| Chapter title |
Posttranscriptional Regulatory Networks: From Expression Profiling to Integrative Analysis of mRNA and MicroRNA Data
|
|---|---|
| Chapter number | 15 |
| Book title |
Quantitative Real-Time PCR
|
| Published in |
Methods in molecular biology, January 2014
|
| DOI | 10.1007/978-1-4939-0733-5_15 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-0732-8, 978-1-4939-0733-5
|
| Authors |
Swanhild U. Meyer, Katharina Stoecker, Steffen Sass, Fabian J. Theis, Michael W. Pfaffl, Meyer, Swanhild U., Stoecker, Katharina, Sass, Steffen, Theis, Fabian J., Pfaffl, Michael W. |
| Abstract |
Protein coding RNAs are posttranscriptionally regulated by microRNAs, a class of small noncoding RNAs. Insights in messenger RNA (mRNA) and microRNA (miRNA) regulatory interactions facilitate the understanding of fine-tuning of gene expression and might allow better estimation of protein synthesis. However, in silico predictions of mRNA-microRNA interactions do not take into account the specific transcriptomic status of the biological system and are biased by false positives. One possible solution to predict rather reliable mRNA-miRNA relations in the specific biological context is to integrate real mRNA and miRNA transcriptomic data as well as in silico target predictions. This chapter addresses the workflow and methods one can apply for expression profiling and the integrative analysis of mRNA and miRNA data, as well as how to analyze and interpret results, and how to build up models of posttranscriptional regulatory networks. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 19 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 37% |
| Student > Ph. D. Student | 5 | 26% |
| Student > Bachelor | 2 | 11% |
| Student > Postgraduate | 2 | 11% |
| Student > Master | 2 | 11% |
| Other | 1 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 42% |
| Biochemistry, Genetics and Molecular Biology | 5 | 26% |
| Mathematics | 1 | 5% |
| Computer Science | 1 | 5% |
| Immunology and Microbiology | 1 | 5% |
| Other | 2 | 11% |
| Unknown | 1 | 5% |