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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
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    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.
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    Chapter 18 Identification of Links Between Cellular Pathways by Genetic Interaction Mapping (GIM).
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    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 22: Predicting Gene and Genomic Regulation in Saccharomyces cerevisiae, using the YEASTRACT Database: A Step-by-Step Guided Analysis.
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Chapter title
Predicting Gene and Genomic Regulation in Saccharomyces cerevisiae, using the YEASTRACT Database: A Step-by-Step Guided Analysis.
Chapter number 22
Book title
Yeast Functional Genomics
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3079-1_22
Pubmed ID
Book ISBNs
978-1-4939-3078-4, 978-1-4939-3079-1
Authors

Teixeira, Miguel C, Monteiro, Pedro T, Sá-Correia, Isabel, Miguel C. Teixeira, Pedro T. Monteiro, Isabel Sá-Correia

Abstract

Transcriptional regulation is one of the key steps in the control of gene expression, with huge impact on the survival, adaptation, and fitness of all organisms. However, it is becoming increasingly clear that transcriptional regulation is far more complex than initially foreseen. In model organisms such as the yeast Saccharomyces cerevisiae evidence has been piling up showing that the expression of each gene can be controlled by several transcription factors, in the close dependency of the environmental conditions. Furthermore, transcription factors work in intricate networks, being themselves regulated at the transcriptional, post-transcriptional, and post-translational levels, working in cooperation or antagonism in the promoters of their target genes.In this chapter, a step-by-step guide using the YEASTRACT database is provided, for the prediction and ranking of the transcription factors required for the regulation of the expression a single gene and of a genome-wide response. These analyses are illustrated with the regulation of the PDR18 gene and of the transcriptome-wide changes induced upon exposure to the herbicide 2,4-Dichlorophenoxyacetic acid (2,4-D), respectively. The newest potentialities of this information system are explored, and the various results obtained in the dependency of the querying criteria are discussed in terms of the knowledge gathered on the biological responses considered as case studies.

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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 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 13%
Portugal 1 13%
Unknown 6 75%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 50%
Professor 1 13%
Professor > Associate Professor 1 13%
Other 1 13%
Unknown 1 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 25%
Business, Management and Accounting 1 13%
Agricultural and Biological Sciences 1 13%
Computer Science 1 13%
Immunology and Microbiology 1 13%
Other 0 0%
Unknown 2 25%
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 21 October 2015.
All research outputs
#13,957,995
of 22,830,751 outputs
Outputs from Methods in molecular biology
#3,932
of 13,126 outputs
Outputs of similar age
#199,561
of 393,554 outputs
Outputs of similar age from Methods in molecular biology
#386
of 1,470 outputs
Altmetric has tracked 22,830,751 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% 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 68% 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 48th percentile – i.e., 48% 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 71% of its contemporaries.