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Genome-wide analysis of mouse transcripts using exon microarrays and factor graphs

Nature Genetics volume 37, pages 991–996 (2005)Cite this article

Abstract

Recent mammalian microarray experiments detected widespread transcription and indicated that there may be many undiscovered multiple-exon protein-coding genes. To explore this possibility, we labeled cDNA from unamplified, polyadenylation-selected RNA samples from 37 mouse tissues to microarrays encompassing 1.14 million exon probes. We analyzed these data using GenRate, a Bayesian algorithm that uses a genome-wide scoring function in a factor graph to infer genes. At a stringent exon false detection rate of 2.7%, GenRate detected 12,145 gene-length transcripts and confirmed 81% of the 10,000 most highly expressed known genes. Notably, our analysis showed that most of the 155,839 exons detected by GenRate were associated with known genes, providing microarray-based evidence that most multiple-exon genes have already been identified. GenRate also detected tens of thousands of potential new exons and reconciled discrepancies in current cDNA databases by 'stitching' new transcribed regions into previously annotated genes.

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Figure 1: Example of results and illustration of analysis method.
Figure 2: GenRate detects exons with high sensitivity and high specificity.
Figure 3: Performance of GenRate on detecting genes.
Figure 4: New exons detected by GenRate and associated with RefSeq Golden Path genes are categorized by 3′ or 5′ extensions of known genes, bridges that join together known genes, new exons that map to an EST or cDNA in the FANTOM2 or Unigene database, new exons that can be stitched together with the known gene by a previously detected EST or cDNA, and new exons that do not map to any previously detected sequences.

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Gene Expression Omnibus

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Acknowledgements

We thank G.E. Hinton for conversations and C. Boone and B. Andrews for their support. This work was supported by grants from the Canadian Institutes of Health Research, the Natural Sciences and Engineering Research Council of Canada and the Canadian Foundation for Innovation (to T.R.H., B.J.F. and B.J.B.), by a PREA award (to B.J.F.) and by a Natural Sciences and Engineering Research Council of Canada postdoctoral fellowship (to Q.D.M.).

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Author notes

  1. Brendan J Frey, Naveed Mohammad, Quaid D Morris and Wen Zhang: These authors contributed equally to this work.

Authors and Affiliations

  1. Electrical and Computer Engineering, University of Toronto, 10 King's College Rd., Toronto, M5S 3G4, Ontario, Canada

    Brendan J Frey, Quaid D Morris & Mark D Robinson

  2. Banting and Best Department of Medical Research, University of Toronto, 112 College St., Toronto, M5G 1L6, Ontario, Canada

    Brendan J Frey, Naveed Mohammad, Quaid D Morris, Wen Zhang, Mark D Robinson, Sanie Mnaimneh, Richard Chang, Qun Pan, Benjamin J Blencowe & Timothy R Hughes

  3. Medical Genetics and Microbiology, University of Toronto, 1 King's College Ct., Toronto, M5S 3G4, Ontario, Canada

    Wen Zhang, Janet Rossant, Benoit G Bruneau, Jane E Aubin, Benjamin J Blencowe & Timothy R Hughes

  4. Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Avenue, Toronto, M5G 1X5, Ontario, Canada

    Eric Sat & Janet Rossant

  5. The Hospital for Sick Children, 555 University Ave., Toronto, M5G 1X8, Ontario, Canada

    Benoit G Bruneau

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  1. Brendan J Frey
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Corresponding authors

Correspondence to Benjamin J Blencowe or Timothy R Hughes.

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Supplementary information

Supplementary Fig. 1

GenRate output for putative novel gene. (PDF 54 kb)

Supplementary Table 1

PCR confirmation of predicted false detections. (PDF 80 kb)

Supplementary Table 2

PCR confirmation of a novel gene. (PDF 11 kb)

Supplementary Methods (PDF 30 kb)

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Frey, B., Mohammad, N., Morris, Q. et al. Genome-wide analysis of mouse transcripts using exon microarrays and factor graphs. Nat Genet 37, 991–996 (2005). https://doi.org/10.1038/ng1630

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