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Attention Score in Context
Chapter title |
Small RNA Sequencing: A Technique for miRNA Profiling
|
---|---|
Chapter number | 21 |
Book title |
Investigations of Early Nutrition Effects on Long-Term Health
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7614-0_21 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7613-3, 978-1-4939-7614-0
|
Authors |
Lucas Carminatti Pantaleão, Susan E. Ozanne, Pantaleão, Lucas Carminatti, Ozanne, Susan E. |
Abstract |
Identifying microRNA (miRNA) signatures in animal tissues is an essential first step in studies assessing post-transcriptional regulation of gene expression in health or disease. Small RNA sequencing (sRNA-Seq) is a next-generation sequencing-based technology that is currently considered the most powerful and versatile tool for miRNA profiling. Here, we describe a sRNA-Seq protocol including RNA purification from mammalian tissues, library preparation, and raw data analysis. |
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.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 30 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 6 | 20% |
Researcher | 5 | 17% |
Student > Ph. D. Student | 4 | 13% |
Student > Bachelor | 1 | 3% |
Other | 1 | 3% |
Other | 1 | 3% |
Unknown | 12 | 40% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 10 | 33% |
Neuroscience | 2 | 7% |
Agricultural and Biological Sciences | 1 | 3% |
Computer Science | 1 | 3% |
Immunology and Microbiology | 1 | 3% |
Other | 5 | 17% |
Unknown | 10 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 31 January 2018.
All research outputs
#20,461,148
of 23,018,998 outputs
Outputs from Methods in molecular biology
#9,945
of 13,165 outputs
Outputs of similar age
#378,193
of 442,354 outputs
Outputs of similar age from Methods in molecular biology
#1,193
of 1,498 outputs
Altmetric has tracked 23,018,998 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,165 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% 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 442,354 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,498 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.