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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Combining multiple ChIP-seq peak detection systems using combinatorial fusion
|
|---|---|
| Published in |
BMC Genomics, December 2012
|
| DOI | 10.1186/1471-2164-13-s8-s12 |
| Pubmed ID | |
| Authors |
Christina Schweikert, Stuart Brown, Zuojian Tang, Phillip R Smith, D Frank Hsu |
| Abstract |
Due to the recent rapid development in ChIP-seq technologies, which uses high-throughput next-generation DNA sequencing to identify the targets of Chromatin Immunoprecipitation, there is an increasing amount of sequencing data being generated that provides us with greater opportunity to analyze genome-wide protein-DNA interactions. In particular, we are interested in evaluating and enhancing computational and statistical techniques for locating protein binding sites. Many peak detection systems have been developed; in this study, we utilize the following six: CisGenome, MACS, PeakSeq, QuEST, SISSRs, and TRLocator. |
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 % |
|---|---|---|
| Germany | 2 | 67% |
| United States | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 2 | 4% |
| Germany | 1 | 2% |
| Korea, Republic of | 1 | 2% |
| Canada | 1 | 2% |
| Unknown | 46 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 24% |
| Researcher | 12 | 24% |
| Student > Master | 6 | 12% |
| Professor > Associate Professor | 4 | 8% |
| Student > Doctoral Student | 4 | 8% |
| Other | 9 | 18% |
| Unknown | 4 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 27 | 53% |
| Computer Science | 7 | 14% |
| Biochemistry, Genetics and Molecular Biology | 6 | 12% |
| Engineering | 4 | 8% |
| Medicine and Dentistry | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 5 | 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 16 January 2013.
All research outputs
#16,722,190
of 25,374,917 outputs
Outputs from BMC Genomics
#6,569
of 11,244 outputs
Outputs of similar age
#179,747
of 275,907 outputs
Outputs of similar age from BMC Genomics
#110
of 202 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 36th percentile – i.e., 36% 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 275,907 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 202 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.