Mikado is a lightweight Python3 pipeline whose purpose is to facilitate the identification of expressed loci from RNA-Seq data * and to select the best models in each locus.
Currently, due to the architecture of Mikado, it can happen that loci get lost in scenarios like the following:
Real loci to find: transcript A, transcript B
Transcript C spans both loci and shares an intron with A but not with B. C is not a valid alternative splicing event for B.
Sublocus stage: C wins over A, B wins as singleton
Monosublocus stage: B wins over C
Locus stage: We only have B as transcript, as C is discarded.
A is lost because no transcript has been retained for that locus.
This tends not to happen too much due to the specifics of scoring, but it is an issue.
Ideally, Mikado should realise it has lost transcripts, backtrack, and rebuild loci for them.
Currently, due to the architecture of Mikado, it can happen that loci get lost in scenarios like the following:
This tends not to happen too much due to the specifics of scoring, but it is an issue. Ideally, Mikado should realise it has lost transcripts, backtrack, and rebuild loci for them.