| Chapter title |
Extraction and Curation of Gene Models for Plant Receptor Kinases for Phylogenetic Analysis
|
|---|---|
| Chapter number | 8 |
| Book title |
Plant Receptor Kinases
|
| Published in |
Methods in molecular biology, June 2017
|
| DOI | 10.1007/978-1-4939-7063-6_8 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7062-9, 978-1-4939-7063-6
|
| Authors |
Vaattovaara, Aleksia, Salojärvi, Jarkko, Wrzaczek, Michael, Aleksia Vaattovaara, Jarkko Salojärvi, Michael Wrzaczek |
| Abstract |
Analysis of gene families and identification of homologous genes are important for phylogenetic analysis and for translating results from model to crop species. While numerous plant genomes have been sequenced and made available, the identification of gene models can be difficult, in particular for large gene families arranged in tandem repeats or encoding proteins with a variable number of internal repeats. Thus, correct annotation of plant receptor kinases (PRK) is a challenge. Here, we describe a workflow for the semi-manual extraction, annotation, and verification of genes from annotated gene models as well as from non-annotated DNA regions. This protocol allows the efficient identification of gene family member of PRK from most available plant genomes. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 9 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 56% |
| Professor > Associate Professor | 1 | 11% |
| Student > Bachelor | 1 | 11% |
| Student > Doctoral Student | 1 | 11% |
| Unknown | 1 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 5 | 56% |
| Computer Science | 1 | 11% |
| Biochemistry, Genetics and Molecular Biology | 1 | 11% |
| Unknown | 2 | 22% |