Genetic Data and Genome Sequencing Advances
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Bio-resource refers to the total biological variation manifested as individual plants, animals or their genes, which could be utilized as drugs, food or feed, etc., along with the development of improved crops and animals for higher yield and tolerance to biotic and abiotic stresses. Man depends on these bio-resources for his continued existence and, therefore, he must use and preserve them for future generations (Sayers et al., 2012). India harbors two hot spots of biodiversity of the world. These are the Eastern Himalayas and the Western Ghats which are abode of numerous plant, animals and microbial species. Since utilization of the available bio-resources, to our advantage, is an inevitable part of existence, there has to be a balance between uses of resources and their conservation. In this way, we could preserve our ecosystem, which although altered would still be rich in bio-resources.
At the level of DNA, genomic data reveal the information that is stored in the genomes of organisms and passed across generations. These include sequences of genes coding for functional proteins, regulatory motifs that serve as markers for the regulation of the expression of specific genes, as well and individual differences in the genetic composition of populations, such as single nucleotide polymorphisms (SNPs—common individual differences at a single nucleotide base) and copy number variants (CNVs—multiplicity or lack of certain DNA segments in genomic sequences), inversions, or transpositions. Sequence data drives translational research through genome-wide association studies (GWAS), discovery of driver mutations and structural variants in progressive diseases of plants, and transfer of biological knowledge across model organisms via comparative genomics (Wen et al., 2014; Lu et al., 2014). Genome sequencing is traditionally achieved by exploiting of the natural process of DNA replication. On the other hand, identification of SNPs and CNVs has been usually carried out using DNA microarrays, which exploit the natural process of hybridization. on the other hand, sequencing technology and associated computational techniques are now being transformed by the appearance of short read sequence data, also known as next-generation sequencing (Wei et al., 2013; Bohra, 2013).
Advances in bioinformatics and the development as well as improvement of high throughput sequencing methods have led to a virtual blast of genomics, proteomics, and metabolomics data, facilitating new and efficient approaches to problems in biotechnology, especially agricultural biotechnology (Lal et al., 2013; Gour et al., 2014). The whole sequencing of the Arabidopsis thaliana genome has been regarded as a landmark in plant biology and so far more than 10 plant genomes have been sequenced and sequencing of several other genomes is underway. Over the last two decades, comparative genomics has revealed that the organization of genes within crop plant genomes has remained conserved over the evolutionary periods. Advances in the field of structural and functional genomics and equitational bio-resource utilization will encompasses a wider range of technologies and disciplines such as biocomputational engineering, bioenergy, and genome systems analysis etc, so as to achieve the goals of world food and nutritional security. In our lab, we are deciphering the complete genome sequence of an economically important quarantined fungal pathogen i. e. Karnal bunt (Tillitia indica of Wheat) for identification of genes/proteins involved in pathogenesis. It will enable us to boost our efforts in molecular breeding and disease diagnostics.
The Genomics option amalgamates the use of computational tools to convert huge amounts of biological information from DNA sequencing, chips, and other high-throughput experimental methods into useful information that can be utilized for enhancing agricultural productivity. The importance is on DNA and protein sequence comparison, analysis and understanding the relationship between plant's genome and its phenotype (Martinez et al., 2013). The aim of functional genomics is to develop and synthesize genomic and proteomic knowledge into a recognition and better understanding of the dynamic properties of plant systems at cellular and/or organismal levels. This would offer a more complete picture of how biological function take place from the information encoded in an organism's genome. Besides, it will also provide opportunity to explore genomics for identification of novel genes from bio-resources having hidden value for engineering superior traits.
DNA microarrays have been used generally to monitor the retrieval of genomic information under various conditions. More specifically, the relative quantity of mRNA molecules that are present in a sample can be measured simultaneously for thousands of mRNA sequences (transcriptome), enabling comparison of the expression of thousands of genes in a given sample or across samples (Carnavale et al., 2013; Villarino et al., 2014). With the arrival of next-generation sequencing technologies, genome-wide assessment mRNA expression of is also becoming more reliable through whole transcriptome shotgun sequencing (RNAseq). Genome-wide assessment of mRNA expression enables identification of genes and groups of genes that are differentially expressed under various conditions, detection of coexpressed genes, and inference of the regulatory effect of genes among each other. Dysregulated genes identified via screening of the transcriptome serve as markers for diagnosis and prognosis of various diseases in plants, as well as targets for disease management (Liu et al., 2013).
The first time developing spikes of transcriptome of finger millet (Eleusine coracana) for understanding molecular basis of three complex agriculturally important traits of nutritional quality (micronutrient Ca, Fe, Zn and proteins), nitrogen use efficiency and stress responsiveness (Kumar et al., 2014).
As we enter 21st century, optimal utilization of available bio-resources in sync with the genomics approaches is poised to become a major platform for driving significant progress over the next 20-50 years. The knowledge and understanding of genome sequences, and their relation with metabolic control mechanisms will allow a sound scientific basis for a healthier and more reliable food supply.
Big Data in Genomics: Challenges and Solutions
These revolutionary modifications in Big Data generation and gaining make profound challenges for storage, transfer and security. Indeed, it may now be less costly to generate the data than it is to store it (Gour et al., 2014). One model of this issue is the National Centre for Biotechnology Information (NCBI). The NCBI has been leading Big Data efforts in plants and other organisms since 1988, but neither the NCBI nor anyone in the private sector has a complete, low-cost, and secure solution to the problem related to data storage and security purposes (Sayers et al., 2012). These potentials are beyond the arrive at small laboratories or institutions, posing various challenges for the future of agricultural research. Another challenge is to transfer data from one site to another; it is mainly done by shipping external hard disks through the mail.
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