| Chapter title |
Constructing simple biological networks for understanding complex high-throughput data in plants.
|
|---|---|
| Chapter number | 25 |
| Book title |
Plant Functional Genomics
|
| Published in |
Methods in molecular biology, January 2015
|
| DOI | 10.1007/978-1-4939-2444-8_25 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-2443-1, 978-1-4939-2444-8
|
| Authors |
Tomás C Moyano, Elena A Vidal, Orlando Contreras-López, Rodrigo A Gutiérrez, Tomás C. Moyano, Elena A. Vidal, Rodrigo A. Gutiérrez, Moyano, Tomás C., Vidal, Elena A., Contreras-López, Orlando, Gutiérrez, Rodrigo A. |
| Abstract |
Technological advances in the last decade have enabled biologists to produce increasing amounts of information for the transcriptome, proteome, interactome, and other -omics data sets in many model organisms. A major challenge is integration and biological interpretation of these massive data sets in order to generate testable hypotheses about gene regulatory networks or molecular mechanisms that govern system behaviors. Constructing gene networks requires bioinformatics skills to adequately manage, integrate, analyze and productively use the data to generate biological insights. In this chapter, we provide detailed methods for users without prior knowledge of bioinformatics to construct gene networks and derive hypotheses that can be experimentally verified. Step-by-step instructions for acquiring, integrating, analyzing, and visualizing genome-wide data are provided for two widely used open source platforms, R and Cytoscape platforms. The examples provided are based on Arabidopsis data, but the protocols presented should be readily applicable to any organism for which similar data can be obtained. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Chile | 1 | 3% |
| United States | 1 | 3% |
| Unknown | 32 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 26% |
| Student > Bachelor | 5 | 15% |
| Student > Postgraduate | 3 | 9% |
| Student > Master | 3 | 9% |
| Other | 2 | 6% |
| Other | 7 | 21% |
| Unknown | 5 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 15 | 44% |
| Biochemistry, Genetics and Molecular Biology | 11 | 32% |
| Computer Science | 2 | 6% |
| Medicine and Dentistry | 1 | 3% |
| Unknown | 5 | 15% |