Quantitative phenotyping via deep barcode sequencing
- Andrew M. Smith1,2,3,
- Lawrence E. Heisler3,4,
- Joseph Mellor5,6,
- Fiona Kaper7,
- Michael J. Thompson7,
- Mark Chee7,
- Frederick P. Roth5,6,
- Guri Giaever1,3,4,8 and
- Corey Nislow1,2,3,8
- 1 Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada;
- 2 Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5G 1L6, Canada;
- 3 Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada;
- 4 Department of Pharmaceutical Sciences, University of Toronto, Toronto, Ontario M5S 3M2, Canada;
- 5 Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, USA;
- 6 Center for Cancer Systems Biology, Dana Farber Cancer Institute, Boston, Massachusetts 02115, USA;
- 7 Prognosys Biosciences, Inc., La Jolla, California 92037, USA
Abstract
Next-generation DNA sequencing technologies have revolutionized diverse genomics applications, including de novo genome sequencing, SNP detection, chromatin immunoprecipitation, and transcriptome analysis. Here we apply deep sequencing to genome-scale fitness profiling to evaluate yeast strain collections in parallel. This method, Barcode analysis by Sequencing, or “Bar-seq,” outperforms the current benchmark barcode microarray assay in terms of both dynamic range and throughput. When applied to a complex chemogenomic assay, Bar-seq quantitatively identifies drug targets, with performance superior to the benchmark microarray assay. We also show that Bar-seq is well-suited for a multiplex format. We completely re-sequenced and re-annotated the yeast deletion collection using deep sequencing, found that ∼20% of the barcodes and common priming sequences varied from expectation, and used this revised list of barcode sequences to improve data quality. Together, this new assay and analysis routine provide a deep-sequencing-based toolkit for identifying gene–environment interactions on a genome-wide scale.
Footnotes
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↵8 Corresponding authors.
E-mail corey.nislow{at}gmail.com; fax (416) 978-4842.
E-mail ggiaever{at}gmail.com; fax (416) 978-4842.
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[Supplemental material is available online at http://www.genome.org. All data and analysis tools are available at http://chemogenomics.med.utoronto.ca/supplemental/barseq/.]
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Article published online before print. Article and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.093955.109.
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- Received March 19, 2009.
- Accepted July 9, 2009.
- Copyright © 2009 by Cold Spring Harbor Laboratory Press
