| Title |
xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data
|
|---|---|
| Published in |
BMC Bioinformatics, January 2013
|
| DOI | 10.1186/1471-2105-14-15 |
| Pubmed ID | |
| Authors |
Karan Uppal, Quinlyn A Soltow, Frederick H Strobel, W Stephen Pittard, Kim M Gernert, Tianwei Yu, Dean P Jones |
| Abstract |
Detection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or environmental exposures. Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection of low abundance chemicals and chemicals found in small subsets of samples. The present study provides software to enhance such algorithms for feature detection, quality assessment, and annotation. |
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 % |
|---|---|---|
| India | 1 | 50% |
| Norway | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 193 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 2% |
| United States | 3 | 2% |
| Netherlands | 2 | 1% |
| Brazil | 2 | 1% |
| Denmark | 1 | <1% |
| South Africa | 1 | <1% |
| Unknown | 181 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 49 | 25% |
| Researcher | 47 | 24% |
| Student > Master | 21 | 11% |
| Professor | 13 | 7% |
| Student > Doctoral Student | 9 | 5% |
| Other | 26 | 13% |
| Unknown | 28 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 33 | 17% |
| Chemistry | 28 | 15% |
| Biochemistry, Genetics and Molecular Biology | 26 | 13% |
| Medicine and Dentistry | 15 | 8% |
| Computer Science | 10 | 5% |
| Other | 39 | 20% |
| Unknown | 42 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 17 November 2022.
All research outputs
#5,080,637
of 24,832,302 outputs
Outputs from BMC Bioinformatics
#1,801
of 7,594 outputs
Outputs of similar age
#52,599
of 296,637 outputs
Outputs of similar age from BMC Bioinformatics
#33
of 137 outputs
Altmetric has tracked 24,832,302 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,594 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 done well, scoring higher than 75% 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 296,637 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 137 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.