ArticleGenes2023
SCP4ssd: A Serverless Platform for Nucleotide Sequence Synthesis Difficulty Prediction Using an AutoML Model.
Article in Genes, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
9 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
DNA synthesis is widely used in synthetic biology to construct and assemble sequences ranging from short RBS to ultra-long synthetic genomes. Many sequence features, such as the GC content and repeat sequences, are known to affect the synthesis difficulty and subsequently the synthesis cost. In addition, there are latent sequence features, especially local characteristics of the sequence, which might affect the DNA synthesis process as well. Reliable prediction of the synthesis difficulty for a given sequence is important for reducing the cost, but this remains a challenge. In this study, we propose a new automated machine learning (AutoML) approach to predict the DNA synthesis difficulty, which achieves an F1 score of 0.930 and outperforms the current state-of-the-art model. We found local sequence features that were neglected in previous methods, which might also affect the difficulty of DNA synthesis. Moreover, experimental validation based on ten genes of
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