Orthology inference in nonmodel organisms using transcriptomes and low-coverage genomes: improving accuracy and matrix occupancy for phylogenomics

Abstract

Orthology inference is central to phylogenomic analyses. Phylogenomic data sets commonly include transcriptomes and low-coverage genomes that are incomplete and contain errors and isoforms. These properties can severely violate the underlying assumptions of orthology inference with existing heuristics. We present a procedure that uses phylogenies for both homology and orthology assignment. The procedure first uses similarity scores to infer putative homologs that are then aligned, constructed into phylogenies, and pruned of spurious branches caused by deep paralogs, misassembly, frameshifts, or recombination. These final homologs are then used to identify orthologs. We explore four alternative tree-based orthology inference approaches, of which two are new. These accommodate gene and genome duplications as well as gene tree discordance. We demonstrate these methods in three published data sets including the grape family, Hymenoptera, and millipedes with divergence times ranging from approximately 100 to over 400 Ma. The procedure significantly increased the completeness and accuracy of the inferred homologs and orthologs. We also found that data sets that are more recently diverged and/or include more high-coverage genomes had more complete sets of orthologs. To explicitly evaluate sources of conflicting phylogenetic signals, we applied serial jackknife analyses of gene regions keeping each locus intact. The methods described here can scale to over 100 taxa. They have been implemented in python with independent scripts for each step, making it easy to modify or incorporate them into existing pipelines. All scripts are available from https://bitbucket.org/yangya/phylogenomic_dataset_construction.

Keywords: Diplopoda; RNA-seq; Vitaceae; phylotranscriptomics.

Fig. 1.

Flow chart of homology and orthology inferences. .

Fig. 2.

Ortholog taxon occupation ranked from high to low. Orthologs with less than eight taxa (six for MIL–RT) were not shown.

Fig. 3.

Maximum-likelihood analysis of the HYM data set. Taxon names were abbreviated to the first four letters of the genus names except the left-most tree. Orthology inference methods: MI, maximum inclusion; RT, extracting rooted ingroup clades; MO, monophyletic outgroups; 1to1, filtered one-to-one orthologs. All nodes received bootstrap and 30% jackknife support values of 100 and are not shown. Node labels are also not shown if all support values are 100. Arrows indicate nodes with relatively low support.

Fig. 4.

Maximum-likelihood analysis of the GRP CDS data set. Taxon names were replaced by the collection numbers except the top left tree. Orthology inference methods: MI, maximum inclusion; RT, extracting rooted ingroup clades; MO, monophyletic outgroups; 1to1, filtered one-to-one orthologs. Node labels are not shown when all support values are 100. Arrows indicate nodes with relatively low support.

Fig. 5.

Maximum-likelihood analysis of the MIL. Taxon names were abbreviated to the first four letters of the genus names except the top left tree. Orthology inference methods: MI, maximum inclusion; RT, extracting rooted ingroup clades; MO, monophyletic outgroups; 1to1, filtered one-to-one orthologs. Node labels are not shown when all support values are 100. Arrows indicate nodes with relatively low support.