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Originally published in Science Express on 11 November 2004
Science 24 December 2004: Vol. 306. no. 5705, pp. 2242 - 2246
DOI: 10.1126/science.1103388
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Reports
Global Identification of Human Transcribed Sequences with Genome Tiling Arrays
Paul Bertone,1*
Viktor Stolc,1,2*
Thomas E. Royce,3
Joel S. Rozowsky,3
Alexander E. Urban,1
Xiaowei Zhu,1
John L. Rinn,3
Waraporn Tongprasit,4
Manoj Samanta,2
Sherman Weissman,5
Mark Gerstein,3
Michael Snyder1,3
Elucidating the transcribed regions of the genome constitutes a fundamental aspect of human biology, yet this remains an outstanding problem. To comprehensively identify coding sequences, we constructed a series of high-density oligonucleotide tiling arrays representing sense and antisense strands of the entire nonrepetitive sequence of the human genome. Transcribed sequences were located across the genome via hybridization to complementary DNA samples, reverse-transcribed from polyadenylated RNA obtained from human liver tissue. In addition to identifying many known and predicted genes, we found 10,595 transcribed sequences not detected by other methods. A large fraction of these are located in intergenic regions distal from previously annotated genes and exhibit significant homology to other mammalian proteins.
1 Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 065208103, USA.
2 Center for Nanotechnology, NASA Ames Research Center, Moffett Field, CA 94035, USA.
3 Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 065208114, USA.
4 Eloret Corporation, Sunnyvale, CA 94087, USA
5 Department of Genetics, Yale University School of Medicine, New Haven, CT 065208005, USA.
* These authors contributed equally to this work.
To whom correspondence should be addressed. E-mail: michael.snyder{at}yale.edu (M.S.), mark.gerstein{at}yale.edu (M.G.)
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Genes & Dev.
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- Dicer-Dependent Turnover of Intergenic Transcripts from the Human {beta}-Globin Gene Cluster.
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- Detection of DNA copy number alterations using penalized least squares regression.
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Bioinformatics
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