title: Genomic Data Quality Impacts Automated Detection of Lateral Gene Transfer in Fungi.
abstract: Lateral gene transfer (LGT, also known as horizontal gene transfer), an atypical mechanism of transferring genes between species, has almost become the default explanation for genes that display an unexpected composition or phylogeny. Numerous methods of detecting LGT events all rely on two fundamental strategies: primary structure composition or gene tree/species tree comparisons. Discouragingly, the results of these different approaches rarely coincide. With the wealth of genome data now available, detection of laterally transferred genes is increasingly being attempted in large uncurated eukaryotic datasets. However, detection methods depend greatly on the quality of the underlying genomic data, which are typically complex for eukaryotes. Furthermore, given the automated nature of genomic data collection, it is typically impractical to manually verify all protein or gene models, orthology predictions, and multiple sequence alignments, requiring researchers to accept a substantial margin of error in their datasets. Using a test case comprising plant-associated genomes across the fungal kingdom, this study reveals that composition- and phylogeny-based methods have little statistical power to detect laterally transferred genes. In particular, phylogenetic methods reveal extreme levels of topological variation in fungal gene trees, the vast majority of which show departures from the canonical species tree. Therefore, it is inherently challenging to detect LGT events in typical eukaryotic genomes. This finding is in striking contrast to the large number of claims for laterally transferred genes in eukaryotic species that routinely appear in the literature, and questions how many of these proposed examples are statistically well supported.
authors.affiliation: Statistics and Bioinformatics Group, Institute of Fundamental Sciences, Massey University, Palmerston North 4442, New Zealand.|the Bio-Protection Research Centre, Massey University, Palmerston North 4442, New Zealand.
authors.forename: Pierre-Yves
authors.identifier: 0000-0002-7384-5181
authors.initials: PY
authors.lastname: Dupont
authors.affiliation: Statistics and Bioinformatics Group, Institute of Fundamental Sciences, Massey University, Palmerston North 4442, New Zealand m.p.cox@massey.ac.nz.|the Bio-Protection Research Centre, Massey University, Palmerston North 4442, New Zealand m.p.cox@massey.ac.nz.
authors.forename: Murray P
authors.identifier: 0000-0003-1936-0236
authors.initials: MP
authors.lastname: Cox
mesh_terms: D001331:Automation; D000068598:Data Accuracy; D055329:Epichloe / Q000235:genetics*; D022761:Gene Transfer, Horizontal; D005800:Genes, Fungal; D014644:Genetic Variation; D023281:Genomics / Q000592:standards*; D010802:Phylogeny; D013045:Species Specificity; D013223:Statistics as Topic
keywords: automated detection methods; comparative genomics; eukaryotes; fungi; horizontal gene transfer; lateral gene transfer