Title |
Finding the active genes in deep RNA-seq gene expression studies
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Published in |
BMC Genomics, November 2013
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DOI | 10.1186/1471-2164-14-778 |
Pubmed ID | |
Authors |
Traver Hart, H Kiyomi Komori, Sarah LaMere, Katie Podshivalova, Daniel R Salomon |
Abstract |
Early application of second-generation sequencing technologies to transcript quantitation (RNA-seq) has hinted at a vast mammalian transcriptome, including transcripts from nearly all known genes, which might be fully measured only by ultradeep sequencing. Subsequent studies suggested that low-abundance transcripts might be the result of technical or biological noise rather than active transcripts; moreover, most RNA-seq experiments did not provide enough read depth to generate high-confidence estimates of gene expression for low-abundance transcripts. As a result, the community adopted several heuristics for RNA-seq analysis, most notably an arbitrary expression threshold of 0.3 - 1 FPKM for downstream analysis. However, advances in RNA-seq library preparation, sequencing technology, and informatic analysis have addressed many of the systemic sources of uncertainty and undermined the assumptions that drove the adoption of these heuristics. We provide an updated view of the accuracy and efficiency of RNA-seq experiments, using genomic data from large-scale studies like the ENCODE project to provide orthogonal information against which to validate our conclusions. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 5 | 20% |
Germany | 2 | 8% |
United Kingdom | 2 | 8% |
Portugal | 1 | 4% |
Canada | 1 | 4% |
Hong Kong | 1 | 4% |
Spain | 1 | 4% |
India | 1 | 4% |
Nigeria | 1 | 4% |
Other | 2 | 8% |
Unknown | 8 | 32% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 17 | 68% |
Members of the public | 7 | 28% |
Practitioners (doctors, other healthcare professionals) | 1 | 4% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 1% |
Spain | 5 | 1% |
Germany | 2 | <1% |
Mexico | 2 | <1% |
Italy | 1 | <1% |
Sweden | 1 | <1% |
Canada | 1 | <1% |
Korea, Republic of | 1 | <1% |
United Kingdom | 1 | <1% |
Other | 1 | <1% |
Unknown | 329 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 87 | 25% |
Student > Ph. D. Student | 82 | 23% |
Student > Master | 34 | 10% |
Student > Bachelor | 25 | 7% |
Professor > Associate Professor | 23 | 7% |
Other | 51 | 15% |
Unknown | 47 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 150 | 43% |
Biochemistry, Genetics and Molecular Biology | 93 | 27% |
Computer Science | 10 | 3% |
Medicine and Dentistry | 9 | 3% |
Immunology and Microbiology | 8 | 2% |
Other | 23 | 7% |
Unknown | 56 | 16% |