Abstract

Rapid amplification of cDNA ends (RACE) is a widely used approach for transcript identification. Random clone selection from the RACE mixture, however, is an ineffective sampling strategy if the dynamic range of transcript abundances is large. To improve sampling efficiency of human transcripts, we hybridized the products of the RACE reaction onto tiling arrays and used the detected exons to delineate a series of reverse-transcriptase (RT)-PCRs, through which the original RACE transcript population was segregated into simpler transcript populations. We independently cloned the products and sequenced randomly selected clones. This approach, RACEarray, is superior to direct cloning and sequencing of RACE products because it specifically targets new transcripts and often results in overall normalization of transcript abundance. We show theoretically and experimentally that this strategy leads indeed to efficient sampling of new transcripts, and we investigated multiplexing the strategy by pooling RACE reactions from multiple interrogated loci before hybridization.

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Acknowledgements

The project at Institut Municipal d'Investigació Mèdica, Center for Genomic Regulation (CRG), the Universities of Lausanne and Geneva, and Affymetrix was supported by grants U01HG003150 and U01HG003147 from the US National Human Genome Research Institute, National Institutes of Health; at IMIM and CRG also funded by grant BIO2006-03380 from the Spanish Ministry of Education and Science and from the European BioSapiens Consortium; at the Universities of Lausanne and Geneva also funded by the Swiss National Science Foundation, the EU AnEUploidy project and the National Center of Competence in Research Frontiers in Genetics; and at Affymetrix also funded by the National Cancer Institute, National Institutes of Health (N01-CO-12400) and by Affymetrix, Inc. The portion of this work carried out at Center for Cancer Systems Biology was funded by a grant from the Ellison Foundation (to M.V.) and as Institute Sponsored Research from the Dana Farber Cancer Institute Strategic Initiative. We acknowledge J.M. Oller for reviewing the probabilistic results and R. Castelo, C. Howald and D. Martin for useful suggestions.

Author information

Author notes

    • Sarah Djebali
    • , Philipp Kapranov
    • , Sylvain Foissac
    • , Julien Lagarde
    •  & Alexandre Reymond

    These authors contributed equally to this work.

Affiliations

  1. Grup de Recerca en Informàtica Biomèdica, Institut Municipal d'Investigació Mèdica/Universitat Pompeu Fabra, Dr. Aiguader 88, 08003 Barcelona, Spain.

    • Sarah Djebali
    • , Julien Lagarde
    • , France Denoeud
    •  & Roderic Guigó
  2. Affymetrix, Inc., 3420 Central Expressway, Santa Clara, California 95051, USA.

    • Philipp Kapranov
    • , Jorg Drenkow
    • , Erica Dumais
    •  & Thomas R Gingeras
  3. Center for Genomic Regulation, Dr. Aiguader 88, 08003 Barcelona, Spain.

    • Sylvain Foissac
    •  & Roderic Guigó
  4. Center for Integrative Genomics, University of Lausanne, Genopole Building, 1015 Lausanne, Switzerland.

    • Alexandre Reymond
  5. Department of Genetic Medicine and Development, University of Geneva Medical School, 1 rue Michel Servet, 1211 Geneva, Switzerland.

    • Catherine Ucla
    • , Carine Wyss
    • , Periklis Makrythanasis
    •  & Stylianos E Antonarakis
  6. Center for Cancer Systems Biology and Department of Cancer Biology, Dana-Farber Cancer Institute and Department of Genetics, Harvard Medical School, 44 Binney Street, Boston, Massachusetts 02115, USA.

    • Ryan R Murray
    • , Chenwei Lin
    • , David Szeto
    • , Marc Vidal
    •  & Kourosh Salehi-Ashtiani
  7. Departament d'Estadística, Universitat de Barcelona, Diagonal 645, 08028 Barcelona, Spain.

    • Miquel Calvo
  8. Human and Vertebrate Analysis and Annotation Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1HH, UK.

    • Adam Frankish
    •  & Jennifer Harrow

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Contributions

T.R.G., S.E.A., A.R., P.K. and R.G. participated in the overall design of the experiments and the subsequent analysis. A.R., C.U., C.W., P.M. and S.E.A. performed the RACE reactions. J.D., E.D. and P.K. performed the hybridization of the RACE reactions into tiling arrays. R.R.M., C.L., D.S., K.S.-A. and M.V. carried out the RT-PCRs, the cloning and sequencing of candidates. S.D., S.F., J.L., F.D. and R.G. developed software and carried out the bioinformatics analysis. M.C. developed the theoretical model for sampling and carried out the computational simulations. A.F. and J.H. provided the reference gene annotation and helped map the RT-PCR sequences to the genome.

Competing interests

P.K., J.D., E.D. and T.R.G. are Affymetrix employees.

Corresponding author

Correspondence to Roderic Guigó.

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    Supplementary Figures 1–8, Supplementary Tables 1–2, Supplementary Methods, Supplementary Results

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DOI

https://doi.org/10.1038/nmeth.1216

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