PHYLUCE is a software package for the analysis of conserved genomic loci

Abstract Summary: Targeted enrichment of conserved and ultraconserved genomic elements allows universal collection of phylogenomic data from hundreds of species at multiple time scales (&amp;lt;5 Ma to &amp;gt; 300 Ma). Prior to downstream inference, data from these types of targeted enrichment studies must undergo preprocessing to assemble contigs from sequence data; identify targeted, enriched loci from the off-target background data; align enriched contigs representing conserved loci to one another; and prepare and manipulate these alignments for subsequent phylogenomic inference. PHYLUCE is an efficient and easy-to-install software package that accomplishes these tasks across hundreds of taxa and thousands of enriched loci. Availability and Implementation: PHYLUCE is written for Python 2.7. PHYLUCE is supported on OSX and Linux (RedHat/CentOS) operating systems. PHYLUCE source code is distributed under a BSD-style license from https://www.github.com/faircloth-lab/phyluce/. PHYLUCE is also available as a package (https://binstar.org/faircloth-lab/phyluce) for the Anaconda Python distribution that installs all dependencies, and users can request a PHYLUCE instance on iPlant Atmosphere (tag: phyluce). The software manual and a tutorial are available from http://phyluce.readthedocs.org/en/latest/ and test data are available from doi: 10.6084/m9.figshare.1284521. Contact:  [email protected] Supplementary information:  Supplementary data are available at Bioinformatics online.</jats:p

Trinity Assemblies

De novo Trinity assemblies of raw-reads for all individuals included in our published study

ExaBayes output

Input and output files for the ExaBayes analyses of the 75p and 95p dataset

Phylogenetic Trees

Files containing all trees presented in the publicatio

Capturing Darwin’s dream

Evolutionary biologists from Darwin forward have dreamed of having data that would elucidate our understanding of evolutionary history and the diversity of life. Sequence capture is a relatively old DNA technology, but its use is growing rapidly due to advances in: 1) massively parallel DNA sequencing approaches and instruments, 2) massively parallel bait construction, 3) methods to identify target regions, and 4) sample preparation. We give a little historical context to these developments, summarize some of the important advances reported in this special issue, and point to further advances that can be made to help fulfill Darwin’s dream