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Standard Flow Liquid Chromatography for Shotgun Proteomics in Bioenergy Research

Overview of attention for article published in Frontiers in Bioengineering and Biotechnology, April 2015
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Title
Standard Flow Liquid Chromatography for Shotgun Proteomics in Bioenergy Research
Published in
Frontiers in Bioengineering and Biotechnology, April 2015
DOI 10.3389/fbioe.2015.00044
Pubmed ID
Authors

Susana M. González Fernández-Niño, A. Michelle Smith-Moritz, Leanne Jade G. Chan, Paul D. Adams, Joshua L. Heazlewood, Christopher J. Petzold

Abstract

Over the past 10 years, the bioenergy field has realized significant achievements that have encouraged many follow on efforts centered on biosynthetic production of fuel-like compounds. Key to the success of these efforts has been transformational developments in feedstock characterization and metabolic engineering of biofuel-producing microbes. Lagging far behind these advancements are analytical methods to characterize and quantify systems of interest to the bioenergy field. In particular, the utilization of proteomics, while valuable for identifying novel enzymes and diagnosing problems associated with biofuel-producing microbes, is limited by a lack of robustness and limited throughput. Nano-flow liquid chromatography coupled to high-mass accuracy, high-resolution mass spectrometers has become the dominant approach for the analysis of complex proteomic samples, yet such assays still require dedicated experts for data acquisition, analysis, and instrument upkeep. The recent adoption of standard flow chromatography (ca. 0.5 mL/min) for targeted proteomics has highlighted the robust nature and increased throughput of this approach for sample analysis. Consequently, we assessed the applicability of standard flow liquid chromatography for shotgun proteomics using samples from Escherichia coli and Arabidopsis thaliana, organisms commonly used as model systems for lignocellulosic biofuels research. Employing 120 min gradients with standard flow chromatography, we were able to routinely identify nearly 800 proteins from E. coli samples; while for samples from Arabidopsis, over 1,000 proteins could be reliably identified. An examination of identified peptides indicated that the method was suitable for reproducible applications in shotgun proteomics. Standard flow liquid chromatography for shotgun proteomics provides a robust approach for the analysis of complex samples. To the best of our knowledge, this study represents the first attempt to validate the standard flow approach for shotgun proteomics.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 23%
Student > Ph. D. Student 7 13%
Student > Bachelor 6 12%
Student > Doctoral Student 5 10%
Student > Master 5 10%
Other 8 15%
Unknown 9 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 20 38%
Chemistry 6 12%
Biochemistry, Genetics and Molecular Biology 4 8%
Engineering 4 8%
Medicine and Dentistry 3 6%
Other 4 8%
Unknown 11 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 06 April 2015.
All research outputs
#18,403,994
of 22,796,179 outputs
Outputs from Frontiers in Bioengineering and Biotechnology
#3,388
of 6,524 outputs
Outputs of similar age
#193,622
of 264,673 outputs
Outputs of similar age from Frontiers in Bioengineering and Biotechnology
#36
of 52 outputs
Altmetric has tracked 22,796,179 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,524 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 29th percentile – i.e., 29% 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 264,673 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.