Current Electronic Lab Notebooks have multiple limitations. A laboratory that wants to be future proof or is currently looking to scale their experimental and analytical workflows needs a robust tool built to scale up. Current ELNs are very helpful when designing small scale experiments, but fall short when trying to scale throughput or complexity.
TeselaGen is designed from the groundup to integrate and work well with others. This can be from your favorite Electronic Lab Notebook (ELN) to your favorite DNA vendor. Some of our current customers they just use TeselaGen, but others leverage the best of both worlds using TeselaGen’s powerful integration features to integrate with their favorite ELN.
Manage the whole Design-Build-Test-Learn cycle and data from a single platform without the need to use different standalone tools, mantaining tracebility across the whole process.
Manage data and part of the process, but you will need to use and mantain other tools if you want to keep track of all of your data across different parts of the Design-Build-Test-Discover process.
Adapt and integrate to existing systems, third-parties software and even DNA vendors. You don’t need to replace the tools that you are your team already love, with TeselaGen you can get the most of ouf them.
Has a well documented API, but they don’t have a robust integration framework to integrate with third-parties and external systems.
Can manage complex designs and workflows with thousands of constructs, enabling real high-throughput workflows.
Works well with low-throughput workflows, but when the need to scale rise up, it falls short.
Eliminate human error when running lab processes and generating valuable data integrating to laboratory equipment.
Prone to error given that most of the processes and data generation need to be done manually by a human.
Automatically generate build instructions optimized for cost and speed for popular cloning methods like Gibson assembly and Golden Gate.
Uses a manual wizard that helps creating the protocol for the desired cloning method. This is time consuming and not scalable with thousands of constructs.
Uses experimental data to assist in the selection of new desings to reduce the number of Design-Build-Test-Learn cycle iterations.
Need to manually decide which designs to try next without leveraging the whole potential of the experimental data generated.
Let’s talk so we can help you accelerate your innovation processes.