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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 6: Mapping the Transcriptome-Wide Landscape of RBP Binding Sites Using gPAR-CLIP-seq: Bioinformatic Analysis.
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Chapter title
Mapping the Transcriptome-Wide Landscape of RBP Binding Sites Using gPAR-CLIP-seq: Bioinformatic Analysis.
Chapter number 6
Book title
Yeast Functional Genomics
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3079-1_6
Pubmed ID
Book ISBNs
978-1-4939-3078-4, 978-1-4939-3079-1
Authors

Freeberg, Mallory A, Kim, John K, Mallory A. Freeberg, John K. Kim

Abstract

Protein-RNA interactions are integral components of posttranscriptional gene regulatory processes including mRNA processing and assembly of cellular architectures. Dysregulation of RNA-binding protein (RBP) expression or disruptions in RBP-RNA interactions underlie a variety of human pathologies and genetic diseases including cancer and neurodegenerative diseases (reviewed in (Cooper et al., Cell 136(4):777-793, 2009; Darnell, Cancer Res Treat 42(3):125-129, 2010; Lukong et al., Trends Genet 24 (8):416-425, 2008)). Recent studies have uncovered only a small proportion of the extensive RBP-RNA interactome in any organism (Baltz et al., Mol Cell 46(5):674-690, 2012; Castello et al., Cell 149(6):1393-1406, 2012; Freeberg et al., Genome Biol 14(2):R13, 2013; Hogan et al., PLoS Biol 6(10):e255, 2008; Mitchell et al., Nat Struct Mol Biol 20(1):127-133, 2013; Tsvetanova et al. PLoS One 5(9): pii: e12671, 2010; Schueler et al., Genome Biol 15(1):R15, 2014; Silverman et al., Genome Biol 15(1):R3, 2014). To expand our understanding of how RBP-RNA interactions govern RNA-related processes, we developed gPAR-CLIP-seq (global photoactivatable-ribonucleoside-enhanced cross-linking and precipitation followed by deep sequencing) for capturing and sequencing all regions of the Saccharomyces cerevisiae transcriptome bound by RBPs (Freeberg et al., Genome Biol 14(2):R13, 2013). This chapter describes a pipeline for bioinformatic analysis of gPAR-CLIP-seq data. The first half of this pipeline can be implemented by running locally installed programs or by running the programs using the Galaxy platform (Blankenberg et al., Curr Protoc Mol Biol. Chapter 19:Unit 19 10 11-21, 2010; Giardine et al., Genome Res 15 (10):1451-1455, 2005; Goecks et al., Genome Biol 11(8):R86, 2010). The second half of this pipeline can be implemented by user-generated code in any language using the pseudocode provided as a template.

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The data shown below were collected from the profiles of 4 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 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 45%
Unspecified 1 9%
Other 1 9%
Professor 1 9%
Student > Doctoral Student 1 9%
Other 2 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 4 36%
Computer Science 2 18%
Agricultural and Biological Sciences 2 18%
Unspecified 1 9%
Medicine and Dentistry 1 9%
Other 1 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 18 June 2016.
All research outputs
#6,585,131
of 23,298,349 outputs
Outputs from Methods in molecular biology
#2,002
of 13,337 outputs
Outputs of similar age
#104,882
of 396,031 outputs
Outputs of similar age from Methods in molecular biology
#235
of 1,473 outputs
Altmetric has tracked 23,298,349 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 13,337 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 84% 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 396,031 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 1,473 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.