| Title |
Output ordering and prioritisation system (OOPS): ranking biosynthetic gene clusters to enhance bioactive metabolite discovery
|
|---|---|
| Published in |
Journal of Industrial Microbiology & Biotechnology, July 2018
|
| DOI | 10.1007/s10295-017-1993-1 |
| Pubmed ID | |
| Authors |
Alejandro Peña, Francesco Del Carratore, Matthew Cummings, Eriko Takano, Rainer Breitling |
| Abstract |
The rapid increase of publicly available microbial genome sequences has highlighted the presence of hundreds of thousands of biosynthetic gene clusters (BGCs) encoding valuable secondary metabolites. The experimental characterization of new BGCs is extremely laborious and struggles to keep pace with the in silico identification of potential BGCs. Therefore, the prioritisation of promising candidates among computationally predicted BGCs represents a pressing need. Here, we propose an output ordering and prioritisation system (OOPS) which helps sorting identified BGCs by a wide variety of custom-weighted biological and biochemical criteria in a flexible and user-friendly interface. OOPS facilitates a judicious prioritisation of BGCs using G+C content, coding sequence length, gene number, cluster self-similarity and codon bias parameters, as well as enabling the user to rank BGCs based upon BGC type, novelty, and taxonomic distribution. Effective prioritisation of BGCs will help to reduce experimental attrition rates and improve the breadth of bioactive metabolites characterized. |
Mendeley readers
The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 22 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 9 | 41% |
| Student > Master | 3 | 14% |
| Student > Doctoral Student | 2 | 9% |
| Researcher | 2 | 9% |
| Professor | 1 | 5% |
| Other | 2 | 9% |
| Unknown | 3 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 9 | 41% |
| Agricultural and Biological Sciences | 4 | 18% |
| Immunology and Microbiology | 2 | 9% |
| Unspecified | 1 | 5% |
| Engineering | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 5 | 23% |