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OMICS. 2019 Apr;23(4):190-206. doi: 10.1089/omi.2019.0024.

Plant Proteome Databases and Bioinformatic Tools: An Expert Review and Comparative Insights.

Author information

1
1 Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore, India.
2
2 Institute of Bioinformatics, International Technology Park, Bangalore, India.

Abstract

Historically, plant biology studies have lagged behind systems biology studies in animals and humans. However, there are signs of positive change as evidenced by the rise of big data in plant proteomics, and the availability of data science tools and next-generation sequencing technologies. Currently, the sequence information on nearly 300 plant species is available although they are curated to varying degrees of sophistication. This has led to significant enrichment of representations in the corresponding plant proteome databases. Analysis of the proteome component of an organism offers structural, functional, and network scale insights. Moreover, the development of high-throughput mass spectrometric techniques has augmented our understanding of proteins and their expression patterns under various conditions. Several thousand proteins can now be identified from a single mass spectrometric analysis. In this expert review, we provide an in-depth analysis on plant proteome databases, how to access them, and, importantly, the biological, research, and application contexts in which each database is significant, their comparative strengths, and limitations. We aimed in this analysis to reach out to young scholars embarking on plant biology and proteomic research as well as to those already established in the field so as to provide integrated critical analyses of plant proteome databases and bioinformatics tools in this nascent field of systems sciences. In conclusion, plant proteome research is an emerging and exciting frontier of integrative biology scholarship and innovation. Our future efforts must also be invested in integrating the available databases to allow for multiomics data analysis, research, and development.

KEYWORDS:

bioinformatics; data science; databases; phosphoproteomics; proteomics

PMID:
31009332
DOI:
10.1089/omi.2019.0024

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