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Optimization of Multiplex Deep Sequencing Applications
UNCG University Groningen

For multiplex deep sequencing applications each sample must be easily discriminated on a basis of unique sample tags. Some strategies utilize Hamming binary codes. Because deep sequencing results are inherently noisy, used sample tags should be on a substantial minimal distance from each other. Otherwise correction of sequence errors and corresponding sequence results is neither possible nor meaningful. It will lead to cross contamination of the data. We have developed several primer design approaches which adapts to a variable extent of multiplexing, the complexity and length of the tag. Further, we employed a network strategy to assess quality and complexity of sequencing data. Limitations of the multiplexing strategy are further discussed.

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