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Genome-wide analysis of mouse transcripts using exon microarrays and factor graphs.
Brendan J Frey1, Naveed Mohammad, Quaid D Morris
1Electrical and Computer Engineering, University of Toronto, 10 King's College Rd., Toronto, Ontario M5S 3G4, Canada. frey@psi.toronto.edu
Nature Genetics
|August 30, 2005
Summary
This study used microarray data and a Bayesian algorithm (GenRate) to analyze mouse gene transcription. Results confirm most known multiple-exon genes and identify potential new exons, improving gene annotation.
Area of Science:
- Genomics
- Molecular Biology
- Bioinformatics
Background:
- Mammalian microarray experiments suggest widespread transcription and the potential for numerous undiscovered multiple-exon protein-coding genes.
- Current cDNA databases may contain discrepancies and incomplete gene annotations.
Purpose of the Study:
- To investigate the existence of undiscovered multiple-exon protein-coding genes using extensive microarray data.
- To refine and improve the accuracy of mammalian gene annotation.
Main Methods:
- Utilized cDNA from unamplified, polyadenylation-selected RNA samples across 37 mouse tissues.
- Applied a Bayesian algorithm, GenRate, analyzing 1.14 million exon probes on microarrays.
- Employed a genome-wide scoring function within a factor graph for gene inference.
Main Results:
- GenRate detected 12,145 gene-length transcripts at a 2.7% exon false detection rate.
- Confirmed 81% of the 10,000 most highly expressed known genes.
- Identified 155,839 exons, with most linked to known genes, supporting the identification of most multiple-exon genes. Detected thousands of potential new exons and reconciled database discrepancies.
Conclusions:
- Microarray data analysis confirms the identification of most multiple-exon genes in mammals.
- The GenRate algorithm enhances gene annotation by identifying new exons and correcting existing databases.
- This study provides robust evidence for the completeness of current multiple-exon gene identification in mammals.