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Abstract Detail


Washburn, Jacob [1], Pires, Joseph Chris [2].

Genome-Guided Phylo-Transcriptomics.

The past few years have witnessed a paradigm shift in molecular systematics from phylogenetic methods (using one or only a few genes) to those that can increasingly be thought of as phylogenomics (phylogenetic inference with whole genomes). One approach that has recently emerged is phylo-transcriptomics (transcriptome-based phylogenetic inference). When compared to other sequencing approaches, phylo-transcriptomics has the benefit of providing more genetic loci for inference than sequence capture approaches while being more cost effective than whole genome sequencing. To date, many orthology inference approaches have used phylogenetically-based orthology detection methods without taking advantage of conserved genome synteny. In this study, we combine de novo transcriptome data and sequenced genomes from an economically important group of grass species, the tribe Paniceae. We inferred syntenic orthologs from sequenced genomes as anchor points for the transcriptomes. This method, which we call genome-guided phylo-transcriptomics, is then compared to recently published orthology determination methods. These comparisons reveal some of the strengths and weaknesses of these approaches and provide a framework for future researchers to evaluate the costs and benefits of adding sequenced genomes to transcriptome data sets. In addition, this study provides the most comprehensive and robust nuclear phylogeny of the tribe Paniceae (Poaceae) to date and enables a detailed dissection of systematic gene tree/species tree incongruence within the tribe.

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1 - University Of Missouri, Biological Sciences, 311 Bond Life Sciences Center, Columbia, MO, 65211, USA
2 - University Of Missouri, 371 Bond Life Sciences Center, 1201 Rollins Street, Columbia, MO, 65211-7310, USA


Presentation Type: Oral Paper
Session: 38, Phylogenomics III
Location: 204/Savannah International Trade and Convention Center
Date: Wednesday, August 3rd, 2016
Time: 11:00 AM
Number: 38012
Abstract ID:861
Candidate for Awards:Margaret Menzel Award

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