Skip to search boxSkip to navigationSkip to main content

De novo transcriptome sequencing in a songbird, the dark-eyed junco (Junco hyemalis): genomic tools for an ecological model system

  • Mark P. Peterson(corresponding author)
    ,
  • Danielle J. Whittaker
    ,
  • Shruthi Ambreth
    ,
  • Suhas Sureshchandra
    ,
  • Aaron Buechlein
    ,
  • Ram Podicheti
*Corresponding author for this work
Scholary Output:
Contribution to journal
Article
Peer-review

Open access

Abstract

Background: Though genomic-level data are becoming widely available, many of the metazoan species sequenced are laboratory systems whose natural history is not well documented. In contrast, the wide array of species with very well-characterized natural history have, until recently, lacked genomics tools. It is now possible to address significant evolutionary genomics questions by applying high-throughput sequencing to discover the majority of genes for ecologically tractable species, and by subsequently developing microarray platforms from which to investigate gene regulatory networks that function in natural systems. We used GS-FLX Titanium Sequencing (Roche/454-Sequencing) of two normalized libraries of pooled RNA samples to characterize a transcriptome of the dark-eyed junco (Junco hyemalis), a North American sparrow that is a classically studied species in the fields of photoperiodism, speciation, and hormone-mediated behavior.Results: From a broad pool of RNA sampled from tissues throughout the body of a male and a female junco, we sequenced a total of 434 million nucleotides from 1.17 million reads that were assembled de novo into 31,379 putative transcripts representing 22,765 gene sets covering 35.8 million nucleotides with 12-fold average depth of coverage. Annotation of roughly half of the putative genes was accomplished using sequence similarity, and expression was confirmed for the majority with a preliminary microarray analysis. Of 716 core bilaterian genes, 646 (90 %) were recovered within our characterized gene set. Gene Ontology, orthoDB orthology groups, and KEGG Pathway annotation provide further functional information about the sequences, and 25,781 potential SNPs were identified.Conclusions: The extensive sequence information returned by this effort adds to the growing store of genomic data on diverse species. The extent of coverage and annotation achieved and confirmation of expression, show that transcriptome sequencing provides useful information for ecological model systems that have historically lacked genomic tools. The junco-specific microarray developed here is allowing investigations of gene expression responses to environmental and hormonal manipulations - extending the historic work on natural history and hormone-mediated phenotypes in this system.

Publication Information

Output type

Scholary Output:
Contribution to journal
Article
Peer-review

Original language

English (US)

Article number

305

Journal (Volume, Issue Number)

BMC Genomics (Volume 13, Issue 1)

Publication milestones

  • Published - 07/09/2012

Publication status

Published - 07/09/2012

ISSN

1471-2164

Publication IDs

  • Scopus: 84863510246
  • PubMed: 22776250

Publication metrics

Metrics

Fractional count
1
Fractional count
0.08
Fractional count
11
Fractional count
0.92
Fractional count
1
Fractional count
1
SciVal
FWCI
1.65
SciVal
Author count
12
SciVal
citations
28
SciVal
Paper percentile
84
Scopus
citations

PlumX, opens in new tab

Citation count
28
Captures
75

Funding Details

The authors would like to thank Kimberly Rosvall and Sarah Wanamaker for help with animal handling for the microarray analysis, and gratefully acknowledge the expert technical contributions of Jacqueline Lopez, RNA preparation and microarray experiments, and James Ford and Zach Smith, transcriptome library preparation and sequencing. We also thank Baiju Parikh, at Roche NimbleGen for contributions to the CGB Ecological Genomics Pipeline. This material is based upon work supported by the National Science Foundation under Grant No. ACI-0338618 l, No. CNS-0521433, OCI-0451237, OCI-0535258, and OCI-0504075. This research was supported in part by the Indiana METACyt Initiative. The Indiana METACyt Initiative of Indiana University is supported in part by Lilly Endowment, Inc. This work was supported in part by Shared University Research grants from IBM, Inc. to Indiana University. This work was further funded by Indiana University, and the National Science Foundation (IOS-0820055).
FundersFunding numbers
IBM, Inc.
-
METACyt Initiative of Indiana University
-
METACyt Initiative of Indiana University
-
NSF
OCI-0504075, OCI-0535258, 0521433, OCI-0451237, CNS-0521433, 0820055, ACI-0338618 l, 0504075, 0338618, 0451237
IU
IOS-0820055
Lilly Endowment
-