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Yeast Functional Genomics

Overview of attention for book
Cover of 'Yeast Functional Genomics'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Using RNA-seq for Analysis of Differential Gene Expression in Fungal Species.
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    Chapter 2 Enhancing Structural Annotation of Yeast Genomes with RNA-Seq Data.
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    Chapter 3 Pathogen Gene Expression Profiling During Infection Using a Nanostring nCounter Platform.
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    Chapter 4 Comparative Transcriptomics in Yeasts.
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    Chapter 5 Mapping the Transcriptome-Wide Landscape of RBP Binding Sites Using gPAR-CLIP-seq: Experimental Procedures.
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    Chapter 6 Mapping the Transcriptome-Wide Landscape of RBP Binding Sites Using gPAR-CLIP-seq: Bioinformatic Analysis.
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    Chapter 7 Translation Analysis at the Genome Scale by Ribosome Profiling.
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    Chapter 8 Biotin-Genomic Run-On (Bio-GRO): A High-Resolution Method for the Analysis of Nascent Transcription in Yeast.
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    Chapter 9 Genome-Wide Probing of RNA Structures In Vitro Using Nucleases and Deep Sequencing.
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    Chapter 10 Genome-Wide Chromatin Immunoprecipitation in Candida albicans and Other Yeasts.
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    Chapter 11 ChIPseq in Yeast Species: From Chromatin Immunoprecipitation to High-Throughput Sequencing and Bioinformatics Data Analyses.
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    Chapter 12 Systematic Determination of Transcription Factor DNA-Binding Specificities in Yeast.
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    Chapter 13 Generation and Analysis of Chromosomal Contact Maps of Yeast Species.
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    Chapter 14 A Versatile Procedure to Generate Genome-Wide Spatiotemporal Program of Replication in Yeast Species.
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    Chapter 15 Single-Step Affinity Purification (ssAP) and Mass Spectrometry of Macromolecular Complexes in the Yeast S. cerevisiae.
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    Chapter 16 Label-Free Quantitative Proteomics in Yeast.
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    Chapter 17 Profiling of Yeast Lipids by Shotgun Lipidomics.
  19. Altmetric Badge
    Chapter 18 Identification of Links Between Cellular Pathways by Genetic Interaction Mapping (GIM).
  20. Altmetric Badge
    Chapter 19 On the Mapping of Epistatic Genetic Interactions in Natural Isolates: Combining Classical Genetics and Genomics.
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    Chapter 20 Experimental Evolution and Resequencing Analysis of Yeast.
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    Chapter 21 Reconstruction and Analysis of the Evolution of Modular Transcriptional Regulatory Programs Using Arboretum.
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    Chapter 22 Predicting Gene and Genomic Regulation in Saccharomyces cerevisiae, using the YEASTRACT Database: A Step-by-Step Guided Analysis.
Attention for Chapter 19: On the Mapping of Epistatic Genetic Interactions in Natural Isolates: Combining Classical Genetics and Genomics.
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Chapter title
On the Mapping of Epistatic Genetic Interactions in Natural Isolates: Combining Classical Genetics and Genomics.
Chapter number 19
Book title
Yeast Functional Genomics
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3079-1_19
Pubmed ID
Book ISBNs
978-1-4939-3078-4, 978-1-4939-3079-1
Authors

Hou, Jing, Schacherer, Joseph, Jing Hou, Joseph Schacherer

Abstract

Genetic variation within species is the substrate of evolution. Epistasis, which designates the non-additive interaction between loci affecting a specific phenotype, could be one of the possible outcomes of genetic diversity. Dissecting the basis of such interactions is of current interest in different fields of biology, from exploring the gene regulatory network, to complex disease genetics, to the onset of reproductive isolation and speciation. We present here a general workflow to identify epistatic interactions between independently evolving loci in natural populations of the yeast Saccharomyces cerevisiae. The idea is to exploit the genetic diversity present in the species by evaluating a large number of crosses and analyzing the phenotypic distribution in the offspring. For a cross of interest, both parental strains would have a similar phenotypic value, whereas the resulting offspring would have a bimodal distribution of the phenotype, possibly indicating the presence of epistasis. Classical segregation analysis of the tetrads uncovers the penetrance and complexity of the interaction. In addition, this segregation could serve as the guidelines for choosing appropriate mapping strategies to narrow down the genomic regions involved. Depending on the segregation patterns observed, we propose different mapping strategies based on bulk segregant analysis or consecutive backcrosses followed by high-throughput genome sequencing. Our method is generally applicable to all systems with a haplodiplobiontic life cycle and allows high resolution mapping of interacting loci that govern various DNA polymorphisms from single nucleotide mutations to large-scale structural variations.

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X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 44%
Researcher 3 33%
Professor > Associate Professor 2 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 44%
Agricultural and Biological Sciences 4 44%
Computer Science 1 11%
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 26 October 2015.
All research outputs
#14,827,133
of 22,830,751 outputs
Outputs from Methods in molecular biology
#4,696
of 13,126 outputs
Outputs of similar age
#218,886
of 393,554 outputs
Outputs of similar age from Methods in molecular biology
#469
of 1,470 outputs
Altmetric has tracked 22,830,751 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,126 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 59% 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 393,554 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,470 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 63% of its contemporaries.