Quantitative phenotyping via deep barcode sequencing.

By Andrew M. Smith, Lawrence E Heisler, J. Mellor, F. Kaper, Michael J. Thompson, M. Chee, Frederick P. Roth, G. Giaever, Corey Nislow. Genome Research, 2009

317 citations recorded by the source corpus.

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 approximately 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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b2eb21210d036fc76c5f7197208e4b7329f39b0d

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In Molecular Biology

Citations in the corpus

Differential expression analysis for sequence count data (2010, methodology): “We will use the term gene synonymously to class, even though a class may also refer to, for example, a transcription factor binding site, or even a barcode [5].”