Unraveling the Impact of Bioinformatics and Omics in Agriculture
Total Page：16
File Type：pdf, Size：1020Kb
Central International Journal of Plant Biology & Research Review Aritcle *Corresponding author Jitendra Narayan, Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, UK, Unraveling the Impact of Tel: +91-7 835 999 528 / +44 0 742 477 477 0; Email: Submitted: 24 January 2015 Bioinformatics and Omics in Accepted: 20 April 2015 Published: 24 April 2015 Agriculture ISSN: 2333-6668 Copyright Rahul Agarwal1 and Jitendra Narayan2* © 2015 Narayan et al. 1Department of Animal and Aquacultural Sciences, Norwegian University of Life OPEN ACCESS Sciences (NMBU), Germany 2Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, UK Keywords • Agriculture Abstract • Crops The existence of diverse communities of plant species, including crops is crucial • Bioinformatics for maintaining the ecological balance between human kind and environment. Their • Omics existence also ensures the continuous food supply to human and animals. Therefore, it • Sequencing is critical to use modern biotechnological techniques in breeding scheme to increase • Breeding the productivity of the economical crops and plants so we can able to continuously feed the billions of organism in this planet. We have seen tremendous progress in the field of bioinformatics, genomics and sequencing recentlyand their potential to improve the economical and agronomic traits in various plants. Agriculture bioinformatics is about using latest genomic advancement, and bioinformatics tools and databases to enrich scientific community with genetic knowledge to yield drought, disease and insect resistant crops and other plant species. With current sequencing technologies, it is possible to sequence thousands of plant species together and then assemble those individually using de novo genome assemblers. Availability of genomic information make possible to trace candidate genes and mutationsassociated with particularcomplex trait. Methylation and microRNAs data make possible to disclose epigenetic regulation of candidate genes. This article reviews how current scenario in agriculture related research restructured entirely under influence of tremendous growth in bioinformatics and omics technology. INTRODUCTION The last decade has witnessed the dawn of a new era of bioinformatics and computational biology which increases the Agriculture is not only an important occupation of some peoples, but also way of life, culture, and custom of two-third of working population for their livelihood [1-3]. Rice, wheat, wepace usually of scientific do plant discovery related researchin life science in previous [13,14]. decades. Involvement Rapid barley, corn, sorghum, millet, sugar cane - ever since the Neolithic groundof computer breaking science evolution in field of plantsequencing biology technology has change over the wayfew revolution, cereals have always been considered as staple food past years made this technology so cost-effective that nowadays it in human populations across different continents [4,5]. From is usual for any experimental lab to employ sequencing methods thousands of years, humans are using breeding and selection to to study genome of interest [15,16]. Including modern bio- create the domestic varieties of these crops with desired traits technology advancement in agriculture will surely provide reap [5-8]. Considerable progress has been accomplished in taste, huge dividends to the bioenergy sector, agro-based industries, nutritional value and productivity, notably during the “Green agricultural by-products utilization, plant improvement, and Revolution” which took place between 1960 and 1970 [9,10]. better management of the environment [17,18]. Latest genome However, the Green Revolution has also known for its failures, and and transcriptomics sequencing of a plant species make possible we no longer able to surviveby few “high yield” varieties [11,12]. to reveal the genetic architecture of numerous plant species, Now, we need to use more advancedbiotechnologymethods the differences in thousands of individuals within and outside in agronomy in order to provide nutritionalfood tocontinuous population, the genes and mutations essential for improving the increasing world population while considering three major limitations- lessarable lands, depletion of energy resources and unpredictable climatechange.In other word, we need to increase specific desired complex traits [19-22]. the pace of research so we can able to secure enough food for disciplines, have led to an exponential growth of plant genomics, future generations. transcriptomics,The recent technological epigenomics, advances proteomics in the fieldand of metabolomics omics related Cite this article: Agarwal R, Narayan J (2015) Unraveling