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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 8: Biotin-Genomic Run-On (Bio-GRO): A High-Resolution Method for the Analysis of Nascent Transcription in Yeast.
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Chapter title
Biotin-Genomic Run-On (Bio-GRO): A High-Resolution Method for the Analysis of Nascent Transcription in Yeast.
Chapter number 8
Book title
Yeast Functional Genomics
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3079-1_8
Pubmed ID
26483020
Book ISBNs
978-1-4939-3078-4, 978-1-4939-3079-1
Authors

Jordán-Pla, Antonio, Miguel, Ana, Serna, Eva, Pelechano, Vicent, Pérez-Ortín, José E, Antonio Jordán-Pla, Ana Miguel, Eva Serna, Vicent Pelechano, José E. Pérez-Ortín

Abstract

Transcription is a highly complex biological process, with extensive layers of regulation, some of which remain to be fully unveiled and understood. To be able to discern the particular contributions of the several transcription steps it is crucial to understand RNA polymerase dynamics and regulation throughout the transcription cycle. Here we describe a new nonradioactive run-on based method that maps elongating RNA polymerases along the genome. In contrast with alternative methodologies for the measurement of nascent transcription, the BioGRO method is designed to minimize technical noise that arises from two of the most common sources that affect this type of strategies: contamination with mature RNA and amplification-based technical biasing. The method is strand-specific, compatible with commercial microarrays, and has been successfully applied to both yeasts Saccharomyces cerevisiae and Candida albicans. BioGRO profiling provides powerful insights not only into the biogenesis and regulation of canonical gene transcription but also into the noncoding and antisense transcriptomes.

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X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 38%
Student > Ph. D. Student 2 15%
Professor > Associate Professor 2 15%
Student > Master 1 8%
Student > Doctoral Student 1 8%
Other 0 0%
Unknown 2 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 6 46%
Agricultural and Biological Sciences 4 31%
Computer Science 1 8%
Unknown 2 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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
#15,821,622
of 23,498,099 outputs
Outputs from Methods in molecular biology
#5,527
of 13,368 outputs
Outputs of similar age
#234,750
of 396,908 outputs
Outputs of similar age from Methods in molecular biology
#553
of 1,472 outputs
Altmetric has tracked 23,498,099 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,368 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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 396,908 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,472 others from the same source and published within six weeks on either side of this one. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.

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