Accurate isoform discovery with IsoQuant using long reads
To mimic real-life situations and assess the ability of an algorithm to predict novel transcripts, we created reduced gene annotations by removing a fraction of expressed isoforms. First, we define a subset of true expressed transcripts that contributed to at least one read during the simulation. Among this set, we select a fraction of transcripts to be excluded from the annotation. These transcripts are denoted as the true novel isoforms.
To evaluate a transcript prediction tool, we provided the entire set of simulated reads and the reduced annotation as an input. Thus, true novel isoforms are hidden from the annotation, but present in the reads. We then compute precision and recall by running gffcomparefor the entire output annotation versus the complete set of expressed transcripts, reported known isoforms versus the set of true known isoforms and predicted novel transcript models versus the true novel set.
For the annotation-free benchmarks we simply compared the entire output annotation with the true set of expressed isoforms using gffcompare. To estimate how recall and precision of novel transcripts depend on the expression levels, predicted transcripts are grouped into bins by their transcripts per million values. For computing recall the number of false negative calls in each TPM bin is required. We thus group transcripts by their TPM values used during the simulation. However, computing precision requires the number of false-positive predictions within each bin and thus only reported TPM values can be used .
To evaluate SIRV transcripts we used an incomplete SIRV annotation containing only 43 out of 69 SIRV transcripts. The output annotations were again split into known and novel transcripts, and compared against the respective reference set using gffcompare. The SIRV-Set 4 annotations are available atConsistency between transcripts generated on real data was estimated using gffcompare .
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