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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Using Proteomics Bioinformatics Tools and Resources in Proteogenomic Studies.
|
|---|---|
| Chapter number | 5 |
| Book title |
Proteogenomics
|
| Published in |
Advances in experimental medicine and biology, September 2016
|
| DOI | 10.1007/978-3-319-42316-6_5 |
| Pubmed ID | |
| Book ISBNs |
978-3-31-942314-2, 978-3-31-942316-6
|
| Authors |
Marc Vaudel, Harald Barsnes, Helge Ræder, Frode S. Berven |
| Editors |
Ákos Végvári |
| Abstract |
Proteogenomic studies ally the omic fields related to gene expression into a combined approach to improve the characterization of biological samples. Part of this consists in mining proteomics datasets for non-canonical sequences of amino acids. These include intergenic peptides, products of mutations, or of RNA editing events hypothesized from genomic, epigenomic, or transcriptomic data. This approach poses new challenges for standard peptide identification workflows. In this chapter, we present the principles behind the use of peptide identification algorithms and highlight the major pitfalls of their application to proteogenomic studies. |
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 % |
|---|---|---|
| United States | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 6% |
| Unknown | 17 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 3 | 17% |
| Student > Ph. D. Student | 2 | 11% |
| Researcher | 2 | 11% |
| Student > Master | 2 | 11% |
| Student > Bachelor | 1 | 6% |
| Other | 4 | 22% |
| Unknown | 4 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 7 | 39% |
| Agricultural and Biological Sciences | 4 | 22% |
| Computer Science | 3 | 17% |
| Unknown | 4 | 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 04 July 2017.
All research outputs
#14,862,678
of 22,890,496 outputs
Outputs from Advances in experimental medicine and biology
#2,270
of 4,952 outputs
Outputs of similar age
#194,462
of 322,482 outputs
Outputs of similar age from Advances in experimental medicine and biology
#53
of 119 outputs
Altmetric has tracked 22,890,496 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,952 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 49th percentile – i.e., 49% 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 322,482 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 119 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 50% of its contemporaries.