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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Improving read mapping using additional prefix grams
|
|---|---|
| Published in |
BMC Bioinformatics, February 2014
|
| DOI | 10.1186/1471-2105-15-42 |
| Pubmed ID | |
| Authors |
Jongik Kim, Chen Li, Xiaohui Xie |
| Abstract |
Next-generation sequencing (NGS) enables rapid production of billions of bases at a relatively low cost. Mapping reads from next-generation sequencers to a given reference genome is an important first step in many sequencing applications. Popular read mappers, such as Bowtie and BWA, are optimized to return top one or a few candidate locations of each read. However, identifying all mapping locations of each read, instead of just one or a few, is also important in some sequencing applications such as ChIP-seq for discovering binding sites in repeat regions, and RNA-seq for transcript abundance estimation. |
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 % |
|---|---|---|
| China | 1 | 17% |
| Germany | 1 | 17% |
| Norway | 1 | 17% |
| France | 1 | 17% |
| Unknown | 2 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 50% |
| Scientists | 3 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Norway | 2 | 5% |
| Germany | 1 | 2% |
| France | 1 | 2% |
| Slovenia | 1 | 2% |
| Japan | 1 | 2% |
| United States | 1 | 2% |
| Luxembourg | 1 | 2% |
| Unknown | 33 | 80% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 32% |
| Student > Ph. D. Student | 7 | 17% |
| Student > Bachelor | 4 | 10% |
| Professor > Associate Professor | 4 | 10% |
| Student > Master | 3 | 7% |
| Other | 7 | 17% |
| Unknown | 3 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 17 | 41% |
| Biochemistry, Genetics and Molecular Biology | 8 | 20% |
| Computer Science | 7 | 17% |
| Immunology and Microbiology | 2 | 5% |
| Business, Management and Accounting | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 5 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 16 February 2014.
All research outputs
#3,288,737
of 23,812,962 outputs
Outputs from BMC Bioinformatics
#1,136
of 7,450 outputs
Outputs of similar age
#39,855
of 311,542 outputs
Outputs of similar age from BMC Bioinformatics
#17
of 99 outputs
Altmetric has tracked 23,812,962 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,450 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 84% 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 311,542 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 87% of its contemporaries.
We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.