Using phylogenetically-informed annotation (PIA) to search for light-interacting genes in transcriptomes from non-model organisms
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Author/Creator ORCID
Date
2014-11-19
Type of Work
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Citation of Original Publication
Daniel I Speiser, M Sabrina Pankey, Alexander K Zaharoff, Barbara A Battelle, et.al, Using phylogenetically-informed annotation (PIA) to search for light-interacting genes in transcriptomes from non-model organisms, BMC Bioinformatics, 2014,15:350, https://doi.org/10.1186/s12859-014-0350-x
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Attribution 4.0 International (CC BY 4.0)
Attribution 4.0 International (CC BY 4.0)
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.