ISeq: A New Double-Barcode Method for Detecting Dynamic Genetic Interactions in Yeast. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Jaffe, Mia; Sherlock, Gavin; Levy, Sasha F
• Is Part Of: G3. Volume 7:Issue 1(2017)
• Page Start: 143
• Page End: 153
• Publication Date: 2017-01-01
• Subjects: Saccharomyces cerevisiae -- DNA barcoding -- genetic interactions -- systems biology -- whole genome sequencing; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1534/g3.116.034207
• Languages: English
• ISSNs: 2160-1836

Abstract

Abstract: Systematic screens for genetic interactions are a cornerstone of both network and systems biology. However, most screens have been limited to characterizing interaction networks in a single environment. Moving beyond this static view of the cell requires a major technological advance to increase the throughput and ease of replication in these assays. Here, we introduce iSeq—a platform to build large double barcode libraries and rapidly assay genetic interactions across environments. We use iSeq in yeast to measure fitness in three conditions of nearly 400 clonal strains, representing 45 possible single or double gene deletions, including multiple replicate strains per genotype. We show that iSeq fitness and interaction scores are highly reproducible for the same clonal strain across replicate cultures. However, consistent with previous work, we find that replicates with the same putative genotype have highly variable genetic interaction scores. By whole-genome sequencing 102 of our strains, we find that segregating variation and de novo mutations, including aneuploidy, occur frequently during strain construction, and can have large effects on genetic interaction scores. Additionally, we uncover several new environment-dependent genetic interactions, suggesting that barcode-based genetic interaction assays have the potential to significantly expand our knowledge of genetic interaction networks.