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We used high-throughput sequencing, combined with a two-dimensional pooling strategy, with either minimum read percentage with non-reference nucleotide or minimum variance multiplier as mutation prediction parameters, to detect genes related to abiotic and biotic stress resistances.
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We then used the optimal prediction parameters determined earlier to predict the masked data.
Proteins were predicted using general open reading frame (ORF) prediction parameters.
Prediction parameters.
The prediction parameters showed in Table 2.
CMC-based mutation prediction may aid design and execution of direct association genotyping studies.
The default prediction parameters were used for both programs.
Algorithm performance and VUS predictions were compared to established phylogenetic based mutation prediction algorithms.
On-line mutation prediction tools have been available for many years.
Here, we add transcriptional subtype as an additional prediction parameter.
Even in the absence of mutation predictions, AmyloidMutants offers the the highest structure prediction accuracy to date.
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