| Chapter title |
Methods to study splicing from high-throughput RNA sequencing data.
|
|---|---|
| Chapter number | 26 |
| Book title |
Spliceosomal Pre-mRNA Splicing
|
| Published in |
Methods in molecular biology, January 2014
|
| DOI | 10.1007/978-1-62703-980-2_26 |
| Pubmed ID | |
| Book ISBNs |
978-1-62703-979-6, 978-1-62703-980-2
|
| Authors |
Gael P Alamancos, Eneritz Agirre, Eduardo Eyras, Gael P. Alamancos, Alamancos, Gael P., Agirre, Eneritz, Eyras, Eduardo |
| Editors |
Klemens J. Hertel |
| Abstract |
The development of novel high-throughput sequencing (HTS) methods for RNA (RNA-Seq) has provided a very powerful mean to study splicing under multiple conditions at unprecedented depth. However, the complexity of the information to be analyzed has turned this into a challenging task. In the last few years, a plethora of tools have been developed, allowing researchers to process RNA-Seq data to study the expression of isoforms and splicing events, and their relative changes under different conditions. We provide an overview of the methods available to study splicing from short RNA-Seq data, which could serve as an entry point for users who need to decide on a suitable tool for a specific analysis. We also attempt to propose a classification of the tools according to the operations they do, to facilitate the comparison and choice of methods. |
X Demographics
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|---|---|---|
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| India | 1 | 4% |
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Demographic breakdown
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Mendeley readers
Geographical breakdown
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|---|---|---|
| United States | 8 | 2% |
| United Kingdom | 7 | 2% |
| Germany | 5 | 1% |
| Spain | 5 | 1% |
| France | 4 | <1% |
| Sweden | 3 | <1% |
| Italy | 2 | <1% |
| Taiwan | 2 | <1% |
| Brazil | 1 | <1% |
| Other | 10 | 2% |
| Unknown | 368 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 120 | 29% |
| Researcher | 115 | 28% |
| Student > Master | 44 | 11% |
| Student > Bachelor | 26 | 6% |
| Student > Doctoral Student | 23 | 6% |
| Other | 64 | 15% |
| Unknown | 23 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 216 | 52% |
| Biochemistry, Genetics and Molecular Biology | 85 | 20% |
| Computer Science | 19 | 5% |
| Medicine and Dentistry | 14 | 3% |
| Mathematics | 8 | 2% |
| Other | 40 | 10% |
| Unknown | 33 | 8% |