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X Demographics
Mendeley readers
Attention Score in Context
| Title |
ASAP: an environment for automated preprocessing of sequencing data
|
|---|---|
| Published in |
BMC Research Notes, January 2013
|
| DOI | 10.1186/1756-0500-6-5 |
| Pubmed ID | |
| Authors |
Eric S Torstenson, Bingshan Li, Chun Li |
| Abstract |
Next-generation sequencing (NGS) has yielded an unprecedented amount of data for genetics research. It is a daunting task to process the data from raw sequence reads to variant calls and manually processing this data can significantly delay downstream analysis and increase the possibility for human error. The research community has produced tools to properly prepare sequence data for analysis and established guidelines on how to apply those tools to achieve the best results, however, existing pipeline programs to automate the process through its entirety are either inaccessible to investigators, or web-based and require a certain amount of administrative expertise to set up. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 33% |
| France | 1 | 33% |
| Belgium | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 31 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 39% |
| Student > Ph. D. Student | 6 | 19% |
| Student > Master | 3 | 10% |
| Student > Bachelor | 2 | 6% |
| Professor > Associate Professor | 2 | 6% |
| Other | 4 | 13% |
| Unknown | 2 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 12 | 39% |
| Computer Science | 6 | 19% |
| Social Sciences | 2 | 6% |
| Medicine and Dentistry | 2 | 6% |
| Business, Management and Accounting | 1 | 3% |
| Other | 3 | 10% |
| Unknown | 5 | 16% |
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 09 January 2013.
All research outputs
#13,678,432
of 22,691,736 outputs
Outputs from BMC Research Notes
#1,783
of 4,255 outputs
Outputs of similar age
#161,874
of 280,650 outputs
Outputs of similar age from BMC Research Notes
#18
of 51 outputs
Altmetric has tracked 22,691,736 research outputs across all sources so far. This one is in the 38th percentile – i.e., 38% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,255 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 gotten more attention than average, scoring higher than 56% 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 280,650 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 51 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 64% of its contemporaries.