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Improved de novo peptide sequencing using LC retention time information

Overview of attention for article published in Algorithms for Molecular Biology, August 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (57th percentile)

Mentioned by

twitter
8 tweeters

Readers on

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7 Mendeley
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Title
Improved de novo peptide sequencing using LC retention time information
Published in
Algorithms for Molecular Biology, August 2018
DOI 10.1186/s13015-018-0132-5
Pubmed ID
Authors

Yves Frank, Tomas Hruz, Thomas Tschager, Valentin Venzin

Abstract

Liquid chromatography combined with tandem mass spectrometry is an important tool in proteomics for peptide identification. Liquid chromatography temporally separates the peptides in a sample. The peptides that elute one after another are analyzed via tandem mass spectrometry by measuring the mass-to-charge ratio of a peptide and its fragments. De novo peptide sequencing is the problem of reconstructing the amino acid sequences of a peptide from this measurement data. Past de novo sequencing algorithms solely consider the mass spectrum of the fragments for reconstructing a sequence. We propose to additionally exploit the information obtained from liquid chromatography. We study the problem of computing a sequence that is not only in accordance with the experimental mass spectrum, but also with the chromatographic retention time. We consider three models for predicting the retention time and develop algorithms for de novo sequencing for each model. Based on an evaluation for two prediction models on experimental data from synthesized peptides we conclude that the identification rates are improved by exploiting the chromatographic information. In our evaluation, we compare our algorithms using the retention time information with algorithms using the same scoring model, but not the retention time.

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 43%
Unspecified 2 29%
Student > Master 1 14%
Other 1 14%
Readers by discipline Count As %
Pharmacology, Toxicology and Pharmaceutical Science 2 29%
Computer Science 2 29%
Unspecified 2 29%
Chemistry 1 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 12 September 2018.
All research outputs
#6,982,323
of 13,505,974 outputs
Outputs from Algorithms for Molecular Biology
#72
of 204 outputs
Outputs of similar age
#111,715
of 266,287 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 1 outputs
Altmetric has tracked 13,505,974 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 204 research outputs from this source. They receive a mean Attention Score of 3.0. 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 266,287 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 57% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them