| Chapter title |
Ribosome Profiling in Maize
|
|---|---|
| Chapter number | 10 |
| Book title |
Maize
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7315-6_10 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7314-9, 978-1-4939-7315-6
|
| Authors |
Prakitchai Chotewutmontri, Nicholas Stiffler, Kenneth P. Watkins, Alice Barkan |
| Abstract |
Ribosome profiling (also known as Ribo-seq) provides a genome-wide, high-resolution, and quantitative accounting of mRNA segments that are occupied by ribosomes in vivo. The method has been used to address numerous questions in bacteria, yeast, and metazoa, but its application to questions in plant biology is just beginning. This chapter provides a detailed protocol for profiling ribosomes in plant leaf tissue. The method was developed and optimized with maize, but it has been used successfully with Arabidopsis and tobacco as well. The method captures ribosome footprints from the chloroplast and cytosol in the same preparation, but it is not optimal for detecting the footprints of mitochondrial ribosomes. The protocol is robust and simpler than many of the methods reported previously for ribosome profiling in plants. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 17% |
| Ireland | 1 | 17% |
| United States | 1 | 17% |
| Unknown | 3 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 83% |
| Scientists | 1 | 17% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 26 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 23% |
| Student > Master | 4 | 15% |
| Researcher | 4 | 15% |
| Professor > Associate Professor | 2 | 8% |
| Student > Bachelor | 2 | 8% |
| Other | 3 | 12% |
| Unknown | 5 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 12 | 46% |
| Biochemistry, Genetics and Molecular Biology | 7 | 27% |
| Computer Science | 1 | 4% |
| Nursing and Health Professions | 1 | 4% |
| Unknown | 5 | 19% |