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X Demographics
Mendeley readers
Attention Score in Context
Title |
HECTOR: a parallel multistage homopolymer spectrum based error corrector for 454 sequencing data
|
---|---|
Published in |
BMC Bioinformatics, May 2014
|
DOI | 10.1186/1471-2105-15-131 |
Pubmed ID | |
Authors |
Adrianto Wirawan, Robert S Harris, Yongchao Liu, Bertil Schmidt, Jan Schröder |
Abstract |
Current-generation sequencing technologies are able to produce low-cost, high-throughput reads. However, the produced reads are imperfect and may contain various sequencing errors. Although many error correction methods have been developed in recent years, none explicitly targets homopolymer-length errors in the 454 sequencing reads. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Norway | 2 | 33% |
United States | 1 | 17% |
United Kingdom | 1 | 17% |
Mexico | 1 | 17% |
Unknown | 1 | 17% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 4 | 67% |
Members of the public | 2 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 3 | 8% |
United States | 2 | 5% |
Czechia | 1 | 3% |
Unknown | 34 | 85% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 16 | 40% |
Student > Ph. D. Student | 7 | 18% |
Student > Bachelor | 6 | 15% |
Student > Master | 4 | 10% |
Professor > Associate Professor | 3 | 8% |
Other | 4 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 15 | 38% |
Computer Science | 12 | 30% |
Biochemistry, Genetics and Molecular Biology | 3 | 8% |
Environmental Science | 2 | 5% |
Immunology and Microbiology | 2 | 5% |
Other | 5 | 13% |
Unknown | 1 | 3% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 17 May 2018.
All research outputs
#5,527,796
of 22,755,127 outputs
Outputs from BMC Bioinformatics
#1,993
of 7,269 outputs
Outputs of similar age
#51,870
of 227,400 outputs
Outputs of similar age from BMC Bioinformatics
#40
of 146 outputs
Altmetric has tracked 22,755,127 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,269 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 72% 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 227,400 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 146 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.