the Impact of Bioinformatics and Omics in Agriculture. Int J Plant Biol Res 3(2): 1039. Narayan et al. (2015) Email: Central data [23-25]. Hence there are immense responsibility on SEQUENCING AND OMICS RELATED DEVELOP- bioinformatics scientist to generate hypothesis and tools to MENT IN PLANT GENOMICS analyze these huge pileups of data. Data mining is a research Recently, lots of fast, high throughput and affordable sequencers from different biotechnology companies available computational tools to overcome the obstacles and constraints area that aims to provide the analysts with novel and efficient posed by the traditional statistical methods [26]. The current genomics to unthinkable pace[30-33]. Most of present sequencers mission inarea of bioinformatics is to provide the tools which not arein the coming market from which Illumina already platform; the research for example activity HiSeq2000 in the field has of a only give impeccablesolution, but also able to provide solution in short span of time. With huge data, there is also demand of paired end reads per run, and most of Illumina sequencer based onpotential the principle of obtaining of shotgun ∼600 approachGb of sequence where datapieces in of100 DNA bp ×are 2 so much information in lesser space [27]. Visualization and sheared randomly, cloned, and sequenced in parallel fashion integrationefficient data of differentstorage mechanism,kinds of omics thus data we arecan also able two to majorstore [34]. Generally, sequencing of anorganismwould likely produce a challenges in bioinformatics [28,29]. thousands of millions of paired end reads which need to assemble together into contigs and then into scaffolds which may later on Therefore, we need to use genomics resourcesavailable for assign with chromosome name based on presence of physical many non-model and model plant species as a result of rapid map of that organism [35,36]. At the forefront of plant genomics is the model dicotyledonous plant, Arabidopsis. Starting with the Arabidopsis EST project in the early 1990s [37,38] and knowntechnological as ‘Plant advances Genomics’. in omics Within and the bioinformatics scope of plant fields genomics, which finally led us to recognize new translational area of plant science we will be able to do following activities: Arabidopsis has led the plant community in capitalizing on genomicculminating era. with the first complete plant genome in 2000 [39], i) Sequencing and de novo assembly of non-model plant species EST SEQUENCING ii) Create an exhaustive inventory of genes with their Sequencing of Arabidopsis ESTs using traditional Sanger functional annotation and ontology. sequencing was done with a purposes to performthe gene discovery and its annotation, expression study, comparative iii) Discovery of a large quantity of SNP/ InDeLs markers to throughout whole genome [40]. (Table 1) represent list of some analysis, and to discover gene specific molecular markers iv) assistIdentify in fine“candidate mapping and genes/mutations/alleles”selection of superior breed. in agriculturally important plant species with its number of EST association withdesired traitsafter demarcating sequences (size) stored in NCBI dbEST which provide us access underlying QTLs from markersgenerated in ii) using QTL toEST data of all sequenced organism. If an EST matches a DNA mapping methods e.g. GWAS. Table 1: dbEST record of some plant species. v) Create “MarkerChip Panel” for the purpose of genotyping * and selection. This panel can also be used for other similar No. Scientific Name Common Name Sizes variety of breeds. 1 Arabidopsis thaliana Thale cress 1,529,700 2 Oryza sativa Rice 1,253,557 vi) To study evolutionary pattern of genome within and among populations (population genomics). 3 Zea mays Maize 2,019,137 4 Triticum aestivum Wheat 1,286,372 However, we always need to integrate bioinformatics 5 Brassica napus Oilseed rape 643,881 discoverywith experimental one for functional validation as Hordeum vulgare + subsp. computational based approach is always statistically biased and 6 Barley 501,838 vulgare suffers from machine and systemic errors. Nevertheless, there are 7 Glycine max Soybean 1,461,722 numerous challenges which need to overcome at computational level: 8 Pinus taeda Loblolly pine 328,662 9 Solanum lycopersicum Tomato 297,142 i) Need to improve the experimental protocol to sequence 10 Malus x domestica Apple tree 325,020 complex plant species in order to avoid creating erroneous reference genome. 11 Medicago truncatula Barrel medic 269,501 12 Solanum tuberosum Potato 249,761 ii) 12 Sorghum bicolor Sorghum 209,835 iii) EfficientSequencing storage data and need compression to be correctlyannotatedandbased algorithms 13 Nicotiana tabacum Tobacco 334,808 visualized better. Brassica rapa subsp.