Metagenomic guilt by association: an operonic perspective.
Next-generation sequencing projects continue to drive a vast accumulation of metagenomic sequence data. Given the growth rate of this data, automated approaches to functional annotation are indispensable and a cornerstone heuristic of many computational protocols is the concept of guilt by association. The guilt by association paradigm has been heavily exploited by genomic context methods that offer functional predictions that are complementary to homology-based annotations, thereby offering a means to extend functional annotation. In particular, operon methods that exploit co-directional intergenic distances can provide homology-free functional annotation through the transfer of functions among co-operonic genes, under the assumption that guilt by association is indeed applicable. Although guilt by association is a well-accepted annotative device, its applicability to metagenomic functional annotation has not been definitively demonstrated. Here a large-scale assessment of metagenomic guilt by association is undertaken where functional associations are predicted on the basis of co-directional intergenic distances. Specifically, functional annotations are compared within pairs of adjacent co-directional genes, as well as operons of various lengths (i.e. number of member genes), in order to reveal new information about annotative cohesion versus operon length. The results suggests that co-directional gene pairs offer reduced confidence for metagenomic guilt by association due to difficulty in resolving the existence of functional associations when intergenic distance is the sole predictor of pairwise gene interactions. However, metagenomic operons, particularly those with substantial lengths, appear to be capable of providing a superior basis for metagenomic guilt by association due to increased annotative stability. The need for improved recognition of metagenomic operons is discussed, as well as the limitations of the present work.
Department of Biology, University of Waterloo, Waterloo, Ontario, Canada. gvey@uwaterloo.ca
Gregory
G
Vey
D000818:Animals; D044822:Biodiversity; D004843:Epistasis, Genetic / Q000502:physiology*; D005796:Genes / Q000502:physiology; D056726:Genetic Association Studies / Q000379:methods*; D006801:Humans; D056186:Metagenomics / Q000379:methods*; D058977:Molecular Sequence Annotation; D009876:Operon / Q000235:genetics*; D017422:Sequence Analysis, DNA