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Attention Score in Context
| Title |
Comparison of next generation sequencing technologies for transcriptome characterization
|
|---|---|
| Published in |
BMC Genomics, August 2009
|
| DOI | 10.1186/1471-2164-10-347 |
| Pubmed ID | |
| Authors |
P Kerr Wall, Jim Leebens-Mack, André S Chanderbali, Abdelali Barakat, Erik Wolcott, Haiying Liang, Lena Landherr, Lynn P Tomsho, Yi Hu, John E Carlson, Hong Ma, Stephan C Schuster, Douglas E Soltis, Pamela S Soltis, Naomi Altman, Claude W dePamphilis |
| Abstract |
We have developed a simulation approach to help determine the optimal mixture of sequencing methods for most complete and cost effective transcriptome sequencing. We compared simulation results for traditional capillary sequencing with "Next Generation" (NG) ultra high-throughput technologies. The simulation model was parameterized using mappings of 130,000 cDNA sequence reads to the Arabidopsis genome (NCBI Accession SRA008180.19). We also generated 454-GS20 sequences and de novo assemblies for the basal eudicot California poppy (Eschscholzia californica) and the magnoliid avocado (Persea americana) using a variety of methods for cDNA synthesis. |
Mendeley readers
The data shown below were compiled from readership statistics for 705 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 25 | 4% |
| United Kingdom | 16 | 2% |
| Brazil | 12 | 2% |
| France | 7 | <1% |
| Germany | 6 | <1% |
| Spain | 4 | <1% |
| Japan | 4 | <1% |
| Netherlands | 3 | <1% |
| Canada | 3 | <1% |
| Other | 28 | 4% |
| Unknown | 597 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 228 | 32% |
| Student > Ph. D. Student | 171 | 24% |
| Professor > Associate Professor | 64 | 9% |
| Student > Master | 60 | 9% |
| Professor | 40 | 6% |
| Other | 100 | 14% |
| Unknown | 42 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 497 | 70% |
| Biochemistry, Genetics and Molecular Biology | 61 | 9% |
| Medicine and Dentistry | 22 | 3% |
| Computer Science | 18 | 3% |
| Engineering | 14 | 2% |
| Other | 40 | 6% |
| Unknown | 53 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 August 2010.
All research outputs
#12,846,160
of 22,649,029 outputs
Outputs from BMC Genomics
#4,546
of 10,605 outputs
Outputs of similar age
#89,904
of 110,296 outputs
Outputs of similar age from BMC Genomics
#30
of 31 outputs
Altmetric has tracked 22,649,029 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,605 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 55% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 110,296 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 3rd percentile – i.e., 3% of its contemporaries scored the same or lower than it.