Name
Abstract | ||
|---|---|---|
Advances in de novo synthesis of DNA and computational gene design methods make possible the customization of genes by direct manipulation of features such as codon bias and mRNA secondary structure. Codon context is another feature significantly affecting mRNA translational efficiency, but existing methods and tools for evaluating and designing novel optimized protein coding sequences utilize untested heuristics and do not provide quantifiable guarantees on design quality. In this study we examine statistical properties of codon context measures in an effort to better understand the phenomenon. We analyze the computational complexity of codon context optimization and design exact and efficient heuristic gene recoding algorithms under reasonable constraint models. We also present a web-based tool for evaluating codon context bias in the appropriate context. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TCBB.2016.2542808 | IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM |
Keywords | Field | DocType |
Computational biology,Dynamic programming,Simulated annealing,Synthetic biology | Heuristic,Computational gene,Computer science,Coding (social sciences),Design methods,Heuristics,Artificial intelligence,Machine learning,Translational efficiency,Codon usage bias,Computational complexity theory | Journal |
Volume | Issue | ISSN |
15 | 2 | 1557-9964 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dimitris Papamichail | 1 | 4 | 4.86 |
| hongmei liu | 2 | 0 | 1.69 |
| vitor machado | 3 | 0 | 0.34 |
| Nathan Gould | 4 | 0 | 0.68 |
| j robert coleman | 5 | 0 | 0.34 |
| Papamichail, G. | 6 | 0 | 0.68 |