MESA
MESA simulates and assesses error probabilities in de novo DNA synthesis, polymerase chain reaction (PCR), sequencing, storage, and molecular cloning to predict and minimize errors in synthetic DNA fragments.
Key Features:
- Automated Assessment: Evaluates synthetic DNA fragments against method-dependent restrictions necessary for minimizing error probabilities in de novo DNA synthesis, PCR, molecular cloning, and sequencing.
- Error Simulation: Simulates potential errors across DNA synthesis, PCR, storage, and sequencing processes to assess their impact on experimental outcomes.
- Biological Restriction Accommodation: Incorporates biological restrictions specific to host organisms when evaluating and simulating fragments.
- Automatic Fragment Adjustment: Automatically adjusts synthetic DNA fragments according to integrated method and biological constraints.
Scientific Applications:
- Genetic Engineering: Assesses and reduces synthesis and cloning errors in construct design for genetic modification experiments.
- Synthetic Biology: Supports design and validation of synthetic DNA constructs by simulating synthesis and sequencing errors.
- Molecular Biology: Improves reliability of PCR, cloning, and sequencing workflows by predicting error-prone regions.
- DNA Synthesis Quality Control: Aids quality control of de novo DNA synthesis by evaluating method-dependent error probabilities.
Methodology:
Integrates constraints from DNA synthesis, amplification (PCR), cloning, sequencing methods, and biological host restrictions into assessment algorithms to perform automated fragment adjustment and error simulation.
Topics
Details
- Tool Type:
- command-line tool, web application
- Programming Languages:
- JavaScript, Python
- Added:
- 1/18/2021
- Last Updated:
- 3/18/2021
Operations
Publications
Schwarz M, Welzel M, Kabdullayeva T, Becker A, Freisleben B, Heider D. MESA: automated assessment of synthetic DNA fragments and simulation of DNA synthesis, storage, sequencing and PCR errors. Bioinformatics. 2020;36(11):3322-3326. doi:10.1093/bioinformatics/btaa140. PMID:32129840. PMCID:PMC7267826.