| Chapter title |
KEGG Bioinformatics Resource for Plant Genomics and Metabolomics.
|
|---|---|
| Chapter number | 3 |
| Book title |
Plant Bioinformatics
|
| Published in |
Methods in molecular biology, January 2016
|
| DOI | 10.1007/978-1-4939-3167-5_3 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-3166-8, 978-1-4939-3167-5
|
| Authors |
Kanehisa, Minoru, Minoru Kanehisa |
| Editors |
David Edwards |
| Abstract |
In the era of high-throughput biology it is necessary to develop not only elaborate computational methods but also well-curated databases that can be used as reference for data interpretation. KEGG ( http://www.kegg.jp/ ) is such a reference knowledge base with two specific aims. One is to compile knowledge on high-level functions of the cell and the organism in terms of the molecular interaction and reaction networks, which is implemented in KEGG pathway maps, BRITE functional hierarchies, and KEGG modules. The other is to expand knowledge on genes and proteins involved in the molecular networks from experimentally observed organisms to other organisms using the concept of orthologs, which is implemented in the KEGG Orthology (KO) system. Thus, KEGG is a generic resource applicable to all organisms and enables interpretation of high-level functions from genomic and molecular data. Here we first present a brief overview of the entire KEGG resource, and then give an introduction of how to use KEGG in plant genomics and metabolomics research. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 60 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 23% |
| Researcher | 9 | 15% |
| Student > Master | 7 | 12% |
| Professor > Associate Professor | 4 | 7% |
| Student > Bachelor | 4 | 7% |
| Other | 10 | 17% |
| Unknown | 12 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 20 | 33% |
| Biochemistry, Genetics and Molecular Biology | 10 | 17% |
| Chemistry | 3 | 5% |
| Computer Science | 3 | 5% |
| Medicine and Dentistry | 2 | 3% |
| Other | 7 | 12% |
| Unknown | 15 | 25% |