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Combining peak- and chromatogram-based retention time alignment algorithms for multiple chromatography-mass spectrometry datasets

Overview of attention for article published in BMC Bioinformatics, August 2012
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  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

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2 X users
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1 patent

Citations

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Title
Combining peak- and chromatogram-based retention time alignment algorithms for multiple chromatography-mass spectrometry datasets
Published in
BMC Bioinformatics, August 2012
DOI 10.1186/1471-2105-13-214
Pubmed ID
Authors

Nils Hoffmann, Matthias Keck, Heiko Neuweger, Mathias Wilhelm, Petra Högy, Karsten Niehaus, Jens Stoye

Abstract

Modern analytical methods in biology and chemistry use separation techniques coupled to sensitive detectors, such as gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These hyphenated methods provide high-dimensional data. Comparing such data manually to find corresponding signals is a laborious task, as each experiment usually consists of thousands of individual scans, each containing hundreds or even thousands of distinct signals. In order to allow for successful identification of metabolites or proteins within such data, especially in the context of metabolomics and proteomics, an accurate alignment and matching of corresponding features between two or more experiments is required. Such a matching algorithm should capture fluctuations in the chromatographic system which lead to non-linear distortions on the time axis, as well as systematic changes in recorded intensities. Many different algorithms for the retention time alignment of GC-MS and LC-MS data have been proposed and published, but all of them focus either on aligning previously extracted peak features or on aligning and comparing the complete raw data containing all available features.

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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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 101 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 2%
Austria 1 <1%
Brazil 1 <1%
Germany 1 <1%
United Kingdom 1 <1%
South Africa 1 <1%
Russia 1 <1%
United States 1 <1%
Unknown 92 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 25 25%
Researcher 24 24%
Student > Master 11 11%
Student > Doctoral Student 7 7%
Student > Bachelor 4 4%
Other 16 16%
Unknown 14 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 33 33%
Chemistry 22 22%
Computer Science 9 9%
Biochemistry, Genetics and Molecular Biology 8 8%
Engineering 5 5%
Other 7 7%
Unknown 17 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 26 July 2018.
All research outputs
#6,752,694
of 22,675,759 outputs
Outputs from BMC Bioinformatics
#2,582
of 7,249 outputs
Outputs of similar age
#49,298
of 169,726 outputs
Outputs of similar age from BMC Bioinformatics
#31
of 99 outputs
Altmetric has tracked 22,675,759 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 7,249 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 63% 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 169,726 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 70% of its contemporaries.
We're also able to compare this research output to 99 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 67% of its contemporaries.