Comparative Genomics
Springer US
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Attention Score in Context
| Chapter title |
Comparative Genomic Analysis of Bacterial Data in BV-BRC: An Example Exploring Antimicrobial Resistance.
|
|---|---|
| Chapter number | 18 |
| Book title |
Comparative Genomics
|
| Published in |
Methods in molecular biology, January 2024
|
| DOI | 10.1007/978-1-0716-3838-5_18 |
| Pubmed ID | |
| Book ISBNs |
978-1-07-163837-8, 978-1-07-163838-5
|
| Authors |
Alice R Wattam, Nicole Bowers, Thomas Brettin, Neal Conrad, Clark Cucinell, James J Davis, Allan W Dickerman, Emily M Dietrich, Ronald W Kenyon, Dustin Machi, Chunhong Mao, Marcus Nguyen, Robert D Olson, Ross Overbeek, Bruce Parrello, Gordon D Pusch, Maulik Shukla, Rick L Stevens, Veronika Vonstein, Andrew S Warren, Wattam, Alice R., Bowers, Nicole, Brettin, Thomas, Conrad, Neal, Cucinell, Clark, Davis, James J., Dickerman, Allan W., Dietrich, Emily M., Kenyon, Ronald W., Machi, Dustin, Mao, Chunhong, Nguyen, Marcus, Olson, Robert D., Overbeek, Ross, Parrello, Bruce, Pusch, Gordon D., Shukla, Maulik, Stevens, Rick L., Vonstein, Veronika, Warren, Andrew S. |
| Abstract |
As genomic and related data continue to expand, research biologists are often hampered by the computational hurdles required to analyze their data. The National Institute of Allergy and Infectious Diseases (NIAID) established the Bioinformatics Resource Centers (BRC) to assist researchers with their analysis of genome sequence and other omics-related data. Recently, the PAThosystems Resource Integration Center (PATRIC), the Influenza Research Database (IRD), and the Virus Pathogen Database and Analysis Resource (ViPR) BRCs merged to form the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) at https://www.bv-brc.org/ . The combined BV-BRC leverages the functionality of the original resources for bacterial and viral research communities with a unified data model, enhanced web-based visualization and analysis tools, and bioinformatics services. Here we demonstrate how antimicrobial resistance data can be analyzed in the new resource. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
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 10 June 2024.
All research outputs
#16,554,801
of 26,112,783 outputs
Outputs from Methods in molecular biology
#4,954
of 14,529 outputs
Outputs of similar age
#171,206
of 370,095 outputs
Outputs of similar age from Methods in molecular biology
#91
of 337 outputs
Altmetric has tracked 26,112,783 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,529 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 62% 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 370,095 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 50% of its contemporaries.
We're also able to compare this research output to 337 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 69% of its contemporaries.