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ProteoModlR for functional proteomic analysis

Overview of attention for article published in BMC Bioinformatics, March 2017
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  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

twitter
7 tweeters

Citations

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4 Dimensions

Readers on

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25 Mendeley
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Title
ProteoModlR for functional proteomic analysis
Published in
BMC Bioinformatics, March 2017
DOI 10.1186/s12859-017-1563-6
Pubmed ID
Authors

Paolo Cifani, Mojdeh Shakiba, Sagar Chhangawala, Alex Kentsis

Abstract

High-accuracy mass spectrometry enables near comprehensive quantification of the components of the cellular proteomes, increasingly including their chemically modified variants. Likewise, large-scale libraries of quantified synthetic peptides are becoming available, enabling absolute quantification of chemically modified proteoforms, and therefore systems-level analyses of changes of their absolute abundance and stoichiometry. Existing computational methods provide advanced tools for mass spectral analysis and statistical inference, but lack integrated functions for quantitative analysis of post-translationally modified proteins and their modification stoichiometry. Here, we develop ProteoModlR, a program for quantitative analysis of abundance and stoichiometry of post-translational chemical modifications across temporal and steady-state biological states. While ProteoModlR is intended for the analysis of experiments using isotopically labeled reference peptides for absolute quantitation, it also supports the analysis of labeled and label-free data, acquired in both data-dependent and data-independent modes for relative quantitation. Moreover, ProteoModlR enables functional analysis of sparsely sampled quantitative mass spectrometry experiments by inferring the missing values from the available measurements, without imputation. The implemented architecture includes parsing and normalization functions to control for common sources of technical variation. Finally, ProteoModlR's modular design and interchangeable format are optimally suited for integration with existing computational proteomics tools, thereby facilitating comprehensive quantitative analysis of cellular signaling. ProteoModlR and its documentation are available for download at http://github.com/kentsisresearchgroup/ProteoModlR as a stand-alone R package.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 32%
Researcher 5 20%
Professor > Associate Professor 4 16%
Student > Bachelor 3 12%
Student > Master 1 4%
Other 1 4%
Unknown 3 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 32%
Biochemistry, Genetics and Molecular Biology 6 24%
Computer Science 2 8%
Engineering 2 8%
Earth and Planetary Sciences 1 4%
Other 1 4%
Unknown 5 20%

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 10 September 2017.
All research outputs
#3,172,779
of 11,741,833 outputs
Outputs from BMC Bioinformatics
#1,519
of 4,297 outputs
Outputs of similar age
#88,862
of 258,313 outputs
Outputs of similar age from BMC Bioinformatics
#40
of 114 outputs
Altmetric has tracked 11,741,833 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 4,297 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 64% 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 258,313 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 114 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 64% of its contemporaries.