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Yeast Genetics

Overview of attention for book
Cover of 'Yeast Genetics'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Yeast Transformation by the LiAc/SS Carrier DNA/PEG Method.
  3. Altmetric Badge
    Chapter 2 Tetrad, random spore, and molecular analysis of meiotic segregation and recombination.
  4. Altmetric Badge
    Chapter 3 PCR Mutagenesis and Gap Repair in Yeast
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    Chapter 4 PCR-Mediated Epitope Tagging of Genes in Yeast.
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    Chapter 5 Manipulating the yeast genome: deletion, mutation, and tagging by PCR.
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    Chapter 6 Preparation of yeast cells for live-cell imaging and indirect immunofluorescence.
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    Chapter 7 Single yeast cell imaging.
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    Chapter 8 Microfluidic platforms for generating dynamic environmental perturbations to study the responses of single yeast cells.
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    Chapter 9 Using Two-Hybrid Interactions to Identify Separation-of-Function Mutations.
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    Chapter 10 Synthetic Genetic Array Analysis for Global Mapping of Genetic Networks in Yeast
  12. Altmetric Badge
    Chapter 11 Chemical genetic and chemogenomic analysis in yeast.
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    Chapter 12 Phenomic assessment of genetic buffering by kinetic analysis of cell arrays.
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    Chapter 13 Detection of Short-Range Chromatin Interactions by Chromosome Conformation Capture (3C) in Yeast.
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    Chapter 14 Chromosome Conformation Capture (3C) of Tandem Arrays in Yeast.
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    Chapter 15 Global Analysis of Transcription Factor-Binding Sites in Yeast Using ChIP-Seq.
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    Chapter 16 High-density tiling microarray analysis of the full transcriptional activity of yeast.
  18. Altmetric Badge
    Chapter 17 Analysis of Silencing in Saccharomyces cerevisiae.
  19. Altmetric Badge
    Chapter 18 A User's Guide to the Ribosomal DNA in Saccharomyces cerevisiae.
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    Chapter 19 Two-dimensional agarose gel electrophoresis for analysis of DNA replication.
  21. Altmetric Badge
    Chapter 20 Replicative life span analysis in budding yeast.
  22. Altmetric Badge
    Chapter 21 Metabolomic and lipidomic analyses of chronologically aging yeast.
Attention for Chapter 10: Synthetic Genetic Array Analysis for Global Mapping of Genetic Networks in Yeast
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Chapter title
Synthetic Genetic Array Analysis for Global Mapping of Genetic Networks in Yeast
Chapter number 10
Book title
Yeast Genetics
Published in
Methods in molecular biology, January 2014
DOI 10.1007/978-1-4939-1363-3_10
Pubmed ID
Book ISBNs
978-1-4939-1362-6, 978-1-4939-1363-3
Authors

Elena Kuzmin, Sara Sharifpoor, Anastasia Baryshnikova, Michael Costanzo, Chad L Myers, Brenda J Andrews, Charles Boone, Chad L. Myers, Brenda J. Andrews, Kuzmin, Elena, Sharifpoor, Sara, Baryshnikova, Anastasia, Costanzo, Michael, Myers, Chad L., Andrews, Brenda J., Boone, Charles

Abstract

Genetic interactions occur when mutant alleles of two or more genes collaborate to generate an unusual composite phenotype, one that would not be predicted based on the expected combined effects of the individual mutant alleles. Synthetic Genetic Array (SGA) methodology was developed to automate yeast genetic analysis and enable systematic genetic interaction studies. In its simplest form, SGA consists of a series of replica pinning steps, which enable the construction of haploid double mutants through mating and meiotic recombination. For example, a strain carrying a query mutation, such as a deletion allele of a nonessential gene or a conditional temperature sensitive allele of an essential gene, could be crossed to an input array of yeast mutants, such as the complete set of ~5,000 viable deletion mutants, to generate an output array of double mutants, that can be scored for genetic interactions based on estimates of cellular fitness derived from colony-size measurements. A simple quantitative measure of genetic interactions can be derived from colony size, which serves as a proxy for fitness. Furthermore, SGA can be applied in a variety of other contexts, such as Synthetic Dosage Lethality (SDL), in which a query mutation is crossed into an array of yeast strains, each of which overexpresses a different gene, thus making use of SGA to probe for gain-of-function phenotypes in specific genetic backgrounds. High-Content Screening (HCS) also integrates SGA to perform genome-wide screens for quantitative analysis of morphological phenotypes or pathway activity based upon fluorescent markers, extending genetic interaction analysis beyond fitness-based measurements. Genetic interaction studies offer insight into gene function, pathway structure, and buffering, and thus a complete genetic interaction network of yeast will generate a global functional wiring diagram for a eukaryotic cell.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Greece 1 1%
Germany 1 1%
Unknown 77 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 23%
Student > Master 12 15%
Researcher 11 14%
Student > Bachelor 10 13%
Student > Doctoral Student 4 5%
Other 9 11%
Unknown 15 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 33 42%
Agricultural and Biological Sciences 18 23%
Computer Science 4 5%
Engineering 2 3%
Medicine and Dentistry 2 3%
Other 7 9%
Unknown 13 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 September 2014.
All research outputs
#14,518,338
of 23,262,131 outputs
Outputs from Methods in molecular biology
#4,282
of 13,319 outputs
Outputs of similar age
#176,307
of 307,642 outputs
Outputs of similar age from Methods in molecular biology
#169
of 591 outputs
Altmetric has tracked 23,262,131 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,319 research outputs from this source. They receive a mean Attention Score of 3.4. This one has gotten more attention than average, scoring higher than 64% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 307,642 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 591 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.