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F. Keith Barker

Publications and source records attributed to F. Keith Barker.

2 recordsLinked to original sources

A phylogenomic supertree of birds

It has long been appreciated that analyses of genomic data (e.g., whole genome sequencing or sequence capture) have the potential to reveal the tree of life, but it remains challenging to move from sequence data to a clear understanding of evolutionary history, in part due to the computational challenges of phylogenetic estimation using genome-scale data. Supertree methods solve that challenge because they facilitate a divide-and-conquer approach for large-scale phylogeny inference by integrating smaller subtrees in a computationally-efficient manner. Here, we combined information from sequence capture and whole-genome phylogenies using supertree methods. However, available phylogenomic trees had limited overlap so we used taxon-rich (but not phylogenomic) megaphylogenies to weave them together. This allowed us to construct a phylogenomic supertree, with support values, that included 707 bird species (~7% of avian species diversity). We estimated branch lengths using mitochondrial sequence data and we used this to estimate divergence times. Our time-calibrated supertree supports radiation of all three major avian clades (Palaeognathae, Galloanseres, and Neoaves) near the Cretaceous-Paleogene (K-Pg) boundary. The approach we used will permit the continued addition of taxa to this supertree as new phylogenomic data are published, and it could be applied to other taxa as well.

Diversity

Blood from a turnip: tissue origin of low-coverage shotgun sequencing libraries affects recovery of mitogenome sequences

Next generation sequencing methods allow rapid, economical accumulation of data that have many applications, even at relatively low levels of genome coverage. However, the utility of shotgun sequencing data sets for specific goals may vary depending on the biological nature of the samples sequenced. We show that the ability to assemble mitogenomes from three avian samples of two different tissue types varies widely. In particular, data with coverage typical of microsatellite development efforts (∼1×) from DNA extracted from avian blood failed to cover even 50% of the mitogenome, relative to at least 500-fold coverage from muscle-derived data. Researchers should consider possible applications of their data and select the tissue source for their work accordingly. Practitioners analyzing low-coverage shotgun sequencing data (including for microsatellite locus development) should consider the potential benefits of mitogenome assembly, including internal barcode verification of species identity, mitochondrial primer development, and phylogenetics.

Mitochondrial DNA