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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Masking as an effective quality control method for next-generation sequencing data analysis
|
|---|---|
| Published in |
BMC Bioinformatics, December 2014
|
| DOI | 10.1186/s12859-014-0382-2 |
| Pubmed ID | |
| Authors |
Sajung Yun, Sijung Yun |
| Abstract |
Next generation sequencing produces base calls with low quality scores that can affect the accuracy of identifying simple nucleotide variation calls, including single nucleotide polymorphisms and small insertions and deletions. Here we compare the effectiveness of two data preprocessing methods, masking and trimming, and the accuracy of simple nucleotide variation calls on whole-genome sequence data from Caenorhabditis elegans. Masking substitutes low quality base calls with 'N's (undetermined bases), whereas trimming removes low quality bases that results in a shorter read lengths. |
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 % |
|---|---|---|
| Germany | 1 | 17% |
| Norway | 1 | 17% |
| Unknown | 4 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 50% |
| Members of the public | 3 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 1 | 2% |
| Denmark | 1 | 2% |
| Slovakia | 1 | 2% |
| Unknown | 57 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 18 | 30% |
| Student > Ph. D. Student | 12 | 20% |
| Student > Master | 9 | 15% |
| Student > Bachelor | 7 | 12% |
| Student > Doctoral Student | 4 | 7% |
| Other | 5 | 8% |
| Unknown | 5 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 27 | 45% |
| Biochemistry, Genetics and Molecular Biology | 16 | 27% |
| Environmental Science | 3 | 5% |
| Computer Science | 2 | 3% |
| Immunology and Microbiology | 2 | 3% |
| Other | 4 | 7% |
| Unknown | 6 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 02 January 2015.
All research outputs
#13,418,483
of 22,774,233 outputs
Outputs from BMC Bioinformatics
#4,192
of 7,276 outputs
Outputs of similar age
#174,037
of 354,732 outputs
Outputs of similar age from BMC Bioinformatics
#61
of 135 outputs
Altmetric has tracked 22,774,233 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,276 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
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 354,732 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 135 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 50% of its contemporaries.