Evidence map›Paper›PMID 25407802›Full record

ArticleBMC bioinformatics2014

Using phylogenetically-informed annotation (PIA) to search for light-interacting genes in transcriptomes from non-model organisms.

Daniel I Speiser, M Sabrina Pankey, Alexander K Zaharoff, Barbara A Battelle, Heather D Bracken-Grissom, Jesse W Breinholt, Seth M Bybee, Thomas W Cronin, Anders Garm, Annie R Lindgren and 8 more

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed
4.7field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed, 88 citations in OpenAlex.

  1. Adaptive molecular convergence is pervasive across deep time and largely decoupled from phenotypic convergence.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Eyeless cave-dwellingScience advances · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Molecular advances to study the function, evolution and spectral tuning of arthropod visual opsins.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022
    Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors at 15 institutions in 2 countries.

Daniel I SpeiserDepartment of Ecology, Evolution, and Marine Biology, University of California Santa Barbara, Santa Barbara, CA, USA. speiser@mailbox.sc.edu.
M Sabrina PankeyDepartment of Ecology, Evolution, and Marine Biology, University of California Santa Barbara, Santa Barbara, CA, USA. abrinas@gmail.com.
Alexander K ZaharoffDepartment of Ecology, Evolution, and Marine Biology, University of California Santa Barbara, Santa Barbara, CA, USA. sashazaharoff@gmail.com.
Barbara A BattelleThe Whitney Laboratory for Marine Bioscience, University of Florida, St. Augustine, FL, USA. Battelle@whitney.ufl.edu.
Heather D Bracken-GrissomDepartment of Biological Sciences, Florida International University-Biscayne Bay Campus, North Miami, FL, USA. hbracken@fiu.edu.
Jesse W BreinholtFlorida Museum of Natural History, University of Florida, Gainesville, FL, USA. jessebreinholt@gmail.com.
Seth M BybeeDepartment of Biology, Brigham Young University, Provo, UT, USA. seth.bybee@gmail.com.
Thomas W CroninDepartment of Biological Sciences, University of Maryland Baltimore County, Baltimore, MD, USA. cronin@umbc.edu.
Anders GarmDepartment of Biology, Marine Biological Section, University of Copenhagen, Copenhagen, Denmark. algarm@bio.ku.dk.
Annie R LindgrenDepartment of Biology, Portland State University, Portland, OR, USA. arl3@pdx.edu.
Nipam H PatelDepartment of Molecular and Cell Biology & Department of Integrative Biology, University of California, Berkeley, CA, USA. nipam@uclink.berkeley.edu.
Megan L PorterDepartment of Biology, University of South Dakota, Vermillion, SD, USA. Megan.Porter@usd.edu.
Meredith E ProtasDepartment of Natural Sciences and Mathematics, Dominican University of California, San Rafael, CA, USA. meredith.protas@dominican.edu.
Ajna S RiveraDepartment of Biology, University of the Pacific, Stockton, CA, USA. arivera@pacific.edu.
Jeanne M SerbDepartment of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA. serb@iastate.edu.
Kirk S ZiglerDepartment of Biology, Sewanee: The University of the South, Sewanee, TN, USA. kzigler@sewanee.edu.
Keith A CrandallComputational Biology Institute, George Washington University, Ashburn, VA, USA. kcrandall@gwu.edu.
Todd H OakleyDepartment of Ecology, Evolution, and Marine Biology, University of California Santa Barbara, Santa Barbara, CA, USA. todd.oakley@lifesci.ucsb.edu.
University of California, Santa Barbara · USBrigham Young University · USDominican University of California · USFlorida International University · USFlorida Museum of Natural History · USGeorge Washington University Virginia Campus · USIowa State University · USPortland State University · USSewanee: The University of the South · USUniversity of California, Berkeley · USUniversity of Copenhagen · DKUniversity of Florida · USUniversity of Maryland, Baltimore County · USUniversity of South Dakota · USUniversity of the Pacific · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTools 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.

resultsWe 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/ ).

conclusionsOur 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.

Indexed as

LightPhylogenyTranscriptomeAlgorithmsAnimalsEye ProteinsGenomeHigh-Throughput Nucleotide SequencingLikelihood FunctionsMolecular Sequence AnnotationSequence Analysis, ProteinVision, OcularEye Proteins

Identifiers

PMID25407802
PMCPMC4255452
OpenAlexW2161320636

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.