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Table 7 MiPepid’s prediction on the non-high-confidence data in SmProt

From: MiPepid: MicroPeptide identification tool using machine learning

Data source

#sORFs

avg sORF length (aa)

#Predicted positive

Proportion

high-throughput literature mining

25,663

44

20,516

0.80

ribosome profiling

13,715

36

8596

0.63

MS data

324

15

233

0.72

  1. high-throughput literature mining: published sORFs that were identified using high-throughput experimental methods;
  2. ribosome profiling: sORFs predicted from Ribo-Seq data;
  3. MS data: sORFs predicted from MS data;
  4. #sORFs: number of sORFs from a particular data source;
  5. avg sORF length (aa): the average length of sORFs measured in number of amino acids;
  6. #predicted positive: number of sORFs that are predicted as positive by MiPepid;
  7. proportion: \( \frac{\mathrm{avg}\ \mathrm{sORF}\ \mathrm{length}}{\#\mathrm{predicted}\ \mathrm{positive}} \)