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iSeq: A New Double-Barcode Method for Detecting Dynamic Genetic Interactions in Yeast
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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Genetics Society of America
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217104/ https://www.ncbi.nlm.nih.gov/pubmed/27821633 http://dx.doi.org/10.1534/g3.116.034207 |
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author | Jaffe, Mia Sherlock, Gavin Levy, Sasha F. |
author_facet | Jaffe, Mia Sherlock, Gavin Levy, Sasha F. |
author_sort | Jaffe, Mia |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5217104 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Genetics Society of America |
record_format | MEDLINE/PubMed |
spelling | pubmed-52171042017-01-09 iSeq: A New Double-Barcode Method for Detecting Dynamic Genetic Interactions in Yeast Jaffe, Mia Sherlock, Gavin Levy, Sasha F. G3 (Bethesda) Investigations 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. Genetics Society of America 2016-11-07 /pmc/articles/PMC5217104/ /pubmed/27821633 http://dx.doi.org/10.1534/g3.116.034207 Text en Copyright © 2017 Jaffe et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Investigations Jaffe, Mia Sherlock, Gavin Levy, Sasha F. iSeq: A New Double-Barcode Method for Detecting Dynamic Genetic Interactions in Yeast |
title | iSeq: A New Double-Barcode Method for Detecting Dynamic Genetic Interactions in Yeast |
title_full | iSeq: A New Double-Barcode Method for Detecting Dynamic Genetic Interactions in Yeast |
title_fullStr | iSeq: A New Double-Barcode Method for Detecting Dynamic Genetic Interactions in Yeast |
title_full_unstemmed | iSeq: A New Double-Barcode Method for Detecting Dynamic Genetic Interactions in Yeast |
title_short | iSeq: A New Double-Barcode Method for Detecting Dynamic Genetic Interactions in Yeast |
title_sort | iseq: a new double-barcode method for detecting dynamic genetic interactions in yeast |
topic | Investigations |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5217104/ https://www.ncbi.nlm.nih.gov/pubmed/27821633 http://dx.doi.org/10.1534/g3.116.034207 |
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