To compare gene structures for prediction of alternative coding transcripts in human, mouse and dog

Living organisms are capable of expressing several alternative transcripts (or RNAs) from a single gene. These transcripts are responsible for the regulatory mechanisms of the organism, some of them are translated into protein. Today, detecting the set of transcripts that can be expressed by a gene is an open problem to which many computational methods such as RNA sequencing, spliced sequence alignment methods or comparative genomics methods try to address.
This thesis proposes a comparative genomics method to compare the sequence of genes shared by several species. The result is a method for predicting transcripts on a multi-species scale, based on a graph structure. This method was applied to three species (human, mouse, and dog). It allowed to predict a relevant number of transcripts as well as to identify a set of genes that are conserved between the three species and that share both the same exonic structures and the same CDS.