SeqDeχ: A Sequence Deconvolution Tool for Genome Separation of Endosymbionts From Mixed Sequencing Samples

In recent years, the advent of NGS technology has made genome sequencing much cheaper than in the past; the high parallelization capability and the possibility to sequence more than one organism at once have opened the door to processing whole symbiotic consortia. However, this approach needs the development of specific bioinformatics tools able to analyze these data. In this work, we describe SeqDex, a tool that starts from a preliminary assembly obtained from sequencing a mixture of DNA from different organisms, to identify the contigs coming from one organism of interest. SeqDex is a fully automated machine learning-based tool exploiting partial taxonomic affiliations and compositional analysis to predict the taxonomic affiliations of contigs in an assembly. In literature, there are few methods able to deconvolve host-symbiont datasets, and most of them heavily rely on user curation and are therefore time consuming. The problem has strong similarities with metagenomic studies, where mixed samples are sequenced and the bioinformatics challenge is trying to separate contigs on the basis of their source organism; however, in symbiotic systems, additional information can be exploited to improve the output. To assess the ability of SeqDex to deconvolve host-symbiont datasets, we compared it to state-of-the-art methods for metagenomic binning and for host-symbiont deconvolution on three study cases. The results point out the good performances of the presented tool that, in addition to the ease of use and customization potential, make SeqDex a useful tool for rapid identification of endosymbiont sequences.