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Improvement of peptide identification with considering the abundance of mRNA and peptide

Overview of attention for article published in BMC Bioinformatics, February 2017
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Title
Improvement of peptide identification with considering the abundance of mRNA and peptide
Published in
BMC Bioinformatics, February 2017
DOI 10.1186/s12859-017-1491-5
Pubmed ID
Authors

Chunwei Ma, Shaohang Xu, Geng Liu, Xin Liu, Xun Xu, Bo Wen, Siqi Liu

Abstract

Tandem mass spectrometry (MS/MS) followed by database search is a main approach to identify peptides/proteins in proteomic studies. A lot of effort has been devoted to improve the identification accuracy and sensitivity for peptides/proteins, such as developing advanced algorithms and expanding protein databases. Herein, we described a new strategy for enhancing the sensitivity of protein/peptide identification through combination of mRNA and peptide abundance in Percolator. In our strategy, a new workflow for peptide identification is established on the basis of the abundance of transcripts and potential novel transcripts derived from RNA-Seq and abundance of peptides towards the same life species. We demonstrate the utility of this strategy by two MS/MS datasets and the results indicate that about 5% ~ 8% improvement of peptide identification can be achieved with 1% FDR in peptide level by integrating the peptide abundance, the transcript abundance and potential novel transcripts from RNA-Seq data. Meanwhile, 181 and 154 novel peptides were identified in the two datasets, respectively. We have demonstrated that this strategy could enable improvement of peptide/protein identification and discovery of novel peptides, as compared with the traditional search methods.

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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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 15%
Student > Master 6 15%
Student > Bachelor 5 13%
Student > Ph. D. Student 5 13%
Professor > Associate Professor 3 8%
Other 6 15%
Unknown 9 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 28%
Agricultural and Biological Sciences 7 18%
Computer Science 6 15%
Medicine and Dentistry 2 5%
Engineering 2 5%
Other 3 8%
Unknown 9 23%
Attention Score in Context

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 22 August 2017.
All research outputs
#14,920,678
of 22,955,959 outputs
Outputs from BMC Bioinformatics
#5,064
of 7,308 outputs
Outputs of similar age
#185,429
of 307,002 outputs
Outputs of similar age from BMC Bioinformatics
#88
of 149 outputs
Altmetric has tracked 22,955,959 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,308 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 26th percentile – i.e., 26% 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 307,002 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 149 others from the same source and published within six weeks on either side of this one. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.