| Title |
MetaSee: An Interactive and Extendable Visualization Toolbox for Metagenomic Sample Analysis and Comparison
|
|---|---|
| Published in |
PLOS ONE, November 2012
|
| DOI | 10.1371/journal.pone.0048998 |
| Pubmed ID | |
| Authors |
Baoxing Song, Xiaoquan Su, Jian Xu, Kang Ning |
| Abstract |
The NGS (next generation sequencing)-based metagenomic data analysis is becoming the mainstream for the study of microbial communities. Faced with a large amount of data in metagenomic research, effective data visualization is important for scientists to effectively explore, interpret and manipulate such rich information. The visualization of the metagenomic data, especially multi-sample data, is one of the most critical challenges. The different data sample sources, sequencing approaches and heterogeneous data formats make robust and seamless data visualization difficult. Moreover, researchers have different focuses on metagenomic studies: taxonomical or functional, sample-centric or genome-centric, single sample or multiple samples, etc. However, current efforts in metagenomic data visualization cannot fulfill all of these needs, and it is extremely hard to organize all of these visualization effects in a systematic manner. An extendable, interactive visualization tool would be the method of choice to fulfill all of these visualization needs. In this paper, we have present MetaSee, an extendable toolbox that facilitates the interactive visualization of metagenomic samples of interests. The main components of MetaSee include: (I) a core visualization engine that is composed of different views for comparison of multiple samples: Global view, Phylogenetic view, Sample view and Taxa view, as well as link-out for more in-depth analysis; (II) front-end user interface with real metagenomic models that connect to the above core visualization engine and (III) open-source portal for the development of plug-ins for MetaSee. This integrative visualization tool not only provides the visualization effects, but also enables researchers to perform in-depth analysis of the metagenomic samples of interests. Moreover, its open-source portal allows for the design of plug-ins for MetaSee, which would facilitate the development of any additional visualization effects. |
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 5 | 42% |
| Peru | 1 | 8% |
| Germany | 1 | 8% |
| Canada | 1 | 8% |
| Unknown | 4 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 7 | 58% |
| Members of the public | 4 | 33% |
| Science communicators (journalists, bloggers, editors) | 1 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| Canada | 2 | 2% |
| Spain | 2 | 2% |
| Sweden | 1 | 1% |
| India | 1 | 1% |
| Brazil | 1 | 1% |
| Belgium | 1 | 1% |
| Germany | 1 | 1% |
| France | 1 | 1% |
| Other | 1 | 1% |
| Unknown | 86 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 30 | 30% |
| Student > Ph. D. Student | 24 | 24% |
| Student > Master | 12 | 12% |
| Student > Doctoral Student | 7 | 7% |
| Student > Bachelor | 6 | 6% |
| Other | 13 | 13% |
| Unknown | 8 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 51 | 51% |
| Biochemistry, Genetics and Molecular Biology | 11 | 11% |
| Environmental Science | 6 | 6% |
| Computer Science | 4 | 4% |
| Immunology and Microbiology | 3 | 3% |
| Other | 12 | 12% |
| Unknown | 13 | 13% |