the exponentially growing field of bioinformatics.
'This book contains a series of tutorials for a biologist who wants to learn to analyze large-scale sequence data. It covers the basics, as well as more advanced methods. It is a very practical guide with useful examples and it will provide a way for researchers to build a foundation in bioinformatic sequence analysis. I will recommend it to my group members.' - Björn AnderssonDepartment of Cell&Molecular Biology, Karolinska Institutet
Praise for Editors' Previous Book:'The book is a great practical introduction to the bioinformatics analysis of next generation sequencing data, covering all the major areas of analysis, from quality control and alignment to domain-specific analysis such as variant calling and transcriptomics. The book is highly practical with well-chosen and relevant exercises for readers who want to get their feet wet analysing sequencing data. It is excellent resource for conducting practical classes or workshops. The exercises are highly relevant and the book covers major areas for next generation bioinformatics, making it suitable for beginners, but also experienced practitioners looking to try out analyses in a different domain.' - Kenneth Hon Kim BAN, MBBS, PhDAssistant Professor, Department of Biochemistry, Yong Loo Lin School of Medicine, NUSAssistant Principal Investigator, Institute of Molecular and Cell Biology, A*STAR
Key Features:
- This invaluable book provides step-by-step guides to complex topics that make it easy for readers to perform essential analyses from raw sequenced data to answering important biological questions
- It is an excellent hands-on material for teachers and lecturers who conduct courses in bioinformatics and as a reference material for professionals
- The chapters are written to be standalone recipes making it suitable for students who wish to self-learn selected topics such as how to apply machine learning to study genomic features
Next-Generation Sequencing (NGS) is increasingly common and has applications in various fields such as clinical diagnosis, animal and plant breeding, and conservation of species. This incredible tool has become cost-effective. However, it generates a deluge of sequence data that requires efficient analysis. The highly sought-after skills in computational and statistical analyses include machine learning and, are essential for successful research within a wide range of specializations, such as identifying causes of cancer, vaccine design, new antibiotics, drug development, personalized medicine, and increased crop yields in agriculture.
This invaluable book provides step-by-step guides to complex topics that make it easy for readers to perform specific analyses, from raw sequenced data to answer important biological questions using machine learning methods. It is an excellent hands-on material for lecturers who conduct courses in bioinformatics and as reference material for professionals. The chapters are standalone recipes making them suitable for readers who wish to self-learn selected topics. Readers gain the essential skills necessary to work on sequenced data from NGS platforms; hence, making themselves more attractive to employers who need skilled bioinformaticians.
Contents:
- Introduction to Next Generation Sequencing Technologies (Lloyd Low and Martti T Tammi)
- Primer on Linux (Adeel Malik and Muhammad Farhan Sjaugi)
- Inspection of Sequence Quality (Kwong Qi Bin, Ong Ai Ling, Heng Huey Ying and Martti T Tammi)
- Alignment of Sequenced Reads (Akzam Saidin)
- Establish a Research Workflow (Joel Low Zi-Bin and Heng Huey Ying)
- De novo Assembly of a Genome (Joel Low Zi-Bin, Martti T Tammi and Wai Yee Low)
- Exome Sequencing (Setia Pramana, Kwong Qi Bin, Heng Huey Ying, Nuha Hassim and Ong Ai Ling)
- Transcriptomics (Yan Ren, Akzam Saidin and Wai Yee Low)
- Metagenomics (Sim Chun Hock, Kee Shao Yong, Ong Ai Ling, Heng Huey Ying and Teh Chee Keng)
- Applications of NGS Data (Teh Chee Keng, Ong Ai Ling and Kwong Qi Bin)
- Predicting Human Enhancers with Machine Learning (Callum MacPhillamy and Wai Yee Low)
Readership: It is an excellent hands-on material for teachers and lecturers who conduct courses in bioinformatics and as a reference material for professionals. The chapters are written to be standalone recipes making it suitable for students who wish to self-learn selected topics such as how to apply machine learning to study genomic features. It is a necessary companion for undergraduates, graduate students, researchers and anyone interested in the exponentially growing field of bioinformatics.
'This book contains a series of tutorials for a biologist who wants to learn to analyze large-scale sequence data. It covers the basics, as well as more advanced methods. It is a very practical guide with useful examples and it will provide a way for researchers to build a foundation in bioinformatic sequence analysis. I will recommend it to my group members.' - Björn AnderssonDepartment of Cell&Molecular Biology, Karolinska Institutet
Praise for Editors' Previous Book:'The book is a great practical introduction to the bioinformatics analysis of next generation sequencing data, covering all the major areas of analysis, from quality control and alignment to domain-specific analysis such as variant calling and transcriptomics. The book is highly practical with well-chosen and relevant exercises for readers who want to get their feet wet analysing sequencing data. It is excellent resource for conducting practical classes or workshops. The exercises are highly relevant and the book covers major areas for next generation bioinformatics, making it suitable for beginners, but also experienced practitioners looking to try out analyses in a different domain.' - Kenneth Hon Kim BAN, MBBS, PhDAssistant Professor, Department of Biochemistry, Yong Loo Lin School of Medicine, NUSAssistant Principal Investigator, Institute of Molecular and Cell Biology, A*STAR
Key Features:
- This invaluable book provides step-by-step guides to complex topics that make it easy for readers to perform essential analyses from raw sequenced data to answering important biological questions
- It is an excellent hands-on material for teachers and lecturers who conduct courses in bioinformatics and as a reference material for professionals
- The chapters are written to be standalone recipes making it suitable for students who wish to self-learn selected topics such as how to apply machine learning to study genomic features
PRACTICAL BIOINFORMATICS FOR BEGINNERS: From Raw Sequence Analysis to Machine Learning Applications
268
PRACTICAL BIOINFORMATICS FOR BEGINNERS: From Raw Sequence Analysis to Machine Learning Applications
268Related collections and offers
Product Details
| ISBN-13: | 9789811259005 |
|---|---|
| Publisher: | WSPC |
| Publication date: | 01/17/2023 |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| Pages: | 268 |
| File size: | 13 MB |
| Note: | This product may take a few minutes to download. |