dna-segmentation-benchmark 0.0.2

A collection of different evaluators for dna nucleodtide level labeling

DNA segmentation benchmark

This benchmark provides easy metrics for segmentation tasks beyond the common scores like f1, precision and recall. The main motivation for this benchmark is that computing the segmentation performance of a model through micro averaging over individual nucleotides can lead to very wrong conclusions about the actual quality of a model. Hence, this package provides a range of additional metrics.

Insertion / Deletion / Excision / Incision metric

Looking at the kind of error models make when segmenting can reveal systematic biases and issues. Further more this package allows to also look at the lengths of the different errors.

Error counts

Error lengths

Whole section correctness metric

Instead of measuring how many errors a model makes, these metrics look at if consecutive sections (e.g. Exons or Introns) were labeled correctly entirely

Correctly predicted sections

All sections of are a sequence are correct

This metric has to be used carefully. If using this on exons it only makes sense to use this if it certain that alternate splicing events are not occurring.

Frameshift metrics

Again, this metric can be incredibly insightful, but you have to be careful how you use it. Unless you are sure that all exons are part of the final transcript for all the benchmarked sequences DON'T USE IT. Your results will be skewed and hold no value.

Traditional Metrics