Chapter title |
Preprocessing and Quality Control for Whole-Genome Sequences from the Illumina HiSeq X Platform
|
---|---|
Chapter number | 30 |
Book title |
Statistical Human Genetics
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-7274-6_30 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7273-9, 978-1-4939-7274-6
|
Authors |
Marvin N. Wright, Damian Gola, Andreas Ziegler |
Abstract |
The advancement of high-throughput sequencing technologies enables sequencing of human genomes at steadily decreasing costs and increasing quality. Before variants can be analyzed, e.g., in association studies, the raw data obtained from the sequencer need to be preprocessed. These preprocessing steps include the removal of adapters, duplicates, and contaminations, alignment to a reference genome and the postprocessing of the alignment. All later steps, such as variant discovery, rely on high data quality and proper preprocessing, emphasizing the great importance of quality control. This chapter presents a workflow for preprocessing Illumina HiSeq X sequencing data. Code snippets are provided for illustrating all necessary steps, along with a brief description of the tools and underlying methods. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 31 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 8 | 26% |
Student > Bachelor | 7 | 23% |
Researcher | 5 | 16% |
Student > Master | 2 | 6% |
Professor | 1 | 3% |
Other | 2 | 6% |
Unknown | 6 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 7 | 23% |
Biochemistry, Genetics and Molecular Biology | 6 | 19% |
Computer Science | 3 | 10% |
Engineering | 2 | 6% |
Nursing and Health Professions | 1 | 3% |
Other | 4 | 13% |
Unknown | 8 | 26% |