↓ Skip to main content

Yeast Functional Genomics

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

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Using RNA-seq for Analysis of Differential Gene Expression in Fungal Species.
  3. Altmetric Badge
    Chapter 2 Enhancing Structural Annotation of Yeast Genomes with RNA-Seq Data.
  4. Altmetric Badge
    Chapter 3 Pathogen Gene Expression Profiling During Infection Using a Nanostring nCounter Platform.
  5. Altmetric Badge
    Chapter 4 Comparative Transcriptomics in Yeasts.
  6. Altmetric Badge
    Chapter 5 Mapping the Transcriptome-Wide Landscape of RBP Binding Sites Using gPAR-CLIP-seq: Experimental Procedures.
  7. Altmetric Badge
    Chapter 6 Mapping the Transcriptome-Wide Landscape of RBP Binding Sites Using gPAR-CLIP-seq: Bioinformatic Analysis.
  8. Altmetric Badge
    Chapter 7 Translation Analysis at the Genome Scale by Ribosome Profiling.
  9. Altmetric Badge
    Chapter 8 Biotin-Genomic Run-On (Bio-GRO): A High-Resolution Method for the Analysis of Nascent Transcription in Yeast.
  10. Altmetric Badge
    Chapter 9 Genome-Wide Probing of RNA Structures In Vitro Using Nucleases and Deep Sequencing.
  11. Altmetric Badge
    Chapter 10 Genome-Wide Chromatin Immunoprecipitation in Candida albicans and Other Yeasts.
  12. Altmetric Badge
    Chapter 11 ChIPseq in Yeast Species: From Chromatin Immunoprecipitation to High-Throughput Sequencing and Bioinformatics Data Analyses.
  13. Altmetric Badge
    Chapter 12 Systematic Determination of Transcription Factor DNA-Binding Specificities in Yeast.
  14. Altmetric Badge
    Chapter 13 Generation and Analysis of Chromosomal Contact Maps of Yeast Species.
  15. Altmetric Badge
    Chapter 14 A Versatile Procedure to Generate Genome-Wide Spatiotemporal Program of Replication in Yeast Species.
  16. Altmetric Badge
    Chapter 15 Single-Step Affinity Purification (ssAP) and Mass Spectrometry of Macromolecular Complexes in the Yeast S. cerevisiae.
  17. Altmetric Badge
    Chapter 16 Label-Free Quantitative Proteomics in Yeast.
  18. Altmetric Badge
    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.
  21. Altmetric Badge
    Chapter 20 Experimental Evolution and Resequencing Analysis of Yeast.
  22. Altmetric Badge
    Chapter 21 Reconstruction and Analysis of the Evolution of Modular Transcriptional Regulatory Programs Using Arboretum.
  23. Altmetric Badge
    Chapter 22 Predicting Gene and Genomic Regulation in Saccharomyces cerevisiae, using the YEASTRACT Database: A Step-by-Step Guided Analysis.
Attention for Chapter 2: Enhancing Structural Annotation of Yeast Genomes with RNA-Seq Data.
Altmetric Badge

About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

twitter
3 X users

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
7 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
Enhancing Structural Annotation of Yeast Genomes with RNA-Seq Data.
Chapter number 2
Book title
Yeast Functional Genomics
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3079-1_2
Pubmed ID
Book ISBNs
978-1-4939-3078-4, 978-1-4939-3079-1
Authors

Devillers, Hugo, Morin, Nicolas, Neuvéglise, Cécile, Hugo Devillers, Nicolas Morin, Cécile Neuvéglise

Abstract

The number of fully sequenced genomes of yeasts is dramatically increasing but both structural and functional annotation quality are usually neglected, as most frequently based on automatic annotation transfer tools from reference genomes. RNA sequencing technologies offer the possibility to better characterize yeast transcriptomes and to correct or improve the prediction of mRNA, ncRNA, or miscellaneous RNA. We describe a computational approach to enhance structural annotation of yeast genomes based on RNA-Seq data exploitation. The proposed pipeline is primarily based on read mapping with TopHat2. Mapping outputs are then used for various applications such as: (1) validation of exon-exon junctions of predicted transcripts, (2) definition of new transcribed features, (3) prediction of 3' UTR, and (4) identification of extra features absent from the genome assembly. We strongly encourage curators to proceed to a manual validation and editing of the reference genome. Releasing genomes with high-quality annotation is an important issue, as they will be considered as references for further predictions.

X Demographics

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

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 43%
Student > Bachelor 2 29%
Student > Ph. D. Student 1 14%
Professor > Associate Professor 1 14%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 43%
Agricultural and Biological Sciences 1 14%
Computer Science 1 14%
Immunology and Microbiology 1 14%
Medicine and Dentistry 1 14%
Other 0 0%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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
#7,729,323
of 23,498,099 outputs
Outputs from Methods in molecular biology
#2,422
of 13,368 outputs
Outputs of similar age
#125,000
of 396,908 outputs
Outputs of similar age from Methods in molecular biology
#273
of 1,472 outputs
Altmetric has tracked 23,498,099 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% 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 has done well, scoring higher than 75% 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 396,908 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 55% of its contemporaries.
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 has done well, scoring higher than 79% of its contemporaries.