Chapter 2
Bioinformatic Tools in Arabidopsis Research
G. Alex Mason, Alex Canto ´ -Pastor, Siobhan M. Brady,
and Nicholas J. Provart
Abstract
Bioinformatic tools are now an everyday part of a plant researcher’s collection of protocols. They allow
almost instantaneous access to large data sets encompassing genomes, transcriptomes, proteomes, epigenomes, and other “-omes,” which are now being generated with increasing speed and decreasing cost. With
the appropriate queries, such tools can generate quality hypotheses, sometimes without the need for new
experimental data. In this chapter, we will investigate some of the tools used for examining gene expression
and coexpression patterns, performing promoter analyses and functional classification enrichment for sets
of genes, and exploring protein–protein and protein–DNA interactions in Arabidopsis. We will also cover
additional tools that allow integration of data from several sources for improved hypothesis generation.
Key words Transcriptomics, eFP, Bioinformatics, Proteomics, Protein–protein interactions, Coexpression, Functional classification, Functional genomics, Promoter analysis, Subcellular localization
1 Introduction
The past decade has been transformative for plant biology. Numerous sequencing-based methods have enabled high-throughput
analysis of genomes, epigenomes, transcriptomes, and protein–
protein or protein–DNA interactions [1]. Other high-throughput
methods have enabled quantitative proteome and metabolome
measurements. While each individual data set has been of obvious
value to the plant biologist who created it, once publicly available
these data sets are useful to plant biologists around the world for
Jose J. Sanchez-Serrano and Julio Salinas (eds.), Arabidopsis Protocols, Methods in Molecular Biology, vol. 2200,
https://doi.org/10.1007/978-1-0716-0880-7_2, © Springer Science+Business Media, LLC, part of Springer Nature 2021
Electronic Supplementary Material: The online version of this chapter (https://doi.org/10.1007/978-10716-0880-7_2) contains supplementary material, which is available to authorized users.
G. Alex Mason and Alex Canto ´ -Pastor are co-first authors.
This chapter is a revision of a chapter with the same name by Miguel de Lucas, Nicholas J. Provart, and Siobhan
Brady in Arabidopsis Protocols (2014, 1062, pp 97–136), edited by Jose ´ Juan Sanchez Serrano. All material has
been revised and updated as of May 2019, and several new tools are described.
25
Bioinformatic Tools in Arabidopsis Research
G. Alex Mason, Alex Canto ´ -Pastor, Siobhan M. Brady,
and Nicholas J. Provart
Abstract
Bioinformatic tools are now an everyday part of a plant researcher’s collection of protocols. They allow
almost instantaneous access to large data sets encompassing genomes, transcriptomes, proteomes, epigenomes, and other “-omes,” which are now being generated with increasing speed and decreasing cost. With
the appropriate queries, such tools can generate quality hypotheses, sometimes without the need for new
experimental data. In this chapter, we will investigate some of the tools used for examining gene expression
and coexpression patterns, performing promoter analyses and functional classification enrichment for sets
of genes, and exploring protein–protein and protein–DNA interactions in Arabidopsis. We will also cover
additional tools that allow integration of data from several sources for improved hypothesis generation.
Key words Transcriptomics, eFP, Bioinformatics, Proteomics, Protein–protein interactions, Coexpression, Functional classification, Functional genomics, Promoter analysis, Subcellular localization
1 Introduction
The past decade has been transformative for plant biology. Numerous sequencing-based methods have enabled high-throughput
analysis of genomes, epigenomes, transcriptomes, and protein–
protein or protein–DNA interactions [1]. Other high-throughput
methods have enabled quantitative proteome and metabolome
measurements. While each individual data set has been of obvious
value to the plant biologist who created it, once publicly available
these data sets are useful to plant biologists around the world for
Jose J. Sanchez-Serrano and Julio Salinas (eds.), Arabidopsis Protocols, Methods in Molecular Biology, vol. 2200,
https://doi.org/10.1007/978-1-0716-0880-7_2, © Springer Science+Business Media, LLC, part of Springer Nature 2021
Electronic Supplementary Material: The online version of this chapter (https://doi.org/10.1007/978-10716-0880-7_2) contains supplementary material, which is available to authorized users.
G. Alex Mason and Alex Canto ´ -Pastor are co-first authors.
This chapter is a revision of a chapter with the same name by Miguel de Lucas, Nicholas J. Provart, and Siobhan
Brady in Arabidopsis Protocols (2014, 1062, pp 97–136), edited by Jose ´ Juan Sanchez Serrano. All material has
been revised and updated as of May 2019, and several new tools are described.
25