Chapter title |
Genome Sequencing.
|
---|---|
Chapter number | 1 |
Book title |
Bioinformatics
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-6622-6_1 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6620-2, 978-1-4939-6622-6
|
Authors |
Mansi Verma, Samarth Kulshrestha, Ayush Puri |
Editors |
Jonathan M. Keith |
Abstract |
Genome sequencing is an important step toward correlating genotypes with phenotypic characters. Sequencing technologies are important in many fields in the life sciences, including functional genomics, transcriptomics, oncology, evolutionary biology, forensic sciences, and many more. The era of sequencing has been divided into three generations. First generation sequencing involved sequencing by synthesis (Sanger sequencing) and sequencing by cleavage (Maxam-Gilbert sequencing). Sanger sequencing led to the completion of various genome sequences (including human) and provided the foundation for development of other sequencing technologies. Since then, various techniques have been developed which can overcome some of the limitations of Sanger sequencing. These techniques are collectively known as "Next-generation sequencing" (NGS), and are further classified into second and third generation technologies. Although NGS methods have many advantages in terms of speed, cost, and parallelism, the accuracy and read length of Sanger sequencing is still superior and has confined the use of NGS mainly to resequencing genomes. Consequently, there is a continuing need to develop improved real time sequencing techniques. This chapter reviews some of the options currently available and provides a generic workflow for sequencing a genome. |
X Demographics
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United States | 1 | 100% |
Demographic breakdown
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
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Brazil | 1 | <1% |
Unknown | 163 | 99% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Bachelor | 30 | 18% |
Student > Master | 24 | 15% |
Researcher | 16 | 10% |
Student > Ph. D. Student | 9 | 5% |
Student > Doctoral Student | 4 | 2% |
Other | 15 | 9% |
Unknown | 66 | 40% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 35 | 21% |
Agricultural and Biological Sciences | 22 | 13% |
Medicine and Dentistry | 10 | 6% |
Computer Science | 4 | 2% |
Unspecified | 4 | 2% |
Other | 14 | 9% |
Unknown | 75 | 46% |