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CPPred-sORF: Coding Potential Prediction of sORF based on non-AUG
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AbstractIn recent years, researchers have discovered thousands of sORFs that can encode micropeptides, and more and more discoveries that non-AUG codons can be used as translation initiation sites for these micropeptides. On the basis of our previous tool CPPred, we develop CPPred-sORF by adding two features and using non-AUG as the starting codon, which makes a comprehensive evaluation of sORF. The database of CPPred-sORF are constructed by small coding RNA and lncRNA as positive and negative data, respectively. Compared to the small coding RNAs and small ncRNAs, lncRNAs and small coding RNAs are less distinguishable. This is because the longer the sequences, the easier to include open reading frames. We find that the sensitivity, specificity and MCC value of CPPred-sORF on the independent testing set can reach 88.22%, 88.84% and 0.768, respectively, which shows much better prediction performance than the other methods.
Title: CPPred-sORF: Coding Potential Prediction of sORF based on non-AUG
Description:
AbstractIn recent years, researchers have discovered thousands of sORFs that can encode micropeptides, and more and more discoveries that non-AUG codons can be used as translation initiation sites for these micropeptides.
On the basis of our previous tool CPPred, we develop CPPred-sORF by adding two features and using non-AUG as the starting codon, which makes a comprehensive evaluation of sORF.
The database of CPPred-sORF are constructed by small coding RNA and lncRNA as positive and negative data, respectively.
Compared to the small coding RNAs and small ncRNAs, lncRNAs and small coding RNAs are less distinguishable.
This is because the longer the sequences, the easier to include open reading frames.
We find that the sensitivity, specificity and MCC value of CPPred-sORF on the independent testing set can reach 88.
22%, 88.
84% and 0.
768, respectively, which shows much better prediction performance than the other methods.
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