Title |
Blind spots of quantitative RNA-seq: the limits for assessing abundance, differential expression, and isoform switching
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Published in |
BMC Bioinformatics, December 2013
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DOI | 10.1186/1471-2105-14-370 |
Pubmed ID | |
Authors |
Hubert Rehrauer, Lennart Opitz, Ge Tan, Lina Sieverling, Ralph Schlapbach |
Abstract |
RNA-seq is now widely used to quantitatively assess gene expression, expression differences and isoform switching, and promises to deliver results for the entire transcriptome. However, whether the transcriptional state of a gene can be captured accurately depends critically on library preparation, read alignment, expression estimation and the tests for differential expression and isoform switching. There are comparisons available for the individual steps but there is not yet a systematic investigation which specific genes are impacted by biases throughout the entire analysis workflow. It is especially unclear whether for a given gene, with current methods and protocols, expression changes and isoform switches can be detected. |
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Geographical breakdown
Country | Count | As % |
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United States | 7 | 37% |
Belgium | 2 | 11% |
Australia | 1 | 5% |
Canada | 1 | 5% |
United Kingdom | 1 | 5% |
Norway | 1 | 5% |
France | 1 | 5% |
China | 1 | 5% |
Unknown | 4 | 21% |
Demographic breakdown
Type | Count | As % |
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Scientists | 12 | 63% |
Members of the public | 5 | 26% |
Science communicators (journalists, bloggers, editors) | 2 | 11% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 16 | 8% |
United Kingdom | 4 | 2% |
Germany | 3 | 2% |
Norway | 3 | 2% |
Brazil | 3 | 2% |
Spain | 2 | 1% |
Sweden | 2 | 1% |
Finland | 1 | <1% |
Czechia | 1 | <1% |
Other | 5 | 3% |
Unknown | 151 | 79% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 62 | 32% |
Student > Ph. D. Student | 55 | 29% |
Student > Master | 17 | 9% |
Student > Bachelor | 12 | 6% |
Professor > Associate Professor | 10 | 5% |
Other | 28 | 15% |
Unknown | 7 | 4% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 120 | 63% |
Biochemistry, Genetics and Molecular Biology | 33 | 17% |
Computer Science | 9 | 5% |
Neuroscience | 5 | 3% |
Engineering | 4 | 2% |
Other | 9 | 5% |
Unknown | 11 | 6% |