| Chapter title |
Quantitative Transcriptome Analysis Using RNA-seq
|
|---|---|
| Chapter number | 5 |
| Book title |
Plant Circadian Networks
|
| Published in |
Methods in molecular biology, April 2014
|
| DOI | 10.1007/978-1-4939-0700-7_5 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-0699-4, 978-1-4939-0700-7
|
| Authors |
Canan Külahoglu, Andrea Bräutigam, Külahoglu, Canan, Bräutigam, Andrea |
| Editors |
Dorothee Staiger |
| Abstract |
RNA-seq has emerged as the technology of choice to quantify gene expression. This technology is a convenient accurate tool to quantify diurnal changes in gene expression, gene discovery, differential use of promoters, and splice variants for all genes expressed in a single tissue. Thus, RNA-seq experiments provide sequence information and absolute expression values about transcripts in addition to relative quantification available with microarrays or qRT-PCR. The depth of information by sequencing requires careful assessment of RNA intactness and DNA contamination. Although the RNA-seq is comparatively recent, a standard analysis framework has emerged with the packages of Bowtie2, TopHat, and Cufflinks. With rising popularity of RNA-seq tools have become manageable for researchers without much bioinformatical knowledge or programming skills. Here, we present a workflow for a RNA-seq experiment from experimental planning to biological data extraction. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 53 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 19% |
| Researcher | 10 | 19% |
| Student > Master | 10 | 19% |
| Student > Bachelor | 6 | 11% |
| Student > Postgraduate | 4 | 8% |
| Other | 5 | 9% |
| Unknown | 8 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 18 | 34% |
| Biochemistry, Genetics and Molecular Biology | 17 | 32% |
| Immunology and Microbiology | 2 | 4% |
| Chemistry | 2 | 4% |
| Computer Science | 2 | 4% |
| Other | 4 | 8% |
| Unknown | 8 | 15% |