The main challenge in de novo assembly of NGS data is certainly to deal with
repeats that are longer than the reads. This is particularly true for RNA- seq
data, since coverage information cannot be used to flag repeated sequences, of
which transposable elements are one of the main examples. Most transcriptome
assemblers are based on de Bruijn graphs and have no clear and explicit model
for repeats in RNA-seq data, relying instead on heuristics to deal with them.
The results of this work are twofold. First, we introduce a formal model for
repre- senting high copy number repeats in RNA-seq data and exploit its
properties for inferring a combinatorial characteristic of repeat-associated
subgraphs. We show that the problem of identifying in a de Bruijn graph a
subgraph with this charac- teristic is NP-complete. In a second step, we show
that in the specific case of a local assembly of alternative splicing (AS)
events, we can implicitly avoid such subgraphs. In particular, we designed and
implemented an algorithm to efficiently identify AS events that are not
included in repeated regions. Finally, we validate our results using synthetic
data. We also give an indication of the usefulness of our method on real data
