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MetaLab: an automated pipeline for metaproteomic data analysis

Overview of attention for article published in Microbiome, December 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

blogs
1 blog
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44 X users

Citations

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114 Dimensions

Readers on

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134 Mendeley
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1 CiteULike
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Title
MetaLab: an automated pipeline for metaproteomic data analysis
Published in
Microbiome, December 2017
DOI 10.1186/s40168-017-0375-2
Pubmed ID
Authors

Kai Cheng, Zhibin Ning, Xu Zhang, Leyuan Li, Bo Liao, Janice Mayne, Alain Stintzi, Daniel Figeys

Abstract

Research involving microbial ecosystems has drawn increasing attention in recent years. Studying microbe-microbe, host-microbe, and environment-microbe interactions are essential for the understanding of microbial ecosystems. Currently, metaproteomics provide qualitative and quantitative information of proteins, providing insights into the functional changes of microbial communities. However, computational analysis of large-scale data generated in metaproteomic studies remains a challenge. Conventional proteomic software have difficulties dealing with the extreme complexity and species diversity present in microbiome samples leading to lower rates of peptide and protein identification. To address this issue, we previously developed the MetaPro-IQ approach for highly efficient microbial protein/peptide identification and quantification. Here, we developed an integrated software platform, named MetaLab, providing a complete and automated, user-friendly pipeline for fast microbial protein identification, quantification, as well as taxonomic profiling, directly from mass spectrometry raw data. Spectral clustering adopted in the pre-processing step dramatically improved the speed of peptide identification from database searches. Quantitative information of identified peptides was used for estimating the relative abundance of taxa at all phylogenetic ranks. Taxonomy result files exported by MetaLab are fully compatible with widely used metagenomics tools. Herein, the potential of MetaLab is evaluated by reanalyzing a metaproteomic dataset from mouse gut microbiome samples. MetaLab is a fully automatic software platform enabling an integrated data-processing pipeline for metaproteomics. The function of sample-specific database generation can be very advantageous for searching peptides against huge protein databases. It provides a seamless connection between peptide determination and taxonomic profiling; therefore, the peptide abundance is readily used for measuring the microbial variations. MetaLab is designed as a versatile, efficient, and easy-to-use tool which can greatly simplify the procedure of metaproteomic data analysis for researchers in microbiome studies.

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X Demographics

The data shown below were collected from the profiles of 44 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 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 134 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 29 22%
Student > Ph. D. Student 25 19%
Student > Master 15 11%
Student > Bachelor 10 7%
Student > Doctoral Student 6 4%
Other 17 13%
Unknown 32 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 31 23%
Agricultural and Biological Sciences 25 19%
Environmental Science 7 5%
Chemistry 6 4%
Computer Science 5 4%
Other 21 16%
Unknown 39 29%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 32. 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 19 August 2020.
All research outputs
#1,197,568
of 24,885,505 outputs
Outputs from Microbiome
#382
of 1,705 outputs
Outputs of similar age
#27,246
of 449,693 outputs
Outputs of similar age from Microbiome
#9
of 37 outputs
Altmetric has tracked 24,885,505 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,705 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 38.5. This one has done well, scoring higher than 77% 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 449,693 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 93% of its contemporaries.
We're also able to compare this research output to 37 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.