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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Shape-based peak identification for ChIP-Seq
|
|---|---|
| Published in |
BMC Bioinformatics, January 2011
|
| DOI | 10.1186/1471-2105-12-15 |
| Pubmed ID | |
| Authors |
Valerie Hower, Steven N Evans, Lior Pachter |
| Abstract |
The identification of binding targets for proteins using ChIP-Seq has gained popularity as an alternative to ChIP-chip. Sequencing can, in principle, eliminate artifacts associated with microarrays, and cheap sequencing offers the ability to sequence deeply and obtain a comprehensive survey of binding. A number of algorithms have been developed to call "peaks" representing bound regions from mapped reads. Most current algorithms incorporate multiple heuristics, and despite much work it remains difficult to accurately determine individual peaks corresponding to distinct binding events. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 138 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 5% |
| United Kingdom | 6 | 4% |
| Germany | 3 | 2% |
| France | 2 | 1% |
| Canada | 2 | 1% |
| Austria | 1 | <1% |
| Denmark | 1 | <1% |
| Australia | 1 | <1% |
| Russia | 1 | <1% |
| Other | 3 | 2% |
| Unknown | 111 | 80% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 44 | 32% |
| Researcher | 42 | 30% |
| Professor > Associate Professor | 12 | 9% |
| Other | 9 | 7% |
| Student > Master | 6 | 4% |
| Other | 19 | 14% |
| Unknown | 6 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 88 | 64% |
| Biochemistry, Genetics and Molecular Biology | 20 | 14% |
| Computer Science | 9 | 7% |
| Mathematics | 4 | 3% |
| Medicine and Dentistry | 4 | 3% |
| Other | 7 | 5% |
| Unknown | 6 | 4% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 19 March 2024.
All research outputs
#7,148,724
of 23,342,092 outputs
Outputs from BMC Bioinformatics
#2,745
of 7,388 outputs
Outputs of similar age
#52,002
of 183,712 outputs
Outputs of similar age from BMC Bioinformatics
#13
of 53 outputs
Altmetric has tracked 23,342,092 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 7,388 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 61% 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 183,712 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 53 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 71% of its contemporaries.