| Title |
High-Throughput SuperSAGE for Digital Gene Expression Analysis of Multiple Samples Using Next Generation Sequencing
|
|---|---|
| Published in |
PLOS ONE, August 2010
|
| DOI | 10.1371/journal.pone.0012010 |
| Pubmed ID | |
| Authors |
Hideo Matsumura, Kentaro Yoshida, Shujun Luo, Eiji Kimura, Takahiro Fujibe, Zayed Albertyn, Roberto A. Barrero, Detlev H. Krüger, Günter Kahl, Gary P. Schroth, Ryohei Terauchi |
| Abstract |
We established a protocol of the SuperSAGE technology combined with next-generation sequencing, coined "High-Throughput (HT-) SuperSAGE". SuperSAGE is a method of digital gene expression profiling that allows isolation of 26-bp tag fragments from expressed transcripts. In the present protocol, index (barcode) sequences are employed to discriminate tags from different samples. Such barcodes allow researchers to analyze digital tags from transcriptomes of many samples in a single sequencing run by simply pooling the libraries. Here, we demonstrated that HT-SuperSAGE provided highly sensitive, reproducible and accurate digital gene expression data. By increasing throughput for analysis in HT-SuperSAGE, various applications are foreseen and several examples are provided in the present study, including analyses of laser-microdissected cells, biological replicates and tag extraction using different anchoring enzymes. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 185 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 6 | 3% |
| Chile | 4 | 2% |
| Germany | 3 | 2% |
| Italy | 2 | 1% |
| Brazil | 2 | 1% |
| Netherlands | 1 | <1% |
| Colombia | 1 | <1% |
| Austria | 1 | <1% |
| Cuba | 1 | <1% |
| Other | 5 | 3% |
| Unknown | 159 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 46 | 25% |
| Student > Ph. D. Student | 44 | 24% |
| Student > Master | 24 | 13% |
| Professor > Associate Professor | 12 | 6% |
| Other | 11 | 6% |
| Other | 32 | 17% |
| Unknown | 16 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 123 | 66% |
| Biochemistry, Genetics and Molecular Biology | 23 | 12% |
| Medicine and Dentistry | 5 | 3% |
| Environmental Science | 4 | 2% |
| Computer Science | 2 | 1% |
| Other | 9 | 5% |
| Unknown | 19 | 10% |