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Table 2 Recall of TAIR9 and TAIR10 annotations

From: SpliceGrapher: detecting patterns of alternative splicing from RNA-Seq data in the context of gene models and EST data

  Recall Novel Transcripts Recall
Method Exons Introns Exons Introns Number Percentage Exons Introns
   Splicegrapher 1.00 1.00 1,428 1,282 28 1.4% 0.039 0.045
   Splicegrapher + EST 1.00 1.00 11,299 3,557 38 1.9% 0.050 0.056
No gene models 0.25 0.19 33,252 3,425 0 0.0% 0.035 0.017
With gene models 0.94 0.89 12,690 5,222 4 0.2% 0.017 0.008
No gene models 0.21 0.29 86,346 5,335 0 0.0% 0.043 0.029
With gene models 0.69 0.79 115,130 3,734 11 1.1% 0.079 0.074
  1. Comparison of the ability of SpliceGrapher, Cufflinks, and TAU to predict TAIR9 and TAIR10 annotations in A. thaliana. The columns of the table provide recall levels of TAIR9 annotations, the number of novel exons and introns that are predicted, i.e., are not in the TAIR9 annotations, the number and percentage of TAIR10 transcripts that are predicted, and the recall level of TAIR10 annotations at the exon and intron level. When TAU and Cufflinks rely on RNA-Seq data alone they tend to produce graphs that are missing many of the features found in the gene models, as reflected in their recall scores between 0.19 and 0.29. When we provide them with TAIR9 annotations their recall scores improve, though TAU's improved statistics result from a vast number of novel exons, many of which may be spurious. When comparing these predictions with novel splice forms in the TAIR10 gene models, SpliceGrapher predicts more novel splice forms correctly than the other packages.