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Plant Genomics Databases

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Cover of 'Plant Genomics Databases'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Ensembl Plants: Integrating Tools for Visualizing, Mining, and Analyzing Plant Genomic Data.
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    Chapter 2 PGSB/MIPS PlantsDB Database Framework for the Integration and Analysis of Plant Genome Data.
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    Chapter 3 Plant Genome DataBase Japan (PGDBj).
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    Chapter 4 FLAGdb(++): A Bioinformatic Environment to Study and Compare Plant Genomes.
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    Chapter 5 Mining Plant Genomic and Genetic Data Using the GnpIS Information System.
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    Chapter 6 The Bio-Analytic Resource for Plant Biology.
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    Chapter 7 The Evolution of Soybean Knowledge Base (SoyKB).
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    Chapter 8 Using TropGeneDB: A Database Containing Data on Molecular Markers, QTLs, Maps, Genotypes, and Phenotypes for Tropical Crops.
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    Chapter 9 Species-Specific Genome Sequence Databases: A Practical Review.
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    Chapter 10 A Guide to the PLAZA 3.0 Plant Comparative Genomic Database.
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    Chapter 11 Exploring Plant Co-Expression and Gene-Gene Interactions with CORNET 3.0.
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    Chapter 12 PlaNet: Comparative Co-Expression Network Analyses for Plants.
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    Chapter 13 Practical Utilization of OryzaExpress and Plant Omics Data Center Databases to Explore Gene Expression Networks in Oryza Sativa and Other Plant Species.
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    Chapter 14 Pathway Analysis and Omics Data Visualization Using Pathway Genome Databases: FragariaCyc, a Case Study.
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    Chapter 15 CSGRqtl: A Comparative Quantitative Trait Locus Database for Saccharinae Grasses.
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    Chapter 16 Plant Genome Duplication Database.
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    Chapter 17 Variant Effect Prediction Analysis Using Resources Available at Gramene Database.
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    Chapter 18 Plant Promoter Database (PPDB).
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    Chapter 19 Construction of the Leaf Senescence Database and Functional Assessment of Senescence-Associated Genes.
Attention for Chapter 6: The Bio-Analytic Resource for Plant Biology.
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Chapter title
The Bio-Analytic Resource for Plant Biology.
Chapter number 6
Book title
Plant Genomics Databases
Published in
Methods in molecular biology, January 2017
DOI 10.1007/978-1-4939-6658-5_6
Pubmed ID
Book ISBNs
978-1-4939-6656-1, 978-1-4939-6658-5
Authors

Jamie Waese, Nicholas J. Provart, Waese, Jamie, Provart, Nicholas J

Editors

Aalt D.J van Dijk

Abstract

Bioinformatic tools have become part of the way plant researchers undertake investigations. Large data sets encompassing genomes, transcriptomes, proteomes, epigenomes, and other "-omes" that have been generated in the past decade may be easily accessed with such tools, such that hypotheses may be generated at the click of a mouse. In this chapter, we'll cover the use of bioinformatic tools available at the Bio-Analytic Resource for Plant Biology at http://bar.utoronto.ca for exploring gene expression and coexpression patterns, undertaking promoter analyses, performing functional classification enrichment analyses for sets of genes, and examining protein-protein interactions. We also touch on some newer bioinformatic tools that allow integration of data from several sources for improved hypothesis generation, both for Arabidopsis and translationally. Most of the data sets come from Arabidopsis, but useful BAR tools for other species will be mentioned where appropriate.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
New Zealand 1 3%
Unknown 35 97%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 12 33%
Student > Master 8 22%
Researcher 7 19%
Lecturer 2 6%
Student > Ph. D. Student 1 3%
Other 2 6%
Unknown 4 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 44%
Biochemistry, Genetics and Molecular Biology 10 28%
Chemistry 3 8%
Neuroscience 1 3%
Medicine and Dentistry 1 3%
Other 0 0%
Unknown 5 14%
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 03 January 2017.
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#20,365,559
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Outputs from Methods in molecular biology
#9,921
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#355,406
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Outputs of similar age from Methods in molecular biology
#842
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