| Chapter title |
Microarray-Based MicroRNA Expression Data Analysis with Bioconductor
|
|---|---|
| Chapter number | 9 |
| Book title |
Transcriptome Data Analysis
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7710-9_9 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7709-3, 978-1-4939-7710-9
|
| Authors |
Emilio Mastriani, Rihong Zhai, Songling Zhu |
| Abstract |
MicroRNAs (miRNAs) are small, noncoding RNAs that are able to regulate the expression of targeted mRNAs. Thousands of miRNAs have been identified; however, only a few of them have been functionally annotated. Microarray-based expression analysis represents a cost-effective way to identify candidate miRNAs that correlate with specific biological pathways, and to detect disease-associated molecular signatures. Generally, microarray-based miRNA data analysis contains four major steps: (1) quality control and normalization, (2) differential expression analysis, (3) target gene prediction, and (4) functional annotation. For each step, a large couple of software tools or packages have been developed. In this chapter, we present a standard analysis pipeline for miRNA microarray data, assembled by packages mainly developed with R and hosted in Bioconductor project. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 14 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 14% |
| Student > Master | 2 | 14% |
| Student > Bachelor | 2 | 14% |
| Unspecified | 1 | 7% |
| Lecturer | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 5 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 3 | 21% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 14% |
| Unspecified | 1 | 7% |
| Computer Science | 1 | 7% |
| Agricultural and Biological Sciences | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 6 | 43% |