Rationale and Tools to look for the unknown in (metagenomic) sequence data
The interpretation of metagenomic data (environmental, microbiome, etc, ...) usually involves the recognition of sequence similarity with previously identified (micro-organisms). This is for instance the main approach to taxonomical assignments and a starting point to most diversity analyses. When exploring beyond the frontier of known biology, one should expect a large proportion of environmental sequences not exhibiting any significant similarity with known organisms. Notably, this is the case for eukaryotic viruses belonging to new families, for which the proportion of "no match" could reach 90%. Most metagenomics studies tend to ignore this large fraction of sequences that might be the equivalent of "black matter" in Biology. We will present some of the ideas and tools we are using to extract that information from large metagenomics data sets in search of truly unknown microorganisms.
One of the tools, "Seqtinizer", an interactive contig selection/inspection interface will also be presented in the context of "pseudo-metagenomic" projects, where the main organism under genomic study (such as sponges or corals) turns out to be (highly) mixed with an unexpected population of food, passing-by, or symbiotic microorganisms.
Licence: Creative Commons Attribution Non Commercial No Derivatives 4.0 International
Remote created date: 2016-12-16
Remote updated date: 2017-01-11