↓ Skip to main content

Using the entrapment sequence method as a standard to evaluate key steps of proteomics data analysis process

Overview of attention for article published in BMC Genomics, March 2017
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age

Mentioned by

twitter
4 tweeters

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
23 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Using the entrapment sequence method as a standard to evaluate key steps of proteomics data analysis process
Published in
BMC Genomics, March 2017
DOI 10.1186/s12864-017-3491-2
Pubmed ID
Authors

Xiao-dong Feng, Li-wei Li, Jian-hong Zhang, Yun-ping Zhu, Cheng Chang, Kun-xian Shu, Jie Ma

Abstract

The mass spectrometry based technical pipeline has provided a high-throughput, high-sensitivity and high-resolution platform for post-genomic biology. Varied models and algorithms are implemented by different tools to improve proteomics data analysis. The target-decoy searching strategy has become the most popular strategy to control false identification in peptide and protein identifications. While this strategy can estimate the false discovery rate (FDR) within a dataset, it cannot directly evaluate the false positive matches in target identifications. As a supplement to target-decoy strategy, the entrapment sequence method was introduced to assess the key steps of mass spectrometry data analysis process, database search engines and quality control methods. Using the entrapment sequences as the standard, we evaluated five database search engines for both the origanal scores and reprocessed scores, as well as four quality control methods in term of quantity and quality aspects. Our results showed that the latest developed search engine MS-GF+ and percolator-embeded quality control method PepDistiller performed best in all tools respectively. Combined with efficient quality control methods, the search engines can improve the low sensitivity of their original scores. Moreover, based on the entrapment sequence method, we proved that filtering the identifications separately could increase the number of identified peptides while improving the confidence level. In this study, we have proved that the entrapment sequence method could be an useful strategy to assess the key steps of the mass spectrometry data analysis process. Its applications can be extended to all steps of the common workflow, such as the protein assembling methods and data integration methods.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 35%
Student > Ph. D. Student 6 26%
Student > Doctoral Student 2 9%
Researcher 2 9%
Student > Bachelor 2 9%
Other 3 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 35%
Agricultural and Biological Sciences 5 22%
Engineering 2 9%
Medicine and Dentistry 2 9%
Computer Science 2 9%
Other 3 13%
Unknown 1 4%

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 12 September 2017.
All research outputs
#7,031,555
of 11,753,826 outputs
Outputs from BMC Genomics
#4,096
of 6,985 outputs
Outputs of similar age
#138,720
of 265,066 outputs
Outputs of similar age from BMC Genomics
#67
of 93 outputs
Altmetric has tracked 11,753,826 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,985 research outputs from this source. They receive a mean Attention Score of 4.2. This one is in the 36th percentile – i.e., 36% 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 265,066 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 93 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.