Quantitative phenotyping via deep barcode sequencing

  1. Andrew M Smith1,
  2. Lawrence E Heisler1,
  3. Joseph Mellor2,
  4. Fiona Kaper3,
  5. Michael J Thompson3,
  6. Mark Chee3,
  7. Frederick P Roth2,
  8. Guri Giaever1 and
  9. Corey Nislow1,4
  1. 1 University of Toronto;
  2. 2 Harvard University;
  3. 3 Prognosys Biosciences
  1. * Corresponding author; email: corey.nislow{at}gmail.com

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 ~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

    • Received March 19, 2009.
    • Accepted July 9, 2009.

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