Authors

Daniel I. Speiser, Department of Ecology, Evolution, and Marine Biology, University of California Santa BarbaraFollow
M. Sabrina Pankey, Department of Ecology, Evolution, and Marine Biology, University of California Santa Barbara
Alexander K. Zaharoff, Department of Ecology, Evolution, and Marine Biology, University of California Santa Barbara
Barbara A. Battelle, The Whitney Laboratory for Marine Bioscience, University of FloridaFollow
Heather D. Bracken-Grissom, Department of Biological Sciences, Florida International University-Biscayne Bay Campus
Jesse W. Breinholt, Department of Biological Sciences Florida International University-Biscayne Bay Campus
Seth M. Bybee, Department of Biology, Brigham Young University
Thomas W. Cronin, Department of Biological Sciences, University of Maryland Baltimore County
Anders Garm, Department of Biology, Marine Biological Section, University of Copenhagen
Annie R. Lindgren, Department of Biology, Portland State University
Nipam H. Patel, Department of Molecular and Cell Biology & Department of Integrative Biology, University of California, BerkeleyFollow
Megan L. Porter, Department of Biology, University of South Dakota
Meredith E. Protas, Department of Natural Sciences and Mathematics, Dominican University of CaliforniaFollow
Anja S. Rivera, Department of Biology, University of the Pacific
Jeanne M. Serb, Department of Ecology, Evolution, and Organismal Biology, Iowa State University
Kirk S. Zigler, Department of Biology, Sewanee: The University of the South
Keith A. Crandall, Computational Biology Institute, George Washington University
Todd H. Oakley, Department of Ecology, Evolution, and Marine Biology, University of California Santa Barbara

Document Type

Article

Journal or Conference Title

BMC Bioinformatics

ISSN

1471-2105

Volume

15

Issue

350

First Page

1

Last Page

12

Publication Date

2014

Department

Natural Sciences and Mathematics

Abstract

Background: Tools for high throughput sequencing and de novo assembly make the analysis of transcriptomes (i.e. the suite of genes expressed in a tissue) feasible for almost any organism. Yet a challenge for biologists is that it can be difficult to assign identities to gene sequences, especially from non-model organisms. Phylogenetic analyses are one useful method for assigning identities to these sequences, but such methods tend to be time-consuming because of the need to re-calculate trees for every gene of interest and each time a new data set is analyzed. In response, we employed existing tools for phylogenetic analysis to produce a computationally efficient, tree-based approach for annotating transcriptomes or new genomes that we term Phylogenetically-Informed Annotation (PIA), which places uncharacterized genes into pre-calculated phylogenies of gene families.

Results: We generated maximum likelihood trees for 109 genes from a Light Interaction Toolkit (LIT), a collection of genes that underlie the function or development of light-interacting structures in metazoans. To do so, we searched protein sequences predicted from 29 fully-sequenced genomes and built trees using tools for phylogenetic analysis in the Osiris package of Galaxy (an open-source workflow management system). Next, to rapidly annotate transcriptomes from organisms that lack sequenced genomes, we repurposed a maximum likelihood-based Evolutionary Placement Algorithm (implemented in RAxML) to place sequences of potential LIT genes on to our pre-calculated gene trees. Finally, we implemented PIA in Galaxy and used it to search for LIT genes in 28 newly-sequenced transcriptomes from the light-interacting tissues of a range of cephalopod mollusks, arthropods, and cubozoan cnidarians. Our new trees for LIT genes are available on the Bitbucket public repository (http://bitbucket.org/osiris_phylogenetics/pia/) and we demonstrate PIA on a publicly-accessible web server (http://galaxy-dev.cnsi.ucsb.edu/pia/).

Conclusions: Our new trees for LIT genes will be a valuable resource for researchers studying the evolution of eyes or other light-interacting structures. We also introduce PIA, a high throughput method for using phylogenetic relationships to identify LIT genes in transcriptomes from non-model organisms. With simple modifications, our methods may be used to search for different sets of genes or to annotate data sets from taxa outside of Metazoa.

Publisher Statement

Originally published as Speiser et al.: Using phylogenetically-informed annotation (PIA) to search for light-interacting genes in transcriptomes from non-model organisms. BMC Bioinformatics 2014 15:350.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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