Mammalian Genome

, Volume 24, Issue 11–12, pp 484–499 | Cite as

Analysis of the canine brain transcriptome with an emphasis on the hypothalamus and cerebral cortex

  • Meenakshi Roy
  • Namshin Kim
  • Kyung Kim
  • Won-Hyong Chung
  • Rujira Achawanantakun
  • Yanni Sun
  • Robert Wayne


The diversity of dog breeds make the domestic dog a valuable model for identifying genes responsible for many phenotypic and behavioral traits. The brain, in particular, is a region of interest for the analysis of molecular changes that are involved in dog-specific behavioral phenotypes. However, such studies are handicapped due to incomplete annotation of the dog genome. We present a high-coverage transcriptome of the dog brain using RNA-Seq. Two areas of the brain, hypothalamus and cerebral cortex, were selected for their roles in cognition, emotion, and neuroendocrine functions. We detected many novel features of the dog transcriptome, including 13,799 novel exons, 51,357 exons with unique 5′ or 3′ modifications, and many novel alternative splicing events. We provide some examples of novel features in genes that are related to domestication, including ADCY8, SMOC2, and PRNP. We also found 247 novel protein-coding genes and 328 noncoding RNAs, including 57 long noncoding RNAs that represent the first empirical evidence for a large fraction of noncoding RNAs in the dog. In addition, we analyze both gene expression and alternative splicing differences between the hypothalamus and cerebral cortex and find that there is very little overlap between genes that are differentially alternatively spliced and genes that are differentially expressed. We thereby suggest that researchers who want to pinpoint the genetic causes for dog breed-specific traits and diseases should not confine their studies to gene expression alone, but should consider other factors such as alternative splicing and changes in untranslated regions.


Gene Ontology Noncoding RNAs Splice Junction Alternative Splice Event Ensembl Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was funded by the following grants to Dr. Robert Wayne’s laboratory: National Science Foundation (US) grant number 0910272 and National Science Foundation (US) grant number 1257716. Dr. Namshin Kim, Kyung Kim and Dr. Won-Hyong Chung were funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2011-0030770) and a grant from the Next-Generation BioGreen 21 Program (No. PJ008019 & PJ008068), Rural Development Administration, Republic of Korea. Dr. Yanni Sun and Rujira Achawanantakun were funded by NSF CAREER Grant DBI-0953738. We are deeply indebted to Dr. Christopher Lee (Chemistry Dept, UCLA) for his assistance and helpful discussion.

Supplementary material

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Supplementary material 1 (PDF 87 kb) GO classification of novel protein-coding genes by (A) biological process, (B) molecular function, and (C) cellular component
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Supplementary material 2 (PDF 50 kb) Pipeline for ncRNA analysis
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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Meenakshi Roy
    • 1
  • Namshin Kim
    • 2
  • Kyung Kim
    • 2
  • Won-Hyong Chung
    • 2
  • Rujira Achawanantakun
    • 3
  • Yanni Sun
    • 3
  • Robert Wayne
    • 1
  1. 1.Department of Ecology and Evolutionary BiologyUCLALos AngelesUSA
  2. 2.Korean Bioinformation CenterKorea Research Institute of Bioscience and BiotechnologyDaejeonKorea
  3. 3.Computer Science and Engineering DepartmentMichigan State UniversityEast LansingUSA

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