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What are the Different Types of Plant Bioinformatic Methods?
Bioinformatics, biotic and abiotic, GWAS, NGS, plant breeding, plant sequencing, plant pathogen, sequence analysis, computational proteomics, microarray data analysis, bio-ontology, biological database.
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. Major advances in molecular biology and genomic technologies have led to an exponential growth in biological information.
In plant biotechnology, the amount of information has multiplied exponentially with many databases available from many individual plant species. Efficient bioinformatics tools and methodologies are also developed to allow rapid genome sequence and the study of plant genome in the ‘omics’ approach.
Bioinformatics, as a new emerging interdisciplinary field, has many tools and techniques that are essential for efficient sorting and organizing of biological data into databases. Bioinformatics can be referred to as a computer-based scientific field which applies mathematics, biology, and computer science to form into a single discipline for the analyses and interpretation of genomics and proteomics data.
In short, the main components of bioinformatics are (a) the collection and analysis of databases and (b) the development of software tools and algorithms as a tool for interpretation of biological data. Bioinformatics played a crucial role in many areas of biology as its applications provide various types of data, including nucleotide and amino acid sequences, protein domains and structure as well as expression patterns from various organisms.
Biotechnology and Bioinformatics for Plant Breeding
Plant breeding can be defined as the changing or improvement of desired traits in plants to produce improved new crops for the benefit of humankind. The revolution of life science in molecular biology and genomics has enabled the leaps forward in plant breeding by applying the knowledge and biological data obtained in genomics research on crops.
The evolution of next-generation sequencing (NGS) and other sequencing technologies produces a large size of biological data which requires databases to store the information. SNP is the unique allelic variation within a genome of same species, which can be used as biological markers to locate the genes associated with desired traits in plants such as rice, wheat, and maize.
In rice biotechnology, one of the most widely known GM rice is golden rice. Golden rice is a variety of rice engineered by introducing the biosynthetic pathway to produce β-carotene (pro-vitamin A) into staple food to resolve vitamin A deficiency.
Vitamin A is an essential nutrient to humans as it helps with development of vision, growth, cellular differentiation, and proliferation of immune system; insufficient intake of vitamin A may lead to childhood blindness, anemia, and reduced immune responsiveness against infection.
Wheat, as the most widely grown consumed crop, together with rice and maize contributes more than 60% of the calories and protein for our daily life. Advances in next-generation sequencing (NGS) platforms and other bioinformatics tools have revealed the extensive structural rearrangements and complex gene content in wheat, which revolutionized wheat genomics with the improvement of wheat yield and its adaptation to diverse environments.
For instance, genome-wide association studies (GWAS) are an approach used in genome research which allows rapid screening of raw data to select specific regions with agronomic traits.
Maize, a globally important crop, not only has a wide variety of uses in terms of economic impact but can also serve as genetic model species in genotype to phenotype relationship in plant genomic studies.
The introduction of NGS techniques in several crops including maize allowed the rapid de novo genome sequencing and production of huge amount genomics and phenomics information. The integration and visualization of high-quality data with these tools enables quick prioritizing phenotype of interest in crops, which play a crucial role in the improvement of plant breeding.
Bioinformatics for Studying Stress Resistance in Plants
Stress response in plants can be divided into biotic and abiotic. Biotic stress mainly refers to negative influence caused by living organism such as virus, fungi, bacteria, insects, nematodes, and weeds while abiotic stress refers to factors such as extreme temperature, drought, flood, salinity, and radiation which dramatically affect the crop yield.
NGS technologies and other potent computational tools, which allowed sequencing of whole genome and transcriptome, have led to the extensive studies of plants towards stress response on a molecular basis.
Bioinformatics Approaches to Study Resistance to Plant Pathogen
The study of plant pathogens plays an essential role in the study of plant diseases, including pathogen identification, disease etiology, disease resistance, and economic impact, among others. Plants protect themselves through a complex defense system against a variety of pathogens, including insects, bacteria, fungi, and viruses. A clearer picture of plant-pathogen interactions in the context of transcriptomic and proteomics can be visualized through the application of different bioinformatics tools, which in turn made feasible the engineering resistance to microbial pathogen in plant.
Applications of Bioinformatics in Plant Biotechnology
The introduction of bioinformatics and computational biology into the area of plant biology is drastically accelerating scientific invention in life science.
Sequence analysis is the most fundamental approach to obtaining the whole genome sequence such as DNA, RNA, and protein sequence from an organism’s genome in modern science.
A complete sequence data consists of coding and non-coding regions, which can act as a necessary precursor for any functional gene that determines the unique traits possessed by organisms.
With the emergence of next-generation sequencing (NGS) and some other omics technologies used to examine plants genomics, more and more sequenced plants genome will be revealed.
To deal with these vast amounts of data, the development and implementation of bioinformatics allow scientists to capture, store, and organize them in a systematic database.
Bioinformaticians and experts with mathematical and programming skills will play an important role in bringing fresh approaches and knowledge into bioinformatics, not only for the advancement in plant biotechnology and agriculture sector, but the future of humanity as well.
Bioinformatics play a significant role in the development of the agriculture sector as it helps to study stress resistance and plant pathogens, which are critical in advancing crop breeding. There is a critical need for effective bioinformatic tools which can provide longer reads with unbiased coverage to overcome the complexity of the plant’s genome.
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