Fully automated annotation of mitochondrial genomes using a cluster-based approach with de Bruijn graphs.

A wide range of scientific fields, such as forensics, anthropology, medicine, and molecular evolution, benefits from the analysis of mitogenomic data. With the development of new sequencing technologies, the amount of mitochondrial sequence data to be analyzed has increased exponentially over the last few years. The accurate annotation of mitochondrial DNA is a prerequisite for any mitogenomic comparative analysis. To sustain with the growth of the available mitochondrial sequence data, highly efficient automatic computational methods are, hence, needed. Automatic annotation methods are typically based on databases that contain information about already annotated (and often pre-curated) mitogenomes of different species. However, the existing approaches have several shortcomings: 1) they do not scale well with the size of the database; 2) they do not allow for a fast (and easy) update of the database; and 3) they can only be applied to a relatively small taxonomic subset of all species. Here, we present a novel approach that does not have any of these aforementioned shortcomings, (1), (2), and (3). The reference database of mitogenomes is represented as a richly annotated de Bruijn graph. To generate gene predictions for a new user-supplied mitogenome, the method utilizes a clustering routine that uses the mapping information of the provided sequence to this graph. The method is implemented in a software package called DeGeCI