Novel sequencing technologies are rapidly expanding the size of datasets that can be applied to phylogenetic studies. Currently the most commonly used phylogenomic approaches involve some form of genome reduction. While these approaches make assembling phylogenomic datasets more economical for organisms with large genomes, they reduce the genomic coverage and thereby the long-term utility of the data. Currently, for organisms with moderate to small genomes (<1000 Mbp) it is feasible to sequence the entire genome at modest coverage (10-30X). Computational challenges for handling these large datasets can be alleviated by assembling targeted reads, rather than assembling the entire genome, to produce a phylogenomic data matrix.
Here we demonstrate the use of automated Target Restricted Assembly Method (aTRAM) to assemble 1,107 single copy ortholog genes from whole genome sequencing of sucking lice (Anoplura) and outgroups. We developed a pipeline to extract exon sequences from the aTRAM assemblies by annotating them with respect to the original target protein. We aligned these protein sequences with the inferred amino acids and then performed phylogenetic analyses on both the concatenated matrix of genes and on each gene separately in a coalescent analysis. Finally, we tested the limits of successful assembly in aTRAM by assembling 100 genes from close to distantly related taxa at high to low levels of coverage.
Both the concatenated analysis and the coalescent-based analysis produced the same tree topology, which was consistent with previously published results and resolved weakly supported nodes. These results demonstrate that this approach is successful at developing phylogenomic datasets from raw genome sequencing reads. Further, we found that with coverages above 5 – 10X, aTRAM was successful at assembling 80 – 90% of the contigs for both close and distantly related taxa. As sequencing costs continue to decline, we expect full genome sequencing will become more feasible for a wider array of organisms, and aTRAM will enable mining of these genomic datasets for an extensive variety of applications, including phylogenomics.
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