| Chapter title |
NanoCAGE: A Method for the Analysis of Coding and Noncoding 5′-Capped Transcriptomes
|
|---|---|
| Chapter number | 4 |
| Book title |
Promoter Associated RNA
|
| Published in |
Methods in molecular biology, March 2017
|
| DOI | 10.1007/978-1-4939-6716-2_4 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6714-8, 978-1-4939-6716-2
|
| Authors |
Poulain, Stéphane, Kato, Sachi, Arnaud, Ophélie, Morlighem, Jean-Étienne, Suzuki, Makoto, Plessy, Charles, Harbers, Matthias, Stéphane Poulain, Sachi Kato, Ophélie Arnaud, Jean-Étienne Morlighem, Makoto Suzuki, Charles Plessy, Matthias Harbers |
| Editors |
Sara Napoli |
| Abstract |
Transcripts in all eukaryotes are characterized by the 5'-end specific cap structure in mRNAs. Cap Analysis Gene Expression or CAGE makes use of these caps to specifically obtain cDNA fragments from the 5'-end of RNA and sequences those at high throughput for transcript identification and genome-wide mapping of transcription start sites for coding and noncoding genes. Here, we provide an improved version of our nanoCAGE protocol that has been developed for preparing CAGE libraries from as little as 50 ng of total RNA within three standard working days. Key steps in library preparation have been improved over our previously published protocol to obtain libraries having a good 5'-end selection and a more equal size distribution for higher sequencing efficiency on Illumina MiSeq and HiSeq sequencers. We recommend nanoCAGE as the method of choice for transcriptome profiling projects even from limited amounts of RNA, and as the best approach for genome-wide mapping of transcription start sites within promoter regions. |
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 % |
|---|---|---|
| Japan | 1 | 3% |
| Unknown | 36 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 27% |
| Researcher | 9 | 24% |
| Student > Doctoral Student | 6 | 16% |
| Student > Bachelor | 3 | 8% |
| Other | 2 | 5% |
| Other | 1 | 3% |
| Unknown | 6 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 19 | 51% |
| Agricultural and Biological Sciences | 6 | 16% |
| Immunology and Microbiology | 3 | 8% |
| Chemical Engineering | 1 | 3% |
| Chemistry | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 7 | 19% |