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Plant Bioinformatics

Overview of attention for book
Cover of 'Plant Bioinformatics'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Using GenBank.
  3. Altmetric Badge
    Chapter 2 UniProtKB/Swiss-Prot, the Manually Annotated Section of the UniProt KnowledgeBase: How to Use the Entry View.
  4. Altmetric Badge
    Chapter 3 KEGG Bioinformatics Resource for Plant Genomics and Metabolomics.
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    Chapter 4 Plant Bioinformatics
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    Chapter 5 The Plant Ontology: A Tool for Plant Genomics.
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    Chapter 6 Ensembl Plants: Integrating Tools for Visualizing, Mining, and Analyzing Plant Genomics Data.
  8. Altmetric Badge
    Chapter 7 Gramene: A Resource for Comparative Analysis of Plants Genomes and Pathways.
  9. Altmetric Badge
    Chapter 8 PGSB/MIPS Plant Genome Information Resources and Concepts for the Analysis of Complex Grass Genomes.
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    Chapter 9 MaizeGDB: The Maize Genetics and Genomics Database.
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    Chapter 10 WheatGenome.info: A Resource for Wheat Genomics Resource.
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    Chapter 11 User Guidelines for the Brassica Database: BRAD.
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    Chapter 12 TAG Sequence Identification of Genomic Regions Using TAGdb.
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    Chapter 13 Short Read Alignment Using SOAP2.
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    Chapter 14 Tablet: Visualizing Next-Generation Sequence Assemblies and Mappings.
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    Chapter 15 Analysis of Genotyping-by-Sequencing (GBS) Data.
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    Chapter 16 Skim-Based Genotyping by Sequencing Using a Double Haploid Population to Call SNPs, Infer Gene Conversions, and Improve Genome Assemblies.
  18. Altmetric Badge
    Chapter 17 Finding and Characterizing Repeats in Plant Genomes.
  19. Altmetric Badge
    Chapter 18 Analysis of RNA-Seq Data Using TopHat and Cufflinks.
Attention for Chapter 18: Analysis of RNA-Seq Data Using TopHat and Cufflinks.
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Chapter title
Analysis of RNA-Seq Data Using TopHat and Cufflinks.
Chapter number 18
Book title
Plant Bioinformatics
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3167-5_18
Pubmed ID
Book ISBNs
978-1-4939-3166-8, 978-1-4939-3167-5
Authors

Ghosh, Sreya, Chan, Chon-Kit Kenneth, Sreya Ghosh, Chon-Kit Kenneth Chan

Editors

David Edwards

Abstract

The recent advances in high throughput RNA sequencing (RNA-Seq) have generated huge amounts of data in a very short span of time for a single sample. These data have required the parallel advancement of computing tools to organize and interpret them meaningfully in terms of biological implications, at the same time using minimum computing resources to reduce computation costs. Here we describe the method of analyzing RNA-seq data using the set of open source software programs of the Tuxedo suite: TopHat and Cufflinks. TopHat is designed to align RNA-seq reads to a reference genome, while Cufflinks assembles these mapped reads into possible transcripts and then generates a final transcriptome assembly. Cufflinks also includes Cuffdiff, which accepts the reads assembled from two or more biological conditions and analyzes their differential expression of genes and transcripts, thus aiding in the investigation of their transcriptional and post transcriptional regulation under different conditions. We also describe the use of an accessory tool called CummeRbund, which processes the output files of Cuffdiff and gives an output of publication quality plots and figures of the user's choice. We demonstrate the effectiveness of the Tuxedo suite by analyzing RNA-Seq datasets of Arabidopsis thaliana root subjected to two different conditions.

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X Demographics

The data shown below were collected from the profiles of 5 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 264 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Spain 1 <1%
United States 1 <1%
Unknown 261 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 44 17%
Student > Master 38 14%
Researcher 37 14%
Student > Bachelor 22 8%
Student > Doctoral Student 18 7%
Other 44 17%
Unknown 61 23%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 71 27%
Agricultural and Biological Sciences 71 27%
Medicine and Dentistry 12 5%
Computer Science 8 3%
Immunology and Microbiology 8 3%
Other 25 9%
Unknown 69 26%
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 30 March 2023.
All research outputs
#14,739,809
of 25,600,774 outputs
Outputs from Methods in molecular biology
#3,822
of 14,303 outputs
Outputs of similar age
#194,047
of 400,920 outputs
Outputs of similar age from Methods in molecular biology
#337
of 1,465 outputs
Altmetric has tracked 25,600,774 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 14,303 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 72% 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 400,920 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 50% of its contemporaries.
We're also able to compare this research output to 1,465 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.