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Mendeley readers
Attention Score in Context
Chapter title |
ChIP-Seq for Genome-Scale Analysis of Bacterial DNA-Binding Proteins.
|
---|---|
Chapter number | 20 |
Book title |
Bacterial Transcriptional Control
|
Published in |
Methods in molecular biology, January 2015
|
DOI | 10.1007/978-1-4939-2392-2_20 |
Pubmed ID | |
Book ISBNs |
978-1-4939-2391-5, 978-1-4939-2392-2
|
Authors |
Richard P Bonocora, Joseph T Wade, Bonocora RP, Wade JT, Richard P. Bonocora, Joseph T. Wade, Bonocora, Richard P., Wade, Joseph T. |
Abstract |
Protein-DNA interactions are central to many basic biological processes, including transcription regulation, DNA replication, and DNA repair. Chromatin Immunoprecipitation (ChIP) is used to determine the position and strength of protein-DNA interactions in vivo. Coupling ChIP with microarrays (ChIP-chip), and more recently with deep sequencing (ChIP-seq), has allowed genome-wide profiling of DNA binding events in vivo. In this chapter we outline the steps to generate ChIP-seq libraries from bacterial samples and briefly discuss basic analysis of the data. |
X Demographics
The data shown below were collected from the profiles of 4 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 | 25% |
France | 1 | 25% |
Unknown | 2 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 75% |
Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 3% |
Unknown | 36 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 11 | 30% |
Student > Bachelor | 8 | 22% |
Researcher | 6 | 16% |
Student > Doctoral Student | 2 | 5% |
Student > Master | 2 | 5% |
Other | 1 | 3% |
Unknown | 7 | 19% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 10 | 27% |
Biochemistry, Genetics and Molecular Biology | 9 | 24% |
Immunology and Microbiology | 6 | 16% |
Business, Management and Accounting | 1 | 3% |
Veterinary Science and Veterinary Medicine | 1 | 3% |
Other | 2 | 5% |
Unknown | 8 | 22% |
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 21 July 2016.
All research outputs
#14,215,235
of 22,789,076 outputs
Outputs from Methods in molecular biology
#4,177
of 13,096 outputs
Outputs of similar age
#186,634
of 352,984 outputs
Outputs of similar age from Methods in molecular biology
#268
of 996 outputs
Altmetric has tracked 22,789,076 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,096 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 64% 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 352,984 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 996 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 70% of its contemporaries.