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.
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
