The Problem

Codon optimization is an indispensable process in modern biotechnology and biomedicine tackling low expression yields of recombinant proteins, which has long hampered biomedical research and product development. The root cause of codon bias in each organism is attributed to the degeneracy of the genetic code, and codon optimisation resolves this through the strategy of using synonymous codons whilst maintaining the original protein sequence.

To make the most out of this strategy, efficient algorithms are needed to calculate a coding sequence, combining different requirements, such as adapted codon usage (substituting rare codons with more frequent ones) or avoidance of restriction sites, in the best possible way.

Current optimization tools are plagued with numerous problems. Free codon optimization provided by synthetic gene companies perform unreliably and does not provide the customer with insights on the intricate workings of the software. This black box hinders scientists from knowing what has been done to their sequences; it is not good scientific practice to press a button in a piece of software and interpret the results without understanding what the software does.

The Solution

Codon Usage Optimizer (CUO) focuses on providing usability, performance and flexibility to molecular biologists. It features modular codon optimization tools that can be created, posted, reused, revised, improved and shared without any limitation. The open source platform provides a melting pot for the evolution of sequence optimization technology, which is steadily taking off owing to the falling price of gene sequencing and synthesis.

In addition, services are included such as one-on-one & group training, consulting, and technical support tailored to tackle issues around the genome optimization project.

With the price of DNA sequencing and synthesis falling rapidly, codon optimisation is set to become a protocol norm for academic and industrial adoption. One of the main advantages of de novo gene synthesis is the fact that researchers are freed from any limitations imposed by the use of natural templates. Below is a summary of CUO’s advantages:

• Reliable expression
• Increased protein yield
• Protein solubility unaffected
• Unaltered functionality

Apart from that, optimised synthetic genes have recently become an invaluable tool in RNAi work. By performing gene ‘silencing and rescue’ experiments, researchers can differentiate between a true cellular phenotype and so-called off-target effects, since siRNA may conjointly trigger a multitude of unspecific secondary mechanisms. Optimised genes have also contributed a fair share to HIV research, by increasing the stability of certain mRNAs by orders of magnitude.

Services

* Prices are for UCL and the Greater London area. For other regions, prices are subject to change.

We also sell 1 and 3 year software licenses which include all of the above packages for single and multiple users. Please contact us for a price quote.

The Competition

In order to better understand the attributes of our competitors we undertook a survey of users of codon optimization utilities at UCL and surrounding research institutes.

The following graph summarizes the added value we provide to the customer based on our software usability criteria checklist. We used the Value Curve popularised by academics W Chan Kim and Renee Mauborgne in their Harvard Business Review article from January 1997 entitled “Value Innovation: The Strategic Logic of High Growth” to differentiate CUO from its four strongest competitors, EMBOSS, GeneOptimiser, GeneDesigner and General Codon Usage Analysis (GCUA), via visualising the value propositions.

To start with, we put together a simple chart as shown below. On the y-axis we have a spectrum of arbitrary values from 0 to 10. This has been calculated based on converting raw scores from the software usability criteria checklist (refer to Appendix). On the x-axis we record a number of features of elements that define the software: user friendly, modular, developer friendly, validation, interoperable and popularity.

Looking at the Value Curve, it is very obvious that CUO (shown in blue) follows closely with EMBOSS (in red) and does not fall behind its other competitors. Both CUO and EMBOSS display high degrees of consistency. Our current lack of popularity is offsetted by our pole position in user-friendliness and high rankings in modularity & developer-friendliness, reflecting our product strategy – that is to create software providing usability, performance and flexibility to users. CUO is most behind on validation as it has only been validated on C.reinhardtii, compared to GeneDesigner (shown in turquoise) which is funded heavily by DNA 2.0.