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Attention Score in Context
| Title |
Integrating multiple ‘omics’ analysis for microbial biology: application and methodologies
|
|---|---|
| Published in |
Microbiology, November 2009
|
| DOI | 10.1099/mic.0.034793-0 |
| Pubmed ID | |
| Authors |
Weiwen Zhang, Feng Li, Lei Nie |
| Abstract |
Recent advances in various 'omics' technologies enable quantitative monitoring of the abundance of various biological molecules in a high-throughput manner, and thus allow determination of their variation between different biological states on a genomic scale. Several popular 'omics' platforms that have been used in microbial systems biology include transcriptomics, which measures mRNA transcript levels; proteomics, which quantifies protein abundance; metabolomics, which determines abundance of small cellular metabolites; interactomics, which resolves the whole set of molecular interactions in cells; and fluxomics, which establishes dynamic changes of molecules within a cell over time. However, no single 'omics' analysis can fully unravel the complexities of fundamental microbial biology. Therefore, integration of multiple layers of information, the multi-'omics' approach, is required to acquire a precise picture of living micro-organisms. In spite of this being a challenging task, some attempts have been made recently to integrate heterogeneous 'omics' datasets in various microbial systems and the results have demonstrated that the multi-'omics' approach is a powerful tool for understanding the functional principles and dynamics of total cellular systems. This article reviews some basic concepts of various experimental 'omics' approaches, recent application of the integrated 'omics' for exploring metabolic and regulatory mechanisms in microbes, and advances in computational and statistical methodologies associated with integrated 'omics' analyses. Online databases and bioinformatic infrastructure available for integrated 'omics' analyses are also briefly discussed. |
Mendeley readers
The data shown below were compiled from readership statistics for 927 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 13 | 1% |
| United Kingdom | 10 | 1% |
| Germany | 6 | <1% |
| Belgium | 5 | <1% |
| Denmark | 5 | <1% |
| Sweden | 5 | <1% |
| Canada | 5 | <1% |
| Brazil | 4 | <1% |
| India | 3 | <1% |
| Other | 22 | 2% |
| Unknown | 849 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 241 | 26% |
| Researcher | 213 | 23% |
| Student > Master | 126 | 14% |
| Student > Bachelor | 67 | 7% |
| Student > Doctoral Student | 51 | 6% |
| Other | 141 | 15% |
| Unknown | 88 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 419 | 45% |
| Biochemistry, Genetics and Molecular Biology | 141 | 15% |
| Computer Science | 45 | 5% |
| Engineering | 31 | 3% |
| Chemistry | 31 | 3% |
| Other | 145 | 16% |
| Unknown | 115 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 February 2024.
All research outputs
#2,863,790
of 25,373,627 outputs
Outputs from Microbiology
#255
of 5,709 outputs
Outputs of similar age
#9,835
of 106,663 outputs
Outputs of similar age from Microbiology
#4
of 37 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,709 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 94% 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 106,663 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 90% 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 89% of its contemporaries.