Title |
A comparison of methods for differential expression analysis of RNA-seq data
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Published in |
BMC Bioinformatics, March 2013
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DOI | 10.1186/1471-2105-14-91 |
Pubmed ID | |
Authors |
Charlotte Soneson, Mauro Delorenzi |
Abstract |
Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of RNA-seq data. |
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United Kingdom | 6 | 8% |
Germany | 5 | 6% |
France | 4 | 5% |
Japan | 3 | 4% |
Switzerland | 2 | 3% |
Mexico | 2 | 3% |
Australia | 2 | 3% |
Spain | 2 | 3% |
Other | 12 | 15% |
Unknown | 23 | 29% |
Demographic breakdown
Type | Count | As % |
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Scientists | 46 | 57% |
Members of the public | 33 | 41% |
Practitioners (doctors, other healthcare professionals) | 1 | 1% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 60 | 2% |
United Kingdom | 24 | <1% |
Germany | 19 | <1% |
Brazil | 16 | <1% |
Spain | 10 | <1% |
France | 9 | <1% |
Sweden | 7 | <1% |
Italy | 6 | <1% |
Denmark | 6 | <1% |
Other | 63 | 2% |
Unknown | 2357 | 91% |
Demographic breakdown
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Researcher | 590 | 23% |
Student > Master | 373 | 14% |
Student > Bachelor | 172 | 7% |
Student > Doctoral Student | 129 | 5% |
Other | 341 | 13% |
Unknown | 255 | 10% |
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Computer Science | 148 | 6% |
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Mathematics | 53 | 2% |
Other | 252 | 10% |
Unknown | 319 | 12% |