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A comprehensive and scalable database search system for metaproteomics

Overview of attention for article published in BMC Genomics, August 2016
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  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

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Title
A comprehensive and scalable database search system for metaproteomics
Published in
BMC Genomics, August 2016
DOI 10.1186/s12864-016-2855-3
Pubmed ID
Authors

Sandip Chatterjee, Gregory S. Stupp, Sung Kyu Robin Park, Jean-Christophe Ducom, John R. Yates, Andrew I. Su, Dennis W. Wolan

Abstract

Mass spectrometry-based shotgun proteomics experiments rely on accurate matching of experimental spectra against a database of protein sequences. Existing computational analysis methods are limited in the size of their sequence databases, which severely restricts the proteomic sequencing depth and functional analysis of highly complex samples. The growing amount of public high-throughput sequencing data will only exacerbate this problem. We designed a broadly applicable metaproteomic analysis method (ComPIL) that addresses protein database size limitations. Our approach to overcome this significant limitation in metaproteomics was to design a scalable set of sequence databases assembled for optimal library querying speeds. ComPIL was integrated with a modified version of the search engine ProLuCID (termed "Blazmass") to permit rapid matching of experimental spectra. Proof-of-principle analysis of human HEK293 lysate with a ComPIL database derived from high-quality genomic libraries was able to detect nearly all of the same peptides as a search with a human database (~500x fewer peptides in the database), with a small reduction in sensitivity. We were also able to detect proteins from the adenovirus used to immortalize these cells. We applied our method to a set of healthy human gut microbiome proteomic samples and showed a substantial increase in the number of identified peptides and proteins compared to previous metaproteomic analyses, while retaining a high degree of protein identification accuracy and allowing for a more in-depth characterization of the functional landscape of the samples. The combination of ComPIL with Blazmass allows proteomic searches to be performed with database sizes much larger than previously possible. These large database searches can be applied to complex meta-samples with unknown composition or proteomic samples where unexpected proteins may be identified. The protein database, proteomic search engine, and the proteomic data files for the 5 microbiome samples characterized and discussed herein are open source and available for use and additional analysis.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 2%
United Kingdom 1 <1%
United States 1 <1%
South Africa 1 <1%
Unknown 111 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 26 22%
Researcher 20 17%
Student > Master 12 10%
Student > Bachelor 12 10%
Professor > Associate Professor 7 6%
Other 18 16%
Unknown 21 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 28%
Biochemistry, Genetics and Molecular Biology 25 22%
Engineering 6 5%
Computer Science 5 4%
Immunology and Microbiology 5 4%
Other 17 15%
Unknown 26 22%
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 18 August 2017.
All research outputs
#7,476,128
of 24,093,053 outputs
Outputs from BMC Genomics
#3,358
of 10,906 outputs
Outputs of similar age
#105,342
of 318,817 outputs
Outputs of similar age from BMC Genomics
#83
of 265 outputs
Altmetric has tracked 24,093,053 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 10,906 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 67% 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 318,817 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 65% of its contemporaries.
We're also able to compare this research output to 265 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 66% of its contemporaries.