Bioinformatics and Its Applications in Plant Biology

Abstract

Bioinformatics plays an essential role in today's plant science. As the amount of data grows exponentially, there is a parallel growth in the demand for tools and methods in data management, visualization, integration, analysis, modeling, and prediction. At the same time, many researchers in biology are unfamiliar with available bioinformatics methods, tools, and databases, which could lead to missed opportunities or misinterpretation of the information. In this review, we describe some of the key concepts, methods, software packages, and databases used in bioinformatics, with an emphasis on those relevant to plant science. We also cover some fundamental issues related to biological sequence analyses, transcriptome analyses, computational proteomics, computational metabolomics, bio-ontologies, and biological databases. Finally, we explore a few emerging research topics in bioinformatics.

On the Locality of Data and Claims about Phenomena

“…One of its main objectives is to exploit new technologies to construct digital databases that are freely and easily available for consultation (Rhee et al 2006). Aside from the technical problems of building such resources, bioinformaticians have to confront two main issues: (1) the fragmentation of model organism biology into epistemic communities with their own expertise, traditions, favourite methods, instruments and research goals (Knorr Cetina 1999), which implies finding a vocabulary and a format that make data retrievable by anyone according to their own interests and background; and (2) the diverse characteristics of data coming from disparate sources and produced for a variety of purposes, which make it difficult to assess the evidential scope of the data that become available ('what are they evidence for?')…”

Identification of biochemical differences between different forms of male infertility by nuclear magnetic resonance (NMR) spectroscopy

“…Differences in the concentration of specific biomarkers are then used in creating unique metabolomics profiles which would help differentiate test subjects from controls. This information is then coupled with bioinformatics to make the analysis as predictive as possible [28,29,34].…”

Systems Biology for Smart Crops and Agricultural Innovation: Filling the Gaps between Genotype and Phenotype for Complex Traits Linked with Robust Agricultural Productivity and Sustainability

“…The investigated information can then be incorporated into models of system function (Gutierrez et al, 2005;Rhee et al, 2006). New hypotheses generated by these models can be tested experimentally, which may lead to revised models and novel testable hypotheses.…